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In episode 170 of The Content Strategy Experts podcast, Bill Swallow and Christine Cuellar dive into the world of content localization strategy. Learn about the obstacles organizations face from initial planning to implementation, when and how organizations should consider localization, localization trends, and more.

Localization is generally a key business driver. Are you positioning your products, services, what have you for one market, one language, and that’s all? Are you looking at diversifying that? Are you looking to expand into foreign markets? Are you looking to hit multilingual people in the same market? All of those factors. Ideally as a company, you’re looking at this from the beginning as part of your business strategy.

— Bill Swallow

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Transcript:

Disclaimer: This is a machine-generated transcript with edits.

Christine Cuellar: Welcome to the Content Strategy Experts Podcast, brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize, and distribute content in an efficient way. In this episode, we are talking about content localization strategy. So maybe you’re starting to think about introducing a localization strategy. Maybe you’re hitting some pain points in your localization processes, all that good stuff we’re going to be talking about today. Hi, I’m Christine Cuellar.

Bill Swallow: And I’m Bill Swallow.

CC: Bill, thanks for being here today to talk about localization. Bill is our go-to localization expert, and localization has been coming up a lot. So I noticed for me, on the marketing side of things, there’s been a lot of, you know, SEO stuff coming up for localization. People seem to be searching about it, asking questions at a more beginning to thinking about the whole localization process level. So that’s what we wanted to talk about today. Give you the chance to have some upfront knowledge about what you could be getting into with introducing localization in your content strategy. And yeah, let’s talk about it with an expert. So thanks, Bill.

BS: Thank you.

CC: First things first, the most basic question, what is content localization strategy? So what do we mean by that?

BS: Okay, so I can kind of frame this in, I guess the same point of view as a content strategy, but basically you’re taking a look at your entire localization process from start to finish. Plus you’re looking at what are the systems that are involved? How are authors prepping the content for localization? Are they writing well upfront? What does the publishing preparation look like? How are you choosing your translators? Are you going to pure machine translation? Are you using live people to do the translation? Are you using people who are content experts? Are you using people who are market experts? So there are a lot of different factors there that all kind of get balled up into this grander strategy of how are you going to approach getting your content authored and translated appropriately in other regional markets.

CC: Yeah, okay. That makes sense. And taking a step back even further, can you walk me through the difference between localization and localization strategy?

BS: Sure. Localization itself is kind of more of an action, and whereas strategy is more planning around that action, I think that’s the best way to put it. So localization involves a bunch of different things. It involves the act of internationalization. So that’s prepping your content, your code, your product, whatever it is to be delivered for multiple regional and language markets. And then you have the translation component of localization, which is actually getting things written, spoken however, in other languages. And the strategy piece is more bridging both of those and adding additional components so that you have a solid plan for every step in that process.

CC: Okay, yeah, that makes sense. And where do we step in? We here at Scriptorium, where do we sit?

BS: Generally we at Scriptorium, we sit on the source content authoring side. And we look at the overall content strategy, and we do look at a localization strategy as a component of that. They’re not separate. They’re very intertwined and we need to take a look at really both of them. So a lot of our clients do come to us because they have localization requirements.

And we have to account for those in the content strategy that we build for them. So we’re looking not only at the source content authoring process and what needs to happen in that to get the job done, but we also have to look at where are they going with their content, how are they going to localize it, what do they need to localize, what processes do they have in place now? Are they working? Are they not looking at systems? Are they adequate? Are they not? And look at the markets. Are they already reaching those markets? Do they need to do something different? How do we need to position the content as it moves through that funnel of production so that when it comes out the other side, it is ready for those markets. So they’re kind of intertwined there.

CC: Okay. Yeah. So when are organizations typically thinking about a content localization strategy?

BS: Well, localization generally it’s a key business driver. Are you positioning your content for one market, one language, and that’s all? Or are you positioning… I shouldn’t say just product because product services, what have you. Are you looking at diversifying that? Are you looking to expand into foreign markets? Are you looking to hit multilingual people in the same market? All of those factors. So ideally as a company, you’re looking at this from the beginning as part of your business strategy. And what are you doing to… What are you producing? Who are you producing it for? How do they need to consume it? So as soon as you catch a whiff of those multilingual requirements, bells should be going off saying, “Hey, we need a plan for this.” More commonly, an organization might be producing for one market or producing for several markets. They’re kind of doing things ad hoc, producing content, then sending it out to a translator. They’re getting something back, they may be polishing it up or it’s a finished product and then they send it out. It’s a very time-consuming process. It’s a very costly process, and it’s very difficult to kind of juggle when things will be done. Because if you don’t have a set process around things and you don’t have an idea of how long things will take, what efficiencies you’re able to build up front and so forth, you’re throwing caution to the wind and just putting stuff out there and hoping that it comes back in time so that you can go to market with it. We’ve worked with clients who have said that generally it takes about nine months or so to get their localized product out the door and into the market after the English is done. And for a lot of those, we’ve brought that number into three months, one month, depending on exactly what they’re producing and how they need to produce it, so-

CC: Yeah, it’s a huge difference.

BS: Looking at that… Oh, huge difference. And looking at that time to market, that’s perhaps more valuable than the cost that you’re dumping into putting a localization strategy or a content strategy together because you’re able to sell quicker into those markets. You’re not waiting for the opportunity to start seeing revenue come back from the initiatives that you’re taking to get stuff out there.

CC: Yeah. Yeah, that makes sense. And I feel like… So correct me if I’m wrong here, but in the global world that we live in, it feels like localizing products and getting them ready for new regions is a very… I think that would be something that executives think about from the get-go like, yes, of course we want our product ready for new regions and locations. But why is the… It sounds like maybe the content piece of that is not thought about or maybe left behind until it’s an absolute emergency. Would you say that that’s… First of all, is that accurate?

BS: Sadly, I’d say yes.

CC: Okay.

BS: Content is often an afterthought in general, whether we’re talking about producing stuff just in your native language for a native market. Localization is usually even more of an afterthought because it’s like, oh, well, we wrote it in English, we’ll just have someone translate it. And by then you’re waiting until that product is done and then sending it to somebody else who’s looking at it going, “I can’t make sense of this. It’s not written well. And I’m going to take my best guess at how to translate this.” It could take months to get that back.

CC: So maybe organizations see the value in having their products and services available in other markets, but they don’t necessarily think of all of the content localization pieces that are involved in getting that out the door.

BS: No, and it’s similar for pretty much anyone trying to get anything done that you want to do something. But for example, I really want to put a new patio in the back of my house. I know exactly… I even have an idea of exactly how that should go in. I don’t have the time. I don’t have the materials needed to do it. And I’d much rely on somebody else who knows what they’re doing to put it in the correct way so it’s not graded improperly, so that there aren’t uneven portions that people will trip over and so forth. So looking at it that way, the same thing with localization. People who are running a company or starting a company, they may have an idea that yes, they need to get from point A to point B to point C to point D. They don’t know those steps along that path, and they need some help figuring out, okay, it’s not that you just write your English content, you throw it over to somebody else and they send it back. It’s a more intricate process. You have some systems in place that we’ll manage that handoff that will allow people to gate the content and proof it and make sure it’s correct before it goes anywhere. And you may have some other efficiencies built in that allow you to automatically format things when the time comes to actually produce. So there are a lot of bits and pieces that people just generally don’t think about because it’s not in their wheelhouse.

CC: Yeah, they can’t know what they don’t know.

BS: Exactly.

CC: Okay. So it sounds like most organizations realize that this is a problem once they’re actually trying to get their product out the door and into a new market, into a new region. What are some obstacles to getting a content localization strategy set up? I’m sure that one issue is probably like, oh, you’re in emergency mode and we just need to get this product out the door. That might present a challenge in and of itself.

BS: Absolutely.

CC: Yeah. Are there other obstacles as well to getting a more future-focused strategy in place?

BS: Oh, that one is a good one. That is the first hurdle to get over.

CC: Is the emergency mode.

BS: So being able to recognize or realize that you’re in emergency mode and getting out of that mindset and saying, okay, it’s not just that this will be a forever problem of just waiting and hoping for good quality coming out in the end. Once you’re able to realize that you need to break that mindset and start looking forward, then we start hitting other obstacles. One of them is going to be funding because there will be systems involved, there will be personnel required, there will be processes that need to change and so forth. And that will certainly cost a lot upfront. You’re going to basically see that return on investment in a pretty quick amount of time. We’ve seen one company make their investment back within a year, but they were producing an insane amount of languages already, and they just needed to tidy up their process. And again, by bringing that window in from nine months to about a month and a half or so, to be able to get their localized stuff out, they were able to quickly realize that return on investment there. But another one is buy-in, because you have a lot of people who are busy doing their job and you’re suddenly telling them that they need to change how they do their job, and it might be abandoning the tools that they like to use. Writing in a different way, looking at publishing in a different way and interacting with people who they normally don’t interact with on a day-to-day basis. So your source author’s interacting with a localization manager internally who needs to send stuff out to translators or your writer’s interacting with translators to explain what they had written so that the translator has a definitive idea of what it is and how to translate it for the market that they’re translating for. And then of course, you have the obstacle of governance and change management comes along with that. You need to be able to make sure that any of the changes that you introduce, that people are following the new way of doing things and aren’t falling back to old bad habits or even old good habits at the time. And you need to make sure that you have these gating processes so that once something is written in English, you have a formal review on that to make sure it’s correct, to make sure it’s written appropriately. That goes out to translation. They have their own gating process of making sure they receive all the files, that they understand the content that they have, all the supporting information that they need to help them translate and localize this information for that market. Then of course, they do their own quality checks. It comes back, you make sure that there’s a final review on the company side to make sure the translation seems good. And then you’re able to publish and deliver. So it still sounds like a lot of gating factors, but once you kind of get things going and figuring out where you can expedite and make things a lot easier, you start to bring in that entire timeline.

CC: Yeah, that makes sense. You mentioned buy-in, and so I could see how if people feel like their workload’s being increased by suddenly needing to talk to more people, coordinate between more departments or even just have more things on their radar, I could see how that could create a lot of, oh, I don’t know if I want to go in this direction. What are some ways… And that’s probably one of the… As you mentioned, that’s just one of a few buy-in challenges. What are some of the ways that you maybe win people over or show people how this can benefit their work life versus just make it harder?

BS: That’s a good question. I think that authors in general to understand where their content is going and who is consuming it. And even though it’s… We’re talking about corporate content, we’re talking about everything from website content to product manuals to troubleshooting tips and all that stuff and training materials. So it’s not really… Even though it belongs to the company, a lot of authors tend to have a kind of, I guess, personal pride built around what they write.

CC: Yeah, okay. Yeah, that makes sense.

BS: So knowing who is consuming it down the road and the reason why you have these additional checkpoints and processes in place will kind of help, I think get a lot of them around the idea of, yeah, this is a good thing and I’m looking forward to helping any way I can. Because the last thing they want is to have something written completely correctly in English and have it go out to, I guess let’s say a market in Denmark. And the content was translated incorrectly because the translator maybe didn’t understand what something meant, and they gave it a different term, which had a different meaning in that market.

CC: Yeah. And I could also see from a safety standpoint, that could be really dangerous too, if you’re not properly translating instructions for high stakes content, medical devices, stuff like that. Just like you do in English, you want that content to be accurate and understandable. Because if it’s not accurate, of course it’s wrong and people could get hurt also if people don’t understand it, even if it’s totally accurate. But it’s just hard to understand. That presents, I’m sure, a lot of dangerous situations where your people could get hurt and your company is liable. So yeah, it makes sense that you would really want to have a good process in place.

BS: Oh, absolutely. And even more along those lines, the regulations that we have to adhere to here in the US are somewhat different to… Very different to anywhere else in the world. There are different directives in place depending on where you are regionally, things that have to be included that have to be said a very specific way. So I guess the easiest way to look at it is that there are more legal ramifications in the US. So you could get sued if something is wrong, whereas opposed to if you go over to the UK, it’s generally more that there’s a directive you have to follow and you simply cannot release in that market if your, for example, machinery content does not meet that specific directive’s requirements. So there’s a slightly different approach. So it might be… There’s still a legal ramification if things go wrong, but there’s also another set of requirements that need to be met before you even start worrying about the legal stuff.

CC: And are most organizations aware of those kind of requirements when they start trying to get into a new market?

BS: Some of them might be, but again, if you’re in one particular region, chances are that’s the region you’ve grown up with and that’s the region you understand. And there’s been very little attention paid to what are their requirements in other geographic regions, other countries and so forth. So I can’t say is it common, is it not common? But in general, you know what? And when you’re looking to move to a foreign market, there’s the foreign context. You’re going to have very little insight into what that foreign market demands by its very nature. As a company moves into a new language market, new geographic market, they’re going to learn things as they go, and they’re going to bring that knowledge back and refine how things are being done currently so that it also satisfies that new requirement. And it’s going to be an iterative process until they really get their arms around it. And again, going back to a localization strategy for your content, you can kind of start putting those feelers out. Because if one market has one set of requirements, it’s like, wait a minute, now we want to go to three. What are the requirements for the other two before we even start thinking in that direction? So you’re able to start building upon that strategy that you’re developing. I mean, we’re not experts in all the requirements for every single market on the face of the earth. I can say that outright, but we can help companies start to identify what they need to start looking into before they start running.

CC: So since we mentioned one of the reasons this topic came about was seeing some SEO search trends, people trying to get more information on localization. What other trends are you seeing in localization right now?

BS: I think the big one is still going to be machine translation. It’s continually evolving and it’s getting smarter, still not, I would say, better than a human. It’s certainly quicker, but we’re getting there. And a lot of that… We talk about AI a lot. And obligatory nod to AI for this podcast, but when we talk about AI, and I think I mentioned this on another podcast already, that when you look at machine translation, that was really like AI Alpha or AI Beta where it was already using an algorithm to start putting together translations for written text. So with AI in the mix now, we’re getting a lot more, I guess, interesting results, a lot more targeted results with machine translation. I still don’t think it’s a perfect solution, and we’ll certainly need some proofreading, but it’s come a long way. And I think that that trend is certainly not going to fall off the radar anytime soon. In fact, recently Sarah O’Keefe had a podcast with Sebastian Göttel about strategies for AI and technical documentation, and they actually recorded that podcast in German. And they used AI to translate and voice augment into English. So not only were things machine translated from German into English, but the German speaking was then synthetically reproduced in English, which just is really cool.

CC: Yeah, it’s super cool to listen to, and we’ll link those in the show notes as well. There’s two versions, the German version and the English version. But yeah, you’re right. It was a super cool process, but you had mentioned earlier there was a human piece to it that was still needed because when it was originally recorded in German, then we got the German transcript, translated that into English. And when we translated that, at first it was Google Translate just to get it all done, but then Sarah needed to go and check it because she speaks both English and German. And we needed that human element to make sure that the translation was correct. Because like you were saying, you can’t just necessarily put it into a machine and cool, yay, it’s done. We need the human to make sure that it was actually translated properly and the things make sense. And we did notice once Sebastian’s synthetic audio was created in English, a lot of the prompts or the questions just were different lengths. The English version sometimes was shorter or sometimes longer of just the exact same question. It’s just the languages are different. So it’s really cool. It was a really cool experiment and does open up some interesting possibilities, would you say, for localization. And we’ve never been able to have a German and English podcast before, so that’s kind of cool.

BS: Yeah, no, it was very cool. I sat in the back of the room just watching the entire process, but it was definitely something I was quite interested in seeing. Yeah, there was a lot of editing of the English translation because again, it was pure machine translation and it needed some help. But once that was done, the synthetic audio really came right together, and I was impressed in how that happened.

CC: Yeah. And it’s so interesting because it’s definitely… It sounds like Sebastian, but then also it sounds not quite human, but it’s really close. It’s really interesting. But it did-

BS: Very uncanny valley.

CC: Yeah, it was, and I only speak English. I don’t speak German, so it made that podcast accessible to me. I was able to listen to it, and it does present some interesting opportunities, but as always with AI, the human element was definitely needed. It was very important to make sure that the humans at the other end of the screen could eventually consume it.

BS: Oh, yeah.

CC: Awesome. Well, bill, thank you so much. We covered a lot of ground today, and we really appreciate it. This was really helpful, and yeah, thanks for being on the show.

BS: Yeah, thanks.

CC: And thank you for listening to the Content Strategy Experts podcast, brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes for relevant links.

The post Accelerate global growth with a content localization strategy appeared first on Scriptorium.

In episode 169 of The Content Strategy Experts podcast, Sarah O’Keefe and special guest Sebastian Göttel of Quanos engage in a captivating conversation on generative AI and its impact on technical documentation. To bring these concepts to life, this English version of the podcast was created with the support of AI transcription and translation tools!

Sarah O’Keefe: So what does AI have to do with poems?

Sebastian Göttel: You often have the impression that AI creates knowledge; that is, creates information out of nothing. And the question is, is that really the case? I think it is quite normal for German scholars to not only look at the text at hand, but also to read between the lines and allow the cultural subtext to flow. From the perspective of scholars of German literature, generative AI actually only interprets or reconstructs information that already exists. Maybe it’s hidden, only implicitly hinted at. But this then becomes visible through the AI.

How this podcast was produced:

This podcast was originally recorded in German by Sarah and Sebastian, then Sarah edited the audio. Sebastian used Whisper, Open AI’s speech-to-text tool to transcribe the German recording, followed by necessary revisions. The revised German transcript was machine translated into English via Google Translate and then we cleaned up the English transcription.

Sebastian used ElevenLabs to generate a synthetic audio track from the English transcript. Sarah re-recorded her responses in English and then we combined the two recordings to produce the composite English podcast.

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Transcript:

Disclaimer: This is a machine-generated transcript with edits.

Sarah O’Keefe: Today’s episode is available in English and German. Since our guest works with AI in German-speaking countries, we had the idea to create this podcast in German. The English version was then put together with AI support, particularly synthetic audio. So welcome to the Content Strategy Experts Podcast, today offered for the first time in German and English. Our topic today is Information compression instead of knowledge creation: Strategies for AI in technical documentation. In the German version, we tried to put it all together in one nice long word, but it didn’t quite work. Welcome to the Content Strategy Experts Podcast, brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize, and distribute content in an efficient way. In this episode, we talk about best practices for AI and tech comm with our guest Sebastian Göttel of Quanos. Hello everyone, my name is Sarah O’Keefe. I am the CEO here at Scriptorium. My guest is Sebastian Göttel. Sebastian Göttel has been working in the area of ​​XML and editorial CCMS systems in technical documentation for over 25 years. He originally studied computer science with a focus on AI. Currently, he is Product Manager for Schema ST4 at Quanos, one of the most used editorial systems in machinery and industrial engineering in the German-speaking regions. He is also active in Tekom and, among other things, contributed to version 1 of the iiRDS standard. Sebastian lives with his wife and daughter, three cats, and two mice just outside Nuremberg. Sebastian, welcome. I look forward to our discussion. In English, we say create once, publish everywhere. This is about recording once and outputting multiple times. So, off we go. Sebastian, our topic today is, as I said, information consolidation instead of knowledge creation and how this strategy could be used for AI in technical documentation. So please, explain.

Sebastian Göttel: Yes, first of all thank you for inviting me to the podcast. It’s not that easy to impress a 14-year-old daughter. And I thought, with this podcast I have a chance. So I told her that I would be talking about AI on an American podcast soon. And the reaction was a little different than I expected. Youuuuu will you speak English? You can put quite a lot of meaning into a single “uuuu” like that. And that’s why I’m glad that I can speak German here. But, and this is now the transition to the topic, what will the AI ​​make of the “You will speak English”? How does it want to pronounce that correctly in text-to-speech or translate it into another language? And that’s what I think our conversation will be about today. If we want to understand how AI understands us, but also how we can use it in technical documentation, then we have to talk about information compression, but also invisible information. “You will speak English?” Can the AI conceptualize that my daughter doesn’t trust me to do this or simply finds my German accent in English gross? Well, if the AI ​​can understand that, then it is new information or actually information that was already there and that both father and daughter were actually aware of during the conversation. I find it quite exciting that German scholars have often dealt with this. Namely, what is in such a text, and what is meant in the text? What’s between the lines? And when you think back to your school days, these interpretations of poems immediately come to mind.

SO: So poems. And what does AI have to do with poems?

SG: Yes, well, you often have the impression that AI creates knowledge; that is, creates information out of nothing. And the question is, is that really the case? I think it is quite normal for German scholars to not only look at the text at hand, but also to read between the lines and allow the cultural subtext to flow. And from the perspective of scholars of German literature, generative AI actually only interprets or reconstructs information that already exists. Maybe it’s hidden, only implicitly hinted at. But this then becomes visible through the AI. Wow, I never thought I would refer to German literature scholarship in a technical podcast.

SO: Yes, and me neither. But the question remains, how does AI work and why does it work? And then why do these problems exist? What is our understanding of the situation today?

SG: Well, I think we’re still pretty impressed by generative AI, and we’re still trying to understand what we’re actually perceiving and what’s happening there. There are things that just make our jaws drop. And then there are those epic fails again, like this recent representation of World War II German soldiers by Gemini, Google’s generative AI. According to our current understanding, the soldiers were politically correct. And there were, among other things, Asian-looking women with steel helmets. I always like to compare this with the beginnings of navigation systems. There were always these anecdotes in the newspaper about someone driving into the river because their navigation system mistook the ferry line for a bridge. It was relatively easy to fix such an error in the navigation system. It was clear why the navigation system made the mistake. Unfortunately, with generative AI it’s not that easy. We don’t know, actually, we haven’t even really understood how these partially intelligent achievements come about. But the epic fails make us aware that it’s not an algorithm, but a phenomenon that seems to emerge if you pack many billions of text fragments into a matrix.

SO: And what do you mean here by “emerge”?

SG: That is a term from natural science. I once compared it to water molecules. A single water molecule isn’t particularly spectacular, but if, for example, you’re sailing in a storm on the Atlantic or hitting an iceberg, you get a different perspective. Because if you put many water molecules together, completely new behavior emerges. And it took physics and chemistry many centuries to partially unravel this. And I think we will, maybe not for quite as long, but we will have to do a lot more research into generative AI in order to understand a little more about what exactly is happening. And I think the epic fails should make us aware that we would currently do well not to blindly place our fate in the hands of a Large Language Model. I think the human-in-the-loop approach, where the AI ​​makes a suggestion and then a human looks at it again, remains the best mode for the time being. The translation industry, which feels like it is a few years ahead of the world when it comes to generative AI or neural networks, has recognized this quite cleverly and implemented it profitably.

SO: And if translation is the model, what does this mean for generative AI and technical documentation?

SG: That’s a good question. Let’s take a step back. So at the beginning of my working life, there was a revolution in technical documentation, these were structured documents; SGML and XML. This has been known for several decades now, and it is still not used in every editorial team. And that means we now have these structured documents and the other thing, which are the nasty unstructured documents. I always thought that was a bit of a misnomer because unstructured documents are actually structured. Well, at least most of the time. There’s a macro level where I have a table of contents, a title page, and an index. There are chapters. Then there are paragraphs, lists, and tables and that goes down to the sentence level. I have lists, prompts, and so on. It’s not for nothing that some linguists call this text structure. And if I now approach XML, the beauty of XML is that I can now suddenly make this implicit structure explicit. And the computer can then calculate with our texts. Because if we’re being honest, in the end, XML is not for us, but for the machine.

SO: Is it possible then that AI ​​can discover structures that, for us humans, have so far only been expressed through XML?

SG: Yes. Well, I recently looked into Invisible XML. There you can overlay patterns onto unstructured text and they become visible as XML. Very clever. I think generative AI is a kind of Invisible XML on steroids. The rules aren’t as strict as in Invisible XML, but genAI also understands linguistic nuances. I found it very exciting, a customer of ours fed unstructured PDF content into ChatGPT; that is unstructured content from the PDF, in order to then convert it to XML. The AI ​​was surprisingly good at discovering the invisible structure that was hidden in the content and converted XML really well. So that was impressive. When AI now appears to create information out of nothing, I think it is more likely that it makes existing but hidden information visible.

SO: Yes, I think the problem is that this hidden structure, in some documents, it’s there, but in others, there’s what we call “crap on a page” in English. So that’s, there’s no structure. And from one document to another, there is no consistency, so they are completely different. Writer 1 and Writer 2, they write and they never talk. And so if the AI ​​now creates an entire chapter and an outline from a few keywords, how does it work? How does that fit together?

SG: Yes, you’re right. So far we’ve been talking about we take PDF and then XML is added to it. But if I’m put on the spot, I’ll throw in a few keywords and ChatGPT suddenly writes something. But also, I think this idea also applies that this is actually hidden information. It might sound a bit daring at first, but there’s nothing new, nothing completely surprising. Now if I just ask, let’s say ChatGPT, give me an outline for documentation for a piece of machinery. And then something comes out. I think most of our listeners would say the same thing. This is nothing new. This is hidden information contained in the training data, which is easily made visible through the query. Because ultimately, generative AI creates this information from my query and this huge amount of training data. And the answer is chosen so that it fits my query and the training data well. It creates a synthetic layer over the top. And in the end, the result is not net new information, but hopefully, the necessary information delivered in a way that’s easier to process further. Either like the example with PDF, enriched with XML or I maybe now have an outline. And I imagine it’s a bit like a juicer. The juicer doesn’t invent juice, it just extracts it from the oranges.

SO: Making information easier to process sounds almost like a job description for technical writers. And what about other methods? So if we now have metadata or knowledge graphs, what does that look like?

SG: That’s right, in addition to XML, these are also really important. So metadata, knowledge graphs. I find that metadata condenses information into a few data points and the knowledge graphs then create the relationships among these data points. And this is precisely why knowledge graphs, but also metadata, make invisible information visible. Because the connections that were previously implicit can now be understood through the knowledge graphs. And that can be easily combined with generative AI. At the beginning, the knowledge graph experts were a bit nervous, as you could tell at conferences, but now they’re actually pretty happy that they’ve discovered that generative AI plus knowledge graphs is much better than generative AI without knowledge graphs. And of course, that’s great. By the way, this isn’t the only trick where we have something in the technical documentation that helps generative AI get going. If you want to make large knowledge bases searchable with Large Language Models, you can do that today with RAG, or Retrieval Augmented Generation. And this means you can combine your own documents with a pre-trained model like ChatGPT very cost-effectively. If you now combine RAG with a faceted search, as we usually have in the content delivery portals in technical documentation, then the results are much better than with the usual vector search, because in the end it is just a better full-text search. That’s another possibility where structured information that we have can help jump-start AI.

SO: Is it your opinion that structured information will not become obsolete through AI, but will actually become more important?

SG: My impression is that the belief has taken hold that structured information is better for AI. I think we’re all a bit biased, naturally. We have to believe that. These are the fruits of our labor. It’s a bit like apples. The apple from an organic farmer is obviously healthier than the conventional apple from the supermarket. I think this is scientific fact. But in the end, any apple is better than a pack of gummy bears. And that’s what can be so disruptive about AI for us. Because at the end of the day, we are providing information. And if users gets information that is sufficient, that is good enough, why should they go the extra mile to get even better information? I don’t know.

SO: Okay, so I’m really interested in this gummy bear career and I want to hear a little bit more about that. But why is your view on the tech comm team’s role so, let’s say, pessimistic?

SG: I think my focus has gotten a little wider recently. I think I’m not really just looking at technical documentation. When it comes to technical documentation, we are lost without structured data. It will not work. But if we take the bigger picture, at Quanos we not only have an CCMS, but we also create a digital twin for information. I’m in all these working groups as the guy from the tech doc area. And I always have to accept that our particularly well-structured information from tech doc, the one with extra vitamins and secondary nutrients, is actually the exception out there when we look at the data silos that we want to combine in the info twin. When I was young, I believed that we had to convince others to work the way we do in tech docs. That would have been really fantastic. But if we’re honest with ourselves, it just doesn’t work. The advantages that XML provides for technical documentation are too small in the other areas and for individuals to justify a switch. The exceptions prove the rule. As a result, tons of information is out there locked up in these unstructured formats. And it can only be made accessible with AI. That will be the key.

SO: And how do we do that? If XML isn’t the right strategy, what does that look like?

SG: Well, so let’s take an example. So many of our customers build machinery and let’s take a look at the documentation that they supply. There are several dozen PDFs for each order. And of course the editor has a checklist and knows what to look for in this pile of PDFs. The test certificate, the maintenance table, parts lists, and so on. And even though the PDFs are completely “unstructured” as compared to XML files, we humans are able to extract the necessary information. And the exciting thing about it is that anyone can actually do it. So you don’t have to be a specialist in bottling systems or industrial pumps or sorting machines. If you have an idea of ​​what a test certificate, a maintenance table, a parts list is, then you can find it. And here’s the kicker: the AI ​​can do that too.

SO: Ahh. And so in this case are you more concerned with metadata…or something else?

SG: No, you’re right. So this is in fact about metadata and links. I find it fascinating what this does to our language usage. Because we have gotten used to saying that we enrich the content with metadata. But in many cases we have simply made the invisible structure explicit. No information was added. Nothing has become richer, just clearer. But now imagine that your supplier didn’t provide a maintenance table. Then you need to start reading, understand the maintenance instructions, and extract the necessary information. And that’s tedious. Even here, AI ​​can still provide support. But how well depends on the clarity of maintenance procedures. The more specific background knowledge is necessary, the more difficult it becomes for the AI to provide assistance.

SO: What does that look like? Do you have an example or use case where AI doesn’t help at all?

SG: It depends on contextual knowledge. I once received parts of a risk analysis from a customer. And her question was, “Can you use AI to create safety messages?” And I said, “Sure, look at the risk analysis and then look at what the technical writers made of it.” And they were exemplary safety messages. But there was so little content in the risk analysis that with the best intentions in the world you couldn’t do anything with artificial intelligence; that end result was only possible because the technical writers had an incredibly good understanding of the product and also had the industry standards. The information was not hidden in this input, but in the contextual knowledge. And that’s so specialized that it’s of course not available in the Large Language Model.

SO: In this use case, you don’t see any possibility for AI at all?

SG: Well, at least not for a generic Large Language Model. So something like ChatGPT or Claude, they have no chance. There is an opportunity in AI to specialize these models again. You can fine-tune this with context-specific content. But we don’t yet know at the moment whether we normally have enough content. There are some initial experiments. But let’s think back to the water molecules. We need quite a few of them to make an iceberg or even a snowman. Ultimately, you have to ask which supporting materials are needed from which point of view, and fine-tuning is really expensive. So there are costs. It takes a long time. Performance is also an issue. And how practical is this approach? Do we have training data? So, given all these aspects, it is still unclear what the gold standard is for making a generic large language model usable for content work in very specific contexts. We just don’t know today.

SO: Can you already see or predict how generative AI will change or must change technical documentation?

SG: I really think it’s more like looking into my crystal ball. So it’s not that easy to estimate which use cases are promising for the use of AI in technical documentation. As a rule, you have a task where a textual input needs to be transformed into a textual output according to a certain standard. And it used to be garbage in, garbage out. In my opinion, the Large Language Models change this equation permanently. Input that we were previously unable to process automatically due to a lack of information density, we can now enrich it with universal contextual knowledge in such a way that it becomes processable. Missing information cannot be added. We’ve discussed that now. But these unspoken assumptions, in fact, we can pack them in. And that helps us in many places in technical documentation, because one of the ways good technical documentation differs from bad documentation is that fewer assumptions are necessary in order to understand the text or if you want to process it automatically. And that’s why I find condensing information instead of creating knowledge to be a kind of Occam’s Razor. I look at the assignment. If it’s simply a matter of making hidden information visible or putting it into a different form, then this is a good candidate for generative AI. What if it’s more about refining the information by using other sources of information? Then it becomes more difficult. If I now have this information, this other information in a knowledge graph, if it is already broken down there, then I can explicitly enrich the information before handing it over to the Large Language Model. And then it works again. But if the information, for example, the inherent product knowledge, is in the editor’s head, as was the case with my client’s risk analysis, then the Large Language Model simply has no chance. It won’t generate any added value. Then you may have to rethink your approach. Can you divide the task somehow? Maybe there is a part where this knowledge is not necessary, and I have an upstream or downstream process where I can optimize something with AI. And I think that’s the mother lode of opportunities lies. This art of distinguishing what is possible from what is impossible, and this will be more of a kind of engineering art, will be the factor in the coming years that will decide whether generative AI is of use to me or not.

SO: And what do you think? Of use, or not of use?

SG: I think we’ll figure it out. But it will take much longer than we think.

SO: Yes, I think that’s true. And so thank you very much, Sebastian. These are really very interesting perspectives and I’m looking forward to our next discussion, when in two weeks or three months there will be something completely new in AI and we’ll have to talk about it again, yes, what can we do today or what new things are available? So thank you very much and see you soon! 

SG: … soon somewhere on this planet.

SO: Somewhere.

SG: Thank you for the invitation. Take care, Sarah.

SO: Yes, thank you, and many thanks to those listening, especially for the first time in the German-speaking areas. Further information about how we produced this podcast is available at scriptorium.com. Thank you for listening to the Content Strategy Experts Podcast, brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes for relevant links. 

The post Strategies for AI in technical documentation (podcast, English version) appeared first on Scriptorium.

Folge 169 ist auf Englisch und Deutsch verfügbar. Da unser Gast Sebastian Göttel sich im deutschsprachigen Raum mit KI beschäftigt, kam die Idee, diesen Podcast auf Deutsch zu erstellen. Die englische Version wurde dann mit KI-Unterstützung zusammengebastelt.

Sarah O’Keefe: Was hat die generative KI mit Gedichtinterpretationen zu tun?

Sebastian Göttel: Ja, nun, also oft hat man da ja den Eindruck, dass KI das Wissen schöpft, also Informationen aus dem Nichts erschafft. Und da ist die Frage, ist das denn wirklich so? Denn für die Germanisten ist es, glaube ich, schon eher normal, nicht nur den vorliegenden Text anzuschauen, sondern auch zwischen den Zeilen zu lesen, den kulturellen Subtext einfließen zu lassen. Und aus dem Blickwinkel der Germanisten, interpretiert oder rekonstruiert generative KI eigentlich nur Informationen, die schon vorhanden ist. Möglicherweise ist die verborgen, nur implizit angedeutet. Aber die wird durch die KI dann sichtbar.

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Sarah O’Keefe: Die heutige Episode ist auf Englisch und Deutsch verfügbar. Da unser Gast sich im deutschsprachigen Raum mit KI beschäftigt, kam die Idee, diesen Podcast auf Deutsch zu erstellen. Die englische Version wurde dann mit KI-Unterstützung zusammengebastelt. Also herzlich willkommen zum Content Strategy Experts Podcast, heute zum ersten Mal auf Deutsch. Unser Thema ist heute Informationsverdichtung statt Wissensschöpfung. Strategien für KI in der technischen Dokumentation. Wir haben versucht, das alles in ein Wort zusammenzubringen, das hat aber nicht ganz geklappt. Welcome to the Content Strategy Experts Podcast, brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize and distribute content in an efficient way. In this episode, we talk about best practices for AI and TechCom with our guest Sebastian Göttel of Quanos. Hallo, ich heiße Sarah O’Keefe. Ich bin hier bei Scriptorium die Geschäftsführerin. Mein Gast ist Sebastian Göttel. Sebastian Göttel arbeitet seit über 25 Jahren im Bereich XML und Redaktionssysteme in der technischen Dokumentation. Ursprünglich hat er mal Informatik mit Schwerpunkt KI studiert. Aktuell ist er bei Quanos Product Manager für Schema ST4, einem der meistgenutzten Redaktionssysteme im Maschinen- und Anlagenbau in DACH. Er ist auch in der Tekom aktiv und hat unter anderem an der Version 1 des iiRDS-Standards mitgewirkt. Sebastian lebt mit Frau und Tochter, drei Katzen und zwei Mäusen vor den Toren von Nürnberg. Sebastian, herzlich willkommen. Ich freue mich auf diesen Austausch. Auf Englisch sagen wir ja create once, publish everywhere. Hier geht es um einmal aufnehmen und mehrfach ausgeben. Also, los geht’s. Sebastian, unser Thema ist heute, wie gesagt, die Informationsverdichtung anstatt von Wissensschöpfung. Und wie diese Strategie für KI in der technischen Dokumentation eingesetzt werden könnte. Also, bitte, erklär doch mal.

Sebastian Göttel: Ja, erstmal vielen Dank für die Einladung in den Podcast. Es ist ja gar nicht so einfach, eine 14-jährige Tochter zu beeindrucken. Und ich dachte mir, mit diesem Podcast habe ich eine Chance. Also habe ich ihr erzählt, dass ich demnächst in einem amerikanischen Podcast über KI sprechen werde. Und die Reaktion war ein bisschen anders, als ich mir das erwartet habe. Duuu wirst da Englisch sprechen? Man kann schon ziemlich viel Bedeutung in so ein einzelnes Duuu legen. Und von daher bin ich zum einen froh, dass ich hier Deutsch sprechen darf. Aber, und das ist jetzt die Überleitung zum Thema, was wird die KI aus dem “Duuu wirst da Englisch sprechen” machen? Wie will sie das beim Text-to-Speech korrekt aussprechen oder in eine andere Sprache übertragen? Und darum, glaube ich, wird es in unserem Gespräch heute gehen. Wenn wir verstehen wollen, wie KI uns versteht, aber auch wie wir sie in der technischen Dokumentation einsetzen können, dann müssen wir über Informationsverdichtung, aber auch unsichtbare Informationen sprechen. Duuu wirst da Englisch sprechen. Kann die KI rekonstruieren, dass meine Tochter mir das nicht zutraut beziehungsweise meinen deutschen Akzent im Englischen einfach grottig findet? Naja, also wenn die KI das rekonstruieren kann, ist es dann neue Information oder eigentlich eher Information, die schon da, war und die eigentlich im Gespräch sowohl Vater als auch Tochter bewusst war. Ich finde das ziemlich spannend, dass die Germanisten sich damit schon ganz häufig beschäftigt haben. Nämlich, was steht in so einem Text drin und was ist in dem Text gemeint? Was steht zwischen den Zeilen? Und wenn man so an seine Schulzeit zurückdenkt, dann fallen einem ja sofort diese Gedichtinterpretationen ein.

SO: Also Gedichte und was hat die generative KI mit Gedichtinterpretationen zu tun?

SG: Ja, nun, also oft hat man da ja den Eindruck, dass KI das Wissen schöpft, also Informationen aus dem Nichts erschafft. Und da ist die Frage, ist das denn wirklich so?

Denn für die Germanisten ist es, glaube ich, schon eher normal, nicht nur den vorliegenden Text anzuschauen, sondern auch zwischen den Zeilen zu lesen, den kulturellen Subtext einfließen zu lassen. Und aus dem Blickwinkel der Germanisten, interpretiert oder rekonstruiert generative KI eigentlich nur Informationen, die schon vorhanden ist. Möglicherweise ist die verborgen, nur implizit angedeutet. Aber die wird durch die KI dann sichtbar. Ui, hätte nie gedacht, dass ich mal in einem technischen Podcast mich auf Germanisten berufe.

SO: Ja, und ich auch nicht. Da bleibt aber doch die Frage, wie funktioniert das? Also wie funktioniert die KI und warum funktioniert das? Und wieso gibt es dann diese Probleme? Was ist denn heute unser Verständnis von der Lage?

SG: Also ich glaube, wir sind immer noch ziemlich beeindruckt von der generativen KI und wir versuchen noch zu begreifen, was wir da überhaupt wahrnehmen, was da passiert. Da gibt es Dinge, die lassen uns einfach den Kiefer runterklappen. Und dann gibt es wieder diese Epic Fails, wie vor kurzem diese Darstellung von Wehrmachtsoldaten von Gemini, der generativen KI von Google. Die Soldaten waren nämlich nach unserer heutigen Vorstellung politisch korrekt. Und da gab es dann unter anderem asiatisch aussehende Frauen mit Stahlhelm. Ich vergleiche das immer so ganz gern mit den Anfängen der Navigationssysteme. Da gab es ja auch immer diese Anekdoten in der Zeitung, dass wieder jemand in den Fluss gefahren ist, weil sein Navi die Fährlinie für eine Brücke gehalten hat. So einen Fehler konnte man im Navigationssystem relativ einfach fixen. Da war klar, warum das Navi den Fehler gemacht hat. Bei der generativen KI ist das leider nicht ganz so einfach. Wir wissen nicht, eigentlich, wir haben es noch nicht mal wirklich verstanden, wie diese teilweise intelligenten Leistungen zustande kommen.Die Epic Fails, die machen uns aber bewusst, dass es sich nicht um einen Algorithmus handelt, sondern um ein Phänomen, das scheinbar emergiert, wenn man viele Milliarden Texte in eine Matrix packt.

SO: Und was meinst du da mit emergiert? Was ist das denn?

SG: Das ist ein Begriff aus der Naturwissenschaft. Ich habe das mal mit Wassermolekülen verglichen. Ein einzelnes Wassermolekül ist nicht sonderlich spektakulär, aber wenn du zum Beispiel im Segelboot in einem Sturm auf dem Atlantik unterwegs bist oder auf einen Eisberg aufläufst, dann kriegst du eine andere Perspektive. Denn viele Wassermoleküle zusammengenommen zeigen ganz neues Verhalten. Und das nennt man Emersion. Und Physik und Chemie haben viele Jahrhunderte gebraucht, um das halbwegs zu enträtseln. Und ich denke, wir werden, vielleicht nicht ganz so lange, aber wir werden noch ein gutes Stück weiterforschen müssen bei der generativen KI, um auch da ein bisschen mehr zu verstehen, was da jetzt genau passiert. Und ich finde, die Epic Fails, die sollten uns bewusst machen, dass wir aktuell gut daran tun, unser Schicksal nicht blind in die Hände eines Large Language Models zu legen. Ich finde, der Ansatz Human in the Loop, wo die KI einen Vorschlag macht und dann ein Mensch nochmal drüber schaut, das bleibt bis auf weiteres der beste Modus. Und die Übersetzerbranche, die gefühlt der ganzen Welt ein paar Jahre voraus ist, wenn es um generative KI geht oder um neuronale Netze, die hat das ziemlich klug erkannt und gewinnbringend umgesetzt.

SO: Und wenn also jetzt die Übersetzung das Muster ist, was heißt das dann für generative KI und die technische Doku?

SG: Das ist eine gute Frage. Lass uns mal einen Schritt zurück machen. Also am Anfang meines Arbeitslebens, da war die Revolution in der technischen Dokumentation, das waren diese strukturierten Dokumente. SGML und XML. Und das kennt man jetzt also mittlerweile schon seit mehreren Jahrzehnten und es ist ja immer noch nicht in jeder Redaktion gebräuchlich. Und das heißt, wir haben jetzt diese strukturierten Dokumente und das andere, das sind die bösen unstrukturierten Dokumente. Und ich fand das schon immer so ein kleines bisschen einen Etikettenschwindel, denn unstrukturierte Dokumente sind ja in Wirklichkeit auch strukturiert. Also meistens zumindest. Da gibt es so eine Makro-Ebene, da habe ich ein Inhaltsverzeichnis, ein Titelblatt, ein Stichwortverzeichnis. Es gibt Kapitel. Dann gibt es Absätze, Listen und Tabellen und das geht dann runter bis auf die Satzebene. Da habe ich Aufzählungen, Aufforderungen und so weiter. Und nicht umsonst nennen das manche Linguisten ja Textstruktur. Und wenn ich jetzt mit XML rangehe, das Schöne daran an XML ist, dass ich diese implizite Struktur nun plötzlich explizit mache. Und damit kann dann der Computer mit unseren Texten rechnen. Denn wenn man ehrlich ist, am Ende ist XML nicht für uns, sondern für die Maschine.

SO: Kann es dann sein, dass die KI Strukturen entdecken kann, die für uns Menschen bis jetzt zwangsweise nur durch XML ausgedrückt wurden?

SG: Ja. Also ich habe mich da mal vor kurzem mit Invisible XML beschäftigt und da kann man über unstrukturierten Text Muster legen und die werden dann als XML sichtbar gemacht. Ganz clever. Und ich finde generative KI ist so eine Art Hochleistungs-Invisible XML. Also weil es zwar nicht so ganz strikt wie Invisible XML Regeln enthält, aber dafür auch sprachliche Nuancen versteht. Und ich fand es ganz spannend, ein Kunde von uns, der hat unstrukturierte PDF-Inhalte in Chat-GPT gefüttert, also unstrukturierte Inhalte aus dem PDF, um sie nach XML dann zu konvertieren. Und die KI hat erstaunlich gut die unsichtbare Struktur entdeckt, die in den Texten verborgen war und echt prima XML konvertiert. Also das war beeindruckend. Also wenn KI jetzt scheinbar Informationen aus dem Nichts schafft, dann ist es eben eher so, dass es existierende, aber verborgene Informationen sichtbar macht.

SO: Ja, ich glaube das Problem ist ja so, dass diese verborgene Struktur, also in manchen Dokumenten ist das da, aber in anderen da ist das, was wir auf Englisch, bei uns heißt das Crap on a Page. Das ist also, da gibt es keine Struktur. Und von einem Dokument zum anderen, da gibt es keine, also keine, die sind ganz anders. Also Redakteur 1 und Redakteur 2, die schreiben und die unterhalten sich niemals. Und also wenn die KI jetzt aus ein paar Stichworten ein ganzes Kapitel und eine Gliederung erstellt, wie geht das? Wie passt das zusammen?

SG: Ja, du hast recht. Jetzt haben wir die ganze Zeit drüber geredet: Wir nehmen PDF und dann wird da XML noch dazu gepackt. Aber wenn ich jetzt hier an der Stelle bin und sage, ich haue mal ein paar Stichworte rein und ChatGPT schreibt dann plötzlich etwas. Aber auch, ich finde auch da gilt dieser Gedanke, dass das eigentlich verborgene Information ist. Klingt vielleicht zuerst mal ein bisschen gewagt, aber da entsteht nichts Neues, nichts völlig Überraschendes. Wenn ich jetzt, sagen wir mal ChatGPT, einfach frage, gib mir mal eine Gliederung für eine Maschinen-Dokumentation. Und dann kommt da was raus. Ich denke, das würden die meisten von unseren Zuhörern genauso hinschreiben. Das ist nichts Neues. Das ist versteckte Information, die in den Trainingsdaten steckt, die durch die Anfrage einfach sichtbar gemacht werden. Denn letztendlich erstellt die generative KI diese Information aus meiner Anfrage und dieser riesigen Menge an Trainingsdaten. Und die Antwort, die ist so gewählt, dass sie gut zu meiner Anfrage und den Trainingsdaten passt. Die, ja, legt sich so ein bisschen wie so ein Layer da drüber, sodass das einfach gut das simuliert. Und am Ende habe ich damit dann keine neue Information, sondern hoffentlich die benötigte Information in einer besser verarbeitbaren Form. Entweder wie vorhin beim Beispiel mit dem PDF, mit XML angereichert oder ich habe jetzt eine Gliederung. Und ein bisschen stelle ich mir das vor wie so bei einer Saftpresse. Ja, die erfindet den Saft ja auch nicht, sondern die holt das aus den Orangen einfach raus.

SO: Informationen besser verarbeitbar zu machen, also das klingt doch fast schon wie eine Tätigkeitsbeschreibung für technische Redakteure. Und was ist mit anderen Methoden? Also wenn wir jetzt Metadaten oder Knowledge Graphs haben, wie sieht das denn da aus?

SG: Stimmt, das ist neben XML natürlich auch total wichtig. Also Metadaten, Knowledge Graphen. Ich finde Metadaten, die verdichten Informationen auf wenige Datenpunkte und die Knowledge Graphen, die machen dann die Beziehungen zwischen diesen Datenpunkten. Und gerade dadurch machen Knowledge Graphen, aber auch Metadaten ja unsichtbare Informationen sichtbar. Denn die Zusammenhänge, die vorher implizit wahr waren, die können jetzt durch die Knowledge Graphen nachvollzogen werden. Und das lässt sich prima mit generativer KI kombinieren. Am Anfang waren die Knowledge Graph Experten ein bisschen nervös, das konnte man merken auf Konferenzen, aber jetzt sind sie eigentlich ziemlich froh, dass sie festgestellt haben, generative KI plus Knowledge Graphen, das ist viel besser als generative KI ohne Knowledge Graphen. Und das ist natürlich prima. Das ist übrigens nicht der einzige Trick, wo wir in der technischen Dokumentation etwas haben, was der generativen KI auf die Sprünge hilft. Wenn man mit Large Language Models große Wissensbasen durchsuchbar machen will, dann macht man das ja heutzutage mit RAG, also Retrieval Augmented Generation. Und damit kann man sehr kostengünstig eigene Dokumente mit einem vortrainierten Modell wie ChatGPT kombinieren. Und kombiniert man jetzt RAG mit einer Facettensuche, so wie wir das in den Content Delivery Portalen in der TechDoc normalerweise haben, dann sind die Ergebnisse viel besser als mit der üblichen Vektorsuche, denn die ist am Ende ja nur eine bessere Volltextsuche. Und das ist dann auch wieder eine Möglichkeit, wo strukturierte Informationen, die wir eben haben, der KI auf die Sprünge hilft.

SO: Also bist du dann auch der Meinung, dass die strukturierte Information, durch KI nicht obsolet wird, sondern sogar noch wichtiger wird?

SG: Ich habe schon den Eindruck, dass sich so ein bisschen der Glaube durchgesetzt hat, strukturierte Informationen sind besser für KI. Ein bisschen sind wir dann natürlich, glaube ich, alle biased. Also wir müssen das glauben. Das sind ja die Früchte unserer Arbeit. Ein bisschen ist es auch so, also genauso wie der Apfel vom Biobauern natürlich gesünder ist, als der konventionelle Apfel aus dem Supermarkt. Ich denke, das ist wissenschaftlich klar erwiesen. Aber am Ende ist ein Apfel immer besser als eine Packung Gummibärchen. Und das ist es, was bei KI so disruptiv sein kann für uns. Denn am Ende machen wir Informationsvermittlung. Und wenn der Anwender Informationen bekommt, die ausreicht, die gut genug ist, warum sollte er dann noch die Extra-Meile gehen, um noch bessere Informationen zu bekommen? Ich weiß nicht.

SO: Ja, also ich interessiere mich wirklich an dieser, also Gummibärchen-Karriere. Da will ich mal ein bisschen mehr hören. Aber warum ist das denn so, sagen wir mal, pessimistisch für die Redaktion von dir?

SG: Ich glaube, mein Bild ist ein bisschen größer geworden in der letzten Zeit. Ich glaube, da geht es mir gar nicht so sehr um die technische Dokumentation. In der technischen Dokumentation sind wir ohne strukturierte Daten aufgeschmissen. Das wird nicht funktionieren. 

Aber wenn wir das größere Bild machen, bei Quanos haben wir ja nicht nur ein Redaktionssystem, sondern wir machen auch so einen digitalen Informationszwilling. Und dann sitze ich immer in diesen Arbeitskreisen drin als der Typ aus dem Tech-Doc-Bereich. Und da muss ich immer hinnehmen, dass unsere besonders gut strukturierten Informationen aus der Tech-Doc, also die mit den besonders viel Vitaminen und sekundären Pflanzenstoffen, das ist halt doch in der Realität da draußen eher die Ausnahme, wenn wir uns die Datensilos angucken, die wir im Info-Twin zusammenfahren wollen. Und als ich jung war, da habe ich noch dran geglaubt, dass wir die anderen davon überzeugen müssen, auch so zu arbeiten wie in der technischen Dokumentation. Das wäre doch echt prima gewesen. Aber wenn wir ehrlich sind, es klappt halt nicht. Die Vorteile, die wir in der technischen Dokumentation dank XML haben, die sind in den anderen Bereichen für die einzelnen Kollegen zu klein, als dass sie umsteigen wollen. Also Ausnahmen bestätigen die Regel. Das bedeutet, da draußen gibt es Tonnen von Informationen, die in diesen unstrukturierten Formaten eingesperrt sind. Und die können nur mit KI zugänglich gemacht werden. Das wird der Schlüssel sein.

SO: Und wie machen wir das? Also wenn jetzt XML da nicht der richtige Pfad ist, dann wie sieht das aus?

SG: Naja, also nehmen wir ein Beispiel. Also viele unserer Kunden sind ja Maschinenanlagenbauer und gucken wir mal auf die Zulieferdokumentation. Da kommen für einen Auftrag, mehrere Dutzende PDF. Und natürlich hat die Redakteurin dann so eine Checkliste und sie weiß, was sie in diesem Haufen PDF suchen muss. Das Prüfzertifikat, die Wartungstabelle, Ersatzteillisten und so weiter. Und obwohl die PDFs ja komplett unstrukturiert sind, also dieses unstrukturiert, wie wir halt als XML-Leute das dann so nennen, sind wir Menschen in der Lage, diese Informationen zu extrahieren. Und das Spannende daran, eigentlich kann das jeder. Also dafür muss man kein Spezialist für Abfüllanlagen oder Industriepumpen oder Sortiermaschinen sein. Wenn du eine Vorstellung davon hast, was ein Prüfzertifikat, eine Wartungstabelle, eine Ersatzteilliste ist, dann findest du die. Und jetzt kommt’s. Dann kann die KI das nämlich auch.

SO: Aha. Und also geht es dir in diesem Fall eher um Metadaten oder um was anderes?

SG: Nee, du hast schon recht. Also es geht hier in der Tat um Metadaten und Verlinkungen.

Ich finde das spannend, was das mit unserem Sprachgebrauch macht. Denn wir haben uns ja so angewöhnt zu sagen, wir reichern die Inhalte mit Metadaten an. Aber in vielen Fällen haben wir einfach nur die unsichtbare Struktur explizit gemacht. Da ist gar keine Information dazugekommen. Da ist nichts reicher geworden, sondern einfach nur klarer. Aber jetzt stell dir mal vor, dein Zulieferer hat keine Wartungstabelle geliefert. Dann musst du anfangen, die Wartungsarbeiten zu lesen, zu verstehen und die notwendigen Informationen zu extrahieren. Und das ist ziemlich mühsam. Selbst hier kann dann die KI noch unterstützen. Aber wie gut, hängt dann schon davon ab, wie verständlich die Wartungstätigkeiten beschrieben sind. Und umso mehr spezifisches Hintergrundwissen notwendig ist, umso schwieriger wird es, für die KI hilfreich zuzuarbeiten.

SO: Und wie sieht das denn aus? Also hast du ein Beispiel oder ein Use Case, wo die KI gar nicht weiterhilft? 

SG: Wie schon gesagt, das hängt natürlich dann vom Kontextwissen ab. Ich hatte von einer Kundin mal Teile der Risikoanalyse bekommen. Und da ging es darum, kann man daraus mit KI Sicherheitshinweise erstellen? Und ich habe dann gesagt, ja klar, guck mal die Risikoanalyse an und dann guck mal an, was die Redakteure daraus gemacht haben. Und es waren mustergültige Sicherheitshinweise. Aber es stand so wenig in der Risikoanalyse drin, dass beim besten Willen konnte man da nichts mit Künstlicher Intelligenz machen, sondern das ging nur, weil die Redakteure ein wahnsinnig gutes Produktverständnis hatten und auch noch die Normen im Hinterkopf hatten, die dafür notwendig waren. Da war eben die Information nicht in diesem Input versteckt, sondern im Kontextwissen. Und das ist so speziell, das ist natürlich auch nicht im Large Language Model vorhanden.

SO: In so einer Anwendung oder in so einem Anwendungsfall siehst du dann überhaupt keine Möglichkeit für KI?

SG: Also zumindest nicht für ein generisches Large Language Model. Also sowas wie ChatGPT oder Claude, die sind da chancenlos. Es gibt die Möglichkeit in der KI, diese Modelle nochmal zu spezialisieren. Man kann die ja mit kontextspezifischen Texten feintunen. Aber ob wir da im Normalfall ausreichend Texte haben, weiß man im Moment noch nicht so. Da gibt es die ersten Experimente. Aber denken wir nochmal zurück an die Wassermoleküle. Für einen Eisberg oder schon für einen Schneemann brauchen wir ziemlich viele davon. Also heute ist letztendlich so, welche Hilfsmittel unter den Gesichtspunkten dann auch, also Feintuning ist echt teuer. Also Kosten. Dauert lange. Also auch Performance ist ein Thema. Und wie praktikabel ist das? Haben wir Trainingsdaten? Also unter diesen ganzen Aspekten, was da jetzt wirklich der goldene Weg ist, um so ein generisches Large Language Model für die Textarbeit für sehr spezifische Kontexte brauchbar zu machen, ist einfach noch unklar. Weiß man heute einfach nicht.

SO: Kannst du denn heute schon sehen oder voraussehen, wie die generative KI die technische Dokumentation verändern wird oder muss?

SG: Ich finde das noch echt mehr so einen Blick in die Kristallkugel. Also das ist noch gar nicht so einfach einzuschätzen, welche Use Cases jetzt für den Einsatz von KI in der technischen Dokumentation vielversprechend sind. In der Regel hast du eine Aufgabenstellung, wo ein textueller Input nach einer bestimmten Maßgabe in einen textuellen Output transformiert werden soll. Und früher galt da Garbage in, Garbage out. Die Large Language Models nach meiner Meinung verändern diese Gleichung nachhaltig. Input, den wir mangels Informationsdichte früher nicht automatisch verarbeiten konnten, den können wir jetzt durch universelles Kontextwissen anreichern, so anreichern, dass er verarbeitbar wird. Fehlende Informationen können nicht ergänzt werden. Das haben wir ja jetzt besprochen. Aber diese unausgesprochenen Annahmen, in der Tat, die können wir mit reinpacken. 6Und das hilft uns in der technischen Dokumentation an vielen Stellen, weil sich eine gute technische Dokumentation ja unter anderem dadurch von einer schlechten unterscheidet, dass weniger Annahmen notwendig sind, um den Text zu verstehen, beziehungsweise auch, wenn man ihn maschinell verarbeiten will. Und deshalb finde ich Informationsverdichtung statt Wissenschöpfung für mich so eine Art Ockhamsches Messer. Ich betrachte mir die Ausgabenstellung. Geht es jetzt einfach nur darum, verborgene Informationen sichtbar zu machen oder sie in eine andere Form zu bringen, dann ist das einfach ein guter Kandidat für den Einsatz von generativer KI. Oder geht es jetzt eher darum, durch den Rückgriff auf andere Informationsquellen die Informationen zu veredeln? Dann wird es schon schwieriger. Wenn ich jetzt diese Informationen, diese anderen Informationen in einem Knowledge Graphen habe, wenn die dort schon aufgeschlüsselt sind, dann kann ich ja die Informationen explizit vor der Übergabe an das Large Language Model anreichern. Und dann geht das auch wieder. Wenn aber die Informationen, zum Beispiel das inhärente Produktwissen im Kopf des Redakteurs ist, wie bei der Risikoanalyse meiner Kundin, dann hat das Large Language Model einfach keine Chance. Das wird da keinen Mehrwert generieren. Dann muss man eventuell nochmal überlegen, kann man die Aufgabenstellung noch irgendwie aufteilen? Vielleicht gibt es einen Teil, wo dieses Wissen nicht notwendig ist und ich habe einen vor- oder nachgelagerten Prozessschritt, wo ich mit der KI was optimieren kann. Und ich finde, da wird in der Zukunft die Musik spielen. Diese Kunst, das Machbare vom Unmachbaren zu unterscheiden, und das wird eher so eine Art Ingenieurskunst sein, das wird in den kommenden Jahren der Faktor sein, der entscheidet, ob die generative KI mir einen Nutzen stiftet oder nicht.

SO: Und was glaubst du, mehr oder nicht?

SG: Ich glaube, wir werden das raustüfteln. Aber es wird viel länger dauern, als wir das glauben.

SO: Ja, also ich glaube, das stimmt.

Und also vielen Dank, Sebastian. Das sind wirklich ganz interessante Perspektiven und ich freue mich auf unsere nächste Diskussion, wenn in so zwei Wochen oder drei Monaten was ganz Neues da in der KI ist und wir uns noch mal darüber unterhalten müssen, ja, was können wir denn heute machen oder jetzt machen? Also vielen Dank und wir sehen uns …

SG: … demnächst irgendwo auf diesem Planeten.

SO: Irgendwo.

SG: Vielen Dank für die Einladung. Mach’s gut, Sarah.

SO: Ja, und vielen Dank an die Zuhörenden, besonders zum ersten Mal im deutschen Raum. Weitere Informationen sind bei scriptorium.com verfügbar. Thank you for listening to the Content Strategy Experts Podcast, brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes for relevant links. Thank you for listening to the Content Strategy Experts Podcast, brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes for relevant links.

The post Strategien für KI in der technischen Dokumentation (podcast, Deutsche version) appeared first on Scriptorium.

In episode 168 of The Content Strategy Experts podcast, Sarah O’Keefe and special guest Leslie Farinella, Chief Strategy Officer at Xyleme, discuss the challenges facing content operations for learning content, insights for navigating information silos, and recommendations for successful enterprise-wide collaboration.

Why do we still have these silos of content? Back to what you said, Sarah, if we’re thinking about the learner experience, the learner doesn’t distinguish between classroom, e-learning, looking something up, or going to technical documentation. They just know, “I gotta get my job done. I need to perform. I need to know what I’m doing.”

— Leslie Farinella

Related links:

LinkedIn:

Transcript:

Disclaimer: This is a machine-generated transcript with edits.

Sarah O’Keefe: Welcome to the Content Strategy Experts Podcast brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize, and distribute content in an efficient way. In this episode, we talk about the challenges that organizations face with content operations for learning. Hey, everyone. I’m Sarah O ‘Keefe, and today I’m delighted to welcome Leslie Farinella of Xyleme to the podcast. Xyleme, as you may know, has recently been acquired by MadCap Software, which also owns Flare and IXIASOFT. So Leslie, welcome. Tell us about yourself and your role at Xyleme/MadCap.

Leslie Farinella: Hi, Sarah. I’m super excited to be here today. So I’ve been at Xyleme for over the last eight years. Actually, prior to that, I was in the learning content space, but on the business side, helping organizations to drive performance within their workforce. And I realized that, you know what, if we wanted to scale, we were going to have to bring technology to help solve this problem. So I got really excited. So I jumped over to the product side. And since I’ve been at Xyleme, I’ve pretty much covered almost all of the roles, ending up with my last role being the chief strategy officer.

SO: And so here we are. And I think you’re probably the perfect person to talk to about this topic where we’re getting a lot of interest all of a sudden. Well, from my point of view, maybe not from your point of view, but from my point of view, we’re getting a lot of interest in content operations for learning content. 

LF: Yeah.

SO: So people are asking questions like, if I have overlapping content between my tech comm content and my learning content, why, you know, why can’t I combine those in some efficient way as opposed to what I’m doing now, which is this terrible copy and paste or worse rewrite without, you know, people ever talking to each other. But also we’re hearing from learning organizations that don’t actually have what I would consider to be tech comm content who need a more mature content workflow. So they’re asking questions like, “How can I develop learning better, faster, cheaper?” So what does that look like on your side of the fence?

LF: We absolutely hear the exact same thing, and I think it’s only gonna get worse because if we think about the root cause and think about what’s really driving this conversation and what’s making this conversation escalate is the speed of change and the need to drive agility within the organizations so organizations have to adapt faster than they have before which means people have to learn new skills new mindsets and new behaviors faster than before and which means inevitably they have to learn on the go, which means that performance support and tech comms is part of that learning. And as you and I know, cause I know you and I’ve had past conversations breaking down that silo between tech comm and learning is gonna be essential to driving that agility that organizations need to change. And that’s why they’re feeling the pressure.

SO: And so what does that look like? You know, Xyleme in particular is an enterprise learning content management system, which perhaps I should have said in the intro. What does it look like when people start considering something, you know, a solution like that? What’s the executive-level argument for that?

LF: Speed, agility, cohesiveness, learner experience. And I think that what we all have to remember is when you’re buying something like a CCMS or an LCMS, you know, component content management system or learning content management system, they’re kind of flip sides of the same coin, but they also need to work together. And I think that is the change in mindset we need in the industry is that if you think about learning, you have formal learning. I take a course. Usually, I’m a novice. I need some scaffolding. But the majority of the learning, once I kind of get my initial scaffolding happens by experience. It happens by solving problems. And inevitably that means looking stuff up. So it means going back to the documentation because no one’s going to go to the LMS to go flip to halfway through the e-learning course to look something up that’s just very painful. So what I hear from the top executive level is how do we make that whole system work together? How do we consider it from a job performance perspective and moving people from a novice to proficiency across that entire spectrum, which is learning and tech comm. And that’s where this idea that we have these separate systems and these separate processes really start to get in our way. And I think that’s where the opportunity is, is to see how do we break down that silo and how do we think about how these technologies can work better together or maybe even collapse into a single tech stack.

SO: Yeah, and I think that, you know, a big part of this is if you if you go back 20, 25, 30 years, we had classroom training, basically, and we had paper like books or maybe a cheat sheet or a job aid, but, you know, some sort of a printout. And so the distinction between I’m going to go to a class and learn the things and they’re going to give me a like a student guide or a textbook or, you know, but something, some sort of supporting material. And then there’s my reference library of books. And today, we still have that. I mean, we still have that distinction between class, e-learning, blended learning, and online, and all the rest of it. But there’s that bucket. And then there’s that bucket of, OK, there’s this other book adjacent or book-derived stuff. However, today, it’s all sitting on the same website. And so now as an end user, as a software user or learner, I show up on your website, your product website, and like, hey, I’m blocked on this task that I need to do. I’ve got a job I need to get done. I don’t know how to do it. And I just frankly don’t care. I just want you to give me the answer. Now, I don’t care where it lives. Not my problem. But give me the answer and give it to me better, faster, cheaper. And then, you know, infamously, we always say, “Don’t ship your org chart,” except we always do. So what does it look like to start to foster these connections and improve the integration or the interaction or the, I’m struggling for words, which is probably a symptom of this problem. What does it look like to start fostering those connections to improve the end-user experience?

LF: I think what you just said, end user experience. You know, we have to map that user experience. And I think that’s one thing that the learning side has done well is they’ve invested in the LMS, the learning experience platforms. Everybody still complains about them, but at least they were, you know, investing and trying and those experiences are getting better and better because there’s more competition in the market. People are coming up with other tools. They’re bringing, you know, more algorithms into play and then, you know, AI will play into that as well. But what they haven’t done well is content management and structured authoring. So Xyleme is an LCMS. So actually, you know, there obviously are people on the learning space that have bought into, we need to bring structured authoring into learning. But it’s not the majority. A lot of organizations still haven’t done that. And I think that once you start to bring what tech docs already knew is, you know, you’ve got to standardize to personalize. You’ve got to bring in, you know, you got to think modular. You’ve got to be able to standardize against your terminology. And then you can start to scale. That’s something that the learning side, you know, needs to learn and that’s something that the LCMS, which is the counterpart to component content management brings in and we’ve tailored it to the audience of instructional designers and learners to help with that transition. But the base ideas underneath the technology are the same. One of the interesting things in the acquisition with MadCap and the IXIA team when we started comparing products, we’re like, we do that, we do that too. yeah, we’ve always wanted to do that. You guys already have it, but we all, we realized very quickly we were solving the same problem and getting to the same result. We made them make different design decisions along the way, but we were solving the same problem and the fundamental premise underneath both technologies were the same, which then starts to beg the question, why aren’t they combined? Like, why are we still have these silos of content? If we’re thinking about back to what you said, Sarah, the learner experience, the learner doesn’t distinguish between classroom e-learning, looking something up, going to technical documentation. They just know, I gotta get my job done. I need to perform. I need to know what I’m doing. And I wanna, you know, ready myself for my next role in promotion within the organization. And they have expectations on their performance. And so how do we look at that and understand it needs to be more cohesive and how can we as both the tech docs and the learning industry break down that silo with the content but also the experience itself to make that more cohesive. 

SO: Yeah, I think one thing that’s sometimes overlooked in this is that the default emotional state of a person who is looking for information is something like frustration and anger, right? Because they’re not reading for fun. They’re not going to class for fun. I mean, probably. They are doing it because this class or this learning piece or this piece of information that I don’t have, is standing between me and getting the job done. I need to generate a pivot table and I don’t know how, so show me how to do it. I need to do a thing and until I do the thing, I can’t progress in my tasks of the day and so I’m annoyed. And we’ve set aside knowledge base for the purpose of this conversation, but knowledge base usually is even worse because usually that’s something like my system crashed, why? So they’re not just annoyed, they’re like incandescently angry because something is not working. Okay, so, and I think you said something really interesting in there about how the learning experience, you know, the downstream user experience for learning, there’s been a lot of work put into that and comparatively less on the tech comm side. I’m not saying all tech comm is bad or anything like that, but when you look at some of the work that’s been done in producing really sophisticated e-learning and really interesting learning experiences on a platform of some sort, and then conversely on the back end, tech comm has done a huge amount of work around reuse and efficiency and automated formatting and automated delivery and multi-channel and all these things, which I think there’s some advantages there and there’s some things there that I think the learning world can can probably leverage and you know vice versa so, you know, while you and I are ruling the world and we’re fixing all of this, you know, we can’t fix the integration next week and I mean I’ve been complaining about that for a while, but you know that is a legitimately difficult hard problem. But what are some of the steps that we can take as content creators, whether learning or tech comm, to start thinking about this sort of more unified approach to enabling content? What can we do there? And what are some of those first steps?

LF: I think the first step is we have to collaborate. I think the first step is, do you even know the people in your tech comm team or your learning team? Like, do you even know who they are? So, you know, I think there’s a conversation. I think the second one is to have a shared goal of we want to create a better user experience. Like, you know, an agreement that that is a goal that’s worth, you know, pursuing. And I think your managers, your VPs, definitely your leadership would agree that is. and then I think mapping that out. Like what would that learning experience look like? What’s your utopia? And then break it down, right? You can’t boil the ocean. You have to kind of have a plan. You know, what does the vision look like? And then what’s the first step? Start small. Like what’s the first step in the vision? And I think you and I talked earlier, a procedure is a procedure. Like there’s no magic. There’s some obvious low hanging fruit here as far as, you know, where you can share content that drives efficiency and makes sense. And then to the learner, you’re not coming up with different terminology. We all know the brain loves consistency because it helps with retrieval within the brain. So when I see the same picture, when I see the same example, when I see the same terms, it unlocks memory within the brain. It helps with retrieval. So, you know, we can make it easier for people. But then also looking at, you know, can we put some of that technical documentation and embed it in the learning content in the LXP so it’s easy to find where are people going? Maybe it is to the tech doc portal. Maybe we put it in both places and we figure out single source, right? We can update it in both places. We can keep that in sync, but really understanding and mapping that learner, the end user experience for performance and working together and understanding that we both have something to contribute to the conversation. I think, you know, to your point, tech comms can learn a little bit about experience and how people, you know, retrieve information, but the learning team can definitely learn a lot about structured authoring content management from the tech comm team. So bring those expertise together, which is 80% business and 20% technology. I mean, the first part is, you know, you just got to agree and set your goals and then figure out what’s the best technical solution that will drive those goals. And I would even argue, take it small. Like, you know, do experiment, see what works, what doesn’t work, trial and error. Cause I wish I had the whole answer. I don’t. I think it definitely is a problem that we need to invest in solve. And the only way we’re going to solve is through experimentation. But I also don’t think there’s a one size fit all answer either. I think each organization has legacy tech stacks. We all know we can’t just throw out the tech stack we have, you know, we have different competing business priorities. We have different skills and capacity within our teams. So do what you can. And I think that sometimes people throw up their hands and they do nothing because they think it’s too big. But you got to start small and you got to start somewhere. And step one is go have lunch with the people, maybe a virtual lunch these days, but on the other side, like talk to them, share you guys. At the end of the day, you have a common goal of driving performance within your organization. You have a shared mission. That’s where I would start.

SO:  Yeah, I like figure out who your counterpart is. That seems like a reasonable achievable goal. And then, yeah, and then back, you know, work from there. What can you, you know, can you reach consensus on shared terminology? Because, you know, I mean, never mind unified content authoring, that would be lovely but can we agree to call a car seat a car seat and not sometimes a safety seat and sometimes a baby seat and sometimes a something else? Because that would be like a really good start.

LF: Yeah. And the more things you can agree on, the more things you’ll find to agree on. So start with that. How do I share, you know, the procedures? How do I keep stuff in sync? You know, how do I even reduce the time between, you know, product release, the technical documentation and any formal training that needs to have? How do I make sure, you know, how can I generate FAQs? There’s a lot of things that you could brainstorm that you could do together, which then it fosters that collaboration.

SO: Mm-hmm.

LF: And then figure out what are the technical barriers I’m hitting. And I’ll say this as a vendor and then talk to the vendor and say, hey, here’s the business problem we need to solve. We think it’s a market problem. We think there’s value with you. We need you to fix this. Like we need you to be able to integrate these systems. And again, from the vendor side, if you make a good business case and you can show that the market in general, it’s good for the market, you can probably push their roadmap. But if you don’t speak up, if you haven’t tried, how do you know what those barriers are? So how do you know what to push? So just because it doesn’t do it today doesn’t mean you can’t get a solution.

SO: And the bigger you are, the more we would like you to kindly contact the vendors because…

LF: Yeah, the more money you have, the more clout you have. But I’ll be honest with you. As far as our roadmap on the vendor side, many times anyone who’s willing to experiment and to put some skin in the game as far as a real use case and to work together, I would rather build features and integrations based on real-world examples and real-world data than a theoretical PowerPoint we may put together from a nice product feature. And I know most product vendors are the same.

SO: I’ll have leverage.

LF: So partnering with your tech vendors and coming to them with, this is the business problem we want to solve. This is why we think it’s worth solving. And partnering with them to solve it is going to help to break down some of those technical silos. And the good news is on the MadCap side, because we do have the IXIA, we have the Flare, we have the Xyleme, that’s our vision is to how do we bring it together? It’s not gonna happen overnight because we all have. Like I said earlier, we all kind of made different design decisions which aren’t necessarily all compatible right this second, but we’re figuring out how do we make them more compatible? Like who’s got to kind of give up what and how can we make these work together? And because they’re all in our product stack, we have a vested interest in doing that. And honestly, we’re looking for customers, if there’s any MadCap customers out there listening, we’re looking for customers who want to partner with us on that journey and help us to figure out the answer because we know the problem pretty clear. We know some of the answer, but the only way you truly find the answer is by partnering with customers to figure it out.

SO: So I have to ask you about AI because we’re not allowed to do podcasts without asking about AI anymore. Tell me a little bit about your take on AI in the content universe that you live in.

LF: Yeah, I can, you know, there’s so much buzz about AI generation and the large language models and chat GPT. And I think because it kind of like wowed us all and it made the news and not that there’s not some efficiencies to be found there around summarization and descriptions. Cause one thing we know is that. The quality of the descriptions that go in the LMS and the LXP really drive retrieval or people being able to find something. And humans actually write really bad descriptions. AI does a better job of writing descriptions that search can find. So I think there’s something there there. But what really excites me is AI retrieval. Being able to match content to a person, like to me specifically based on my role context, where am I searching from? Am I searching from within Salesforce? Am I searching within my technical app? You know, what gives some idea of what I might be my problem that I’m having? Maybe even send error messages in what’s my region? What are my current skills? What are my skill gaps that would get me the information that I need faster and just the information that I need, not, you know, the 20 page document and now I’ve got to go find page five of 20. The great thing about AI retrieval is it can just bring me topic seven out of 70 and just bring that back to me. So I think that really solving that retrieval problem is huge, that time to an answer. The second one is AI data. AI’s been doing a lot with data. It’s not new news as far as data classification, looking at patterns. But if we think about if our common mission is performance and people being able to do their job, understanding holistically somebody’s journey from novice to proficiency and expert and what really drove those. And we might find out it’s all on the managers. And I argue a lot of it is their manager, you know, their manager and their coaching and had nothing to do, nothing against the audiences we’re talking to, but had very little to do with the learning team and the comp team. It had a lot to do with the managers, but understanding that and how we contribute into that journey will help us to understand what’s really important. And the nice thing about AI is it can bring in a lot more data and look at patterns that are much more sophisticated than us as humans can. We can’t hold that many variables in our head at one time. So I’m excited about AI to bring personalization of content, matching people to content, helping us better understand the value of the content we write and what drives that value of the content so that we can drive those best practices because I think we guess a lot and we have our ideas, we might be surprised at the answer. And then yeah, I mean, AI generation does definitely have a role. I don’t want to say it doesn’t have any, but honestly, it doesn’t excite me quite as much as the other two.

SO: Well, I, you know, I sort of lost interest early on when I asked chat GPT to generate a bio for me and it informed me that I had a PhD, which I mean, cool, but no. So, you know, it, it just, there were a couple of other things like that. It, and, and you said this earlier, you know, it is, it is important in our context to get the information right. And the thing that ChatGPT and the other generators don’t necessarily do is accuracy. They generate plausible content. But if we care about getting it right, cut the blue wire, then the red wire. no, wait, wrong. So it’s important to have this stuff be correct. And that’s the thing that GenAI really struggles with because it doesn’t really have a concept of correct.

LF: Yep. I think that’s where we are sitting on a gold mine with our content, because if you think of RAD, which is Retrieval Augmented Generation, which is the current, you know, leading answer as far as proprietary information that must be correct, it really is about retrieval. And what it does is it points to your vetted database of content. Well, where are those? By LCMS? CCMS? Gold mines of content, because, well, AI can do unstructured content. So not saying that you can’t give it a PDF or PowerPoint, whatever unstructured content. If you give it structured content, it’s like rocket fuel. It’s just easier, it’s better. And if you tagged that content, even if you use AI to help tag it, but if you’ve tagged that content, now that retrieval accuracy goes up exponentially, so we are sitting on rocket fuel. If you’ve already invested in an LCMS or a CCMS, you’re doing structured authoring, you have rocket fuel to drive your AI solution. And you don’t need AI to do it. It’s not that it has AI inherently in our databases. It’s just that we have the content that’s going to generate those AI agents and help to generate those answers and drive those right answers. And one of the key things when you think about proprietary content in these rag systems is the attribution. So it will provide a response. It’s not totally, it may summarize it, but it’s not rewriting it to the, it’s not just making it up like Jack GPT would, where it’s writing it from scratch. It is retrieving it. It may summarize it, but it gives an attribution. It tells me where it got that content from so as the person looking at it, I can decide whether I trust that source and I can verify it. So if it’s red wire versus blue wire and the wrong blue wire, something’s gonna blow up, I can go check the source and say, okay, yes, I trust that source and I’m gonna cut the blue wire.

SO: And on that cheery and I think hopefully explosive note, that seems like that sounds like a good place to wrap it up. Leslie, thank you so much for coming on. I hope we’ll continue this conversation and drive some positive change and some new cool integration and cooperation possibilities. And with that, thank you for listening to the content strategy experts podcast brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes for relevant links.

The post Overcoming operational challenges for learning content, feat. Leslie Farinella (podcast) appeared first on Scriptorium.

In episode 167 of The Content Strategy Experts Podcast, Sarah O’Keefe, Alan Pringle, and Bill Swallow discuss the difficulties organizations encounter when they try to create a unified content experience for their end users.

AP: Technical content, your tech content or product content, wants to convey knowledge so the user or reader can do whatever thing that they need to do. Learning content is about improving performance. And with your knowledge base content, it’s when, “I need to solve this very specific problem.” So those are the distinctions that I see among those three types.

SO: Okay, and from a customer point of view, what does this mean?

AP: Well, in reality, I don’t think the customers care. They want the information available, and they want it in the formats they want it in. And also, they want the right information so they can either get that thing done, improve their performance, or solve a specific problem.

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Disclaimer: This is a machine-generated transcript with edits.

Sarah O’Keefe: Welcome to the Content Strategy Experts Podcast brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize, and distribute content in an efficient way. In this episode, we talk about the challenges of content operations across the enterprise. Hi, everyone. I’m Sarah O ‘Keefe. I’m here today with two partners in crime, Alan Pringle and Bill Swallow.

Alan Pringle: Hello.

Bill Swallow: Howdy.

SO: That first one was Alan, and the second one was Bill. Good luck with that everybody. So I have a big topic today. I want to focus on the intersection of technical content, learning content, and knowledge base content. And Alan, what’s the difference between the three?

AP: Okay, let me see if I can break this down, because I’m sure people have very strong opinions about this, and we may hear about them, but this is how I’m gonna break them down. Technical content, your tech content or product content, wants to convey knowledge so the user or reader can do whatever thing that they need to do. Learning content is about improving performance. And with your knowledge base content, it’s when, “I need to solve this very specific problem.” So those are the distinctions that I see among those three types.

SO: Okay, and from a customer point of view, what does this mean?

AP: Well, in reality, I don’t think the customers care. They want the information available, and they want it in the formats they want it in. And also, they want the right information so they can either get that thing done, improve their performance, or solve a specific problem.

At the end of the day, they don’t care what department or what group wrote it. They just want it, and they want it then and there.

SO: So this enabling content is like, here’s how you can get your job done. Here’s how you can do the thing you need to do and move on with your day so that you can generate the report or write the thing or do the code or whatever it is. They need this content so that they can do the thing. So then, we have all these silos, right? We have technical content in its silo, and we have learning content, and we have knowledge base content, and then we have tools optimized for each of those use cases or for each of those sets of authors. So, now, is this a bad thing from a content perspective?

AP: That is possibly the worst leading question I’ve ever heard on this podcast. The worst. 

SO: Okay, I’ll rephrase.

AP: You don’t need to, but of course it’s bad. It is very, very bad. And the reason that it’s bad is because there is so much overlap in this content. Roughly half of technical content, there’s overlap because you’re both dealing with tasks. You’re dealing with tasks. 

SO: Procedures, yeah.

AP: Yeah, step-by-step instructions. So you don’t need two sets, one for each group. Why are we doing this? And when I say we, I mean the entire content world because folks, we are. You’ve also got overlap between your technical/product content and your support content. Troubleshooting instructions, Q&A’s on avoiding very specific problems. Same exact stuff, yet again, we’re often maintaining two different versions of that information. So there you go.

SO: So what we want is shared content, right? But we can’t do it because the tools aren’t there. Is that right? It is right. I know it’s right.

AP: Well, yeah, I mean, but it’s not just the tools. It’s the people that write this content because they often have, shall we say, fairly strong opinions that they need a special flavor or they need a special twist on the content. So it’s tools, but they’re also these opinions that the content creators have that inform these problems as well, I think.

SO: Okay, and then Bill turning to infrastructure, what does this look like from an infrastructure point of view as opposed to a, I mean, shared content is kind of an infrastructure problem, but I think there’s additional ones. What does that look like?

BS: Goodie, it’s my turn. Yeah, so shared infrastructure is a big one, you know, getting everyone to kind of play in that, you know, that same sandbox. But there are other things that really need to be shared across the enterprise. 

AP: Hmm.

BS: So things like taxonomy, you know, making sure everyone is aligning, you know, with the same terms, the same way of categorizing things, the same way of organizing information, their localization workflow, and even the vendors that they’re using, you know, that they’re all going under the same process so that they get a uniform result back. And then, you know, design systems, making sure that there’s a federated search in place, and making sure that anything that’s being produced for customer or reader consumption has the same unified experience might be a little bit different from content type to content type from delivery platform to platform, but in the end, you have a unified experience so that people aren’t relearning how to engage with your content in every context you produce it.

SO: So from an infrastructure point of view, what does it look like today to set up shared infrastructure? Can you tell us a little bit about the software tools that are available that allow you to do all of this in a unified way?

BS: You know, it’s too big of a list. And that list is basically consumed with things like duct tape, string, Bondo, you name it. There is nothing out there that will give you a unified experience across the enterprise for every content type out there. Right now, it does not exist.

SO: So on the authoring side, I think there’s some unified delivery kinds of integrations. But I think we’re talking about the back end. 

BS: We’re starting to see a lot with portals that are starting to collect a lot of information and present them all in one unified space, or at least provide one universal point of access for that content. And we are seeing some tools start to reach out and kind of embrace other traditional content silos. So things like, for example, being able to do develop all of your content in one single place and be able to push to a same branded let’s say knowledge base and documentation portal But I don’t think that there’s anything out there that really grabs everything and says okay. We’re going to do you know manuals We’re going to do other tech content. We’re gonna do web-based references we’re going to do knowledge base articles and tech support guides and training materials, you name it, and produce it all from one source to all these different things. So we have a lot of duct tape and string in place at the moment.

SO: And point solutions like, hey, we’re optimized for learning. Hey, we’re optimized for KB. We’re optimized for tech com. And I mean, it does seem to me that there’s a really big disconnect between what our clients are asking for and what the market has available because our clients are asking for slash demanding unified authoring solutions. And like you said, we have duct tape and string to offer them.

BS: Mm-hmm.

SO: So, okay, so if let’s step back a little bit and say you don’t do this. So you take the departmental approach and you push your tech com content through your tech com solution to the web and you push your KB to a KB article database thing and you have learning content which goes to a learning management system and therefore some sort of a learning platform. What happens when those are not unified? And I’ll, Alan, I’ll start with you. What happens with that if they’re not unified from a content point of view?

AP: Well, the terminology you’re using is not gonna be consistent or often is not consistent across your content types. For example, you go to your knowledge base, and you find a support article that uses a certain term for some widget. And then later on, when you try to search for the name of that widget and some other content, like on the product side of the content, and that product side uses a slightly different term, you’re not gonna get a search result because they’re using different terminology for what is really the same exact thing. So you have that lack of alignment. And the same thing is true, for example, with your product content and your training content. You may have slightly different how-tos or tasks to accomplish the same exact thing. So you’ve got those contradictions there in how to do things, in terminology, and you’re not getting a consistent voice at all in what you are presenting to your customers because of these departmental silos that we were talking about.

SO: And then Bill, on the infrastructure side, what do you see there in terms of problems that surface?

BS: A lot of it comes around or comes back to user experience, you know, because all these tools have very, I guess, a targeted focus. They have a lot of custom feature sets that are built just for that type of content. And a lot of the more generalized features are built out in slightly different ways. And you don’t have a lot of, or you may have a lot of ability to customize, but generally they’re not customized for whatever reason. Either it’s too difficult, no time, one group likes it one way, one group likes it another way. So you have these disjointed user experiences, just going from one area of the website to another. So being able to navigate manuals online to going over to a knowledge base and seeing a completely different interface and not knowing how to navigate it out of the box. So you’re now asking your customers to learn how to use your content in addition to having to use your content to find information in the first place.

SO: So we’re, I mean, we’re doing a lot of complaining, right?

BS: It’s fun to complain.

SO:  It is fun to complain. But I guess as consultants, our job is, in fact, to take on the complaints and then come up with a solution. So in the absence of the magic system that does all the things, you know, one thing we’ve seen a lot of customers do is make that compromise where they say, okay, we’re gonna take the thing that’s optimized for A, but we’re gonna use it for A and B even though it’s suboptimal for B. And of course, then the B people feel like B-class citizens, which isn’t great, but enterprise-wide, it’s very, very helpful. On the taxonomy side of things and some of these others, it does feel as though you can build that over the top and then just integrate it into all the other tools and push it down onto those. So I guess that part’s okay-ish. But I mean, what does this look like? And I guess my question to both of you is what’s the solution here? I mean, what’s the path forward and where do we want this to land? I mean, for our personal gratification, but mostly for our customers. What do our customers need the solution to look like so that this is, infamously, the line is you don’t want to ship your org chart, right? You don’t want your website to be a reflection of your org chart at a level that is recognizable to the end customer because, again, they don’t care. So what are some of the solutions here? What are some of the options that people have?

AP: Well, I think one thing you’ve got to do and step back and realize this is not just a tech problem. Now, the tech problem is very real in regard to the silos because you’re using different sets of tools, especially on the authoring side and the content creation side to get things done. But I think all of those content creators need to step back and think a little more globally across the company and not just about this is just for my people, this is just for me. Need to take a bigger step back and think, how can other departments potentially use this information? And then you start getting into tech, how can they actually reuse it? And that’s where you slip away from more culture to tech and how it can enable that sharing and that reuse.

BS: Mm-hmm.

SO: And some of the things like terminology is a good example. If you standardize terminology, you can ask people to follow that across all their systems, right? Like use this term and not that term does not require a unified, you know, content management solution. It’s just a writing practice. And you could layer the terminology management over the top of multiple systems. I mean, it’s more expensive, but you could. Bill, do you have any hope?

BS: Mm-hmm. There’s always hope. You know, we’re starting to get there and especially as, you know, at least systems are starting to be able to somewhat talk to each other via API. So there is a way to share information across. It’s not a, it’s not what I would call anything remotely close to, you know, intelligent reuse, because you’re still duplicating content from one system to another. But at least if you’re consistent about writing in one place, and pushing it out where it needs to go via those hooks, then it’s better than authoring everything separately.

AP: You still have a single source of truth in what you’re talking about and that’s the end goal or it should be the end goal for this problem.

BS: Exactly.

SO: And it might be helpful to look at single source of truth less as the process of doing a task, like how do I change my password in a database, right? There’s a four-step or a two-step or one-step procedure, but there’s a procedure and there’s only one way of doing it. And I think a lot of times the ultimate solution to this is to do some, essentially, forensics on where does that information originate. And if it originates here and I am a downstream user of that information, that’s fine. Just don’t ever modify it. Always go back to the source of the information and modify it at the beginning and then flow it back through. The problem that arises is that in a scenario where flow it back through involves manual processes or copy and pasting, it’s always going to fail because people fail, right? People don’t do the thing. And so you get those inconsistencies and now there’s four different ways of changing your password. One in the tech docs, one in the learning and like two in the knowledge base. And now what do you do with it?

AP: And you’ve got frustration among your users because they’re getting inconsistent information. And then you’ve got frustration with your content creators because they constantly feel like they’re having to go hunt for something or it is not worth my time to go find it. 

BS: Mm-hmm.

AP: I’m just gonna copy and paste and then they forget to update one of the umpteen versions they have. And then they’re stuck in this constant go go go process so it’s bad on both sides of the content equation for your content creators and the people who were consuming that content as well.

SO: Okay, well, this is super encouraging. And with those helpful words from Bill and Alan and maybe me, but mostly them, I will leave you to it. And so with that, thank you for listening to the Content Strategy Experts podcast brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes for relevant links.

The post The challenges of content operations across the enterprise (podcast) appeared first on Scriptorium.

In episode 166 of The Content Strategy Experts Podcast, Sarah O’Keefe and Alan Pringle check in on the current state of AI as of May 2024. The landscape is evolving rapidly, so in this episode, they share predictions, cautions, and insights for what to expect in the upcoming months.

We’ve seen this before, right? It’s the gold rush. There’s a new opportunity. There’s a new possibility. There’s a new frontier of business. And typically, the people who make money in the gold rush are the ones selling the picks and shovels and other ancillary services to the “gold rushees.”

— Sarah O’Keefe

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Disclaimer: This is a machine-generated transcript with edits.

Alan Pringle: Welcome to the Content Strategy Experts Podcast brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize, and distribute content in an efficient way. In this episode, we’re checking in on the state of artificial intelligence. Things are moving really fast in the AI space, so we want to let you know we recorded this podcast in May of 2024. Hey everyone, I’m Alan Pringle.

Sarah O’Keefe: And I’m Sarah O’Keefe, hi.

AP: And we’re going to talk about AI yet again, but we need to circle back to it because it’s been a while and kind of assess the space right now. Last week I saw a really great meme. It was a still of Carrie Brownstein and Fred Armisen from the Put a Bird On It sketch from Portlandia. And it said, “Put an AI on it!” And that’s kind of where we are now.

SO: Yay.

AP: So many companies, so many services, so many products look at this AI thing that we’ve got now. And a lot of these AI birds, if you will, have landed on content creation, kind of our wheelhouse. So let’s pick that apart for a minute.

SO: So I guess we can start with generative AI, GenAI, which is a ChatGPT and all of its general ilk, right? The chat interfaces. And generally speaking, at least for technical content. There does seem to be an emerging consensus that this is not where you go for content creation. You’re not going to start from scratch. Now, maybe you get it to throw out some ideas. Maybe you can do a first draft, but overall, the idea that, you know, ChatGPT or generative AI is just going to generate your docs for you is not the case. So there’s a big nope on content creation, but there’s also a big yes for productivity enhancement. I wrote a draft, but did I write it at the appropriate seventh or eighth grade level? Can I run it through the AI and let it clean it up? I need a summary. I need this cleaned up. I need my XML tag set corrected. I need a proposal for keywords that metadata that I haven’t put in yet, those kinds of things. So there does seem to be a rising level of capabilities in that space, in that productivity enhancement, how can I take this thing that I wrote or that I created and refine it further to get to where I need to be.

AP: Yeah, I was at a conference a few weeks ago, and in the expo hall, so many of the vendors were selling an AI service or some kind of AI add-on. And my thought was, how can the market possibly sustain all of these new products and new services? And I know there was an article in the New York Times last week that was talking about the business viability of AI. And it really doesn’t matter how cool or neat what your AI tool does. If there’s not business viability behind it, you’re going to have a really hard time in the marketplace, because you’ve got so many established players like the likes of Google and Microsoft who are really starting to dig into AI, and does it leave room for anyone else? So part of me wonders, is this going to help some vendors, hurt some vendors, or some vendors just going to go away at some point because of this tussle with AI features?

SO: Well, I mean, we’ve seen this before, right? And it’s the gold rush. There’s a new opportunity. There’s a new possibility. There’s a new frontier of business. And typically, the people who make money in the gold rush are the ones selling the picks and shovels and other ancillary services to the “gold rushees.”

AP: Exactly.

SO: And so to me, I’m starting to think about this as it’s going to fade into the background eventually in the sense that it would not occur to me at least to write a document without a spell checker and/or, you know, some sort of built-in grammar checker. They’re super useful, but I don’t necessarily do exactly what they tell me at all times. I look at what they tell me and then I use my own judgment. So I think that’s where we’re going to land where AI is going to be this useful tool that sort of a little bit fades into the background and that has human review. And we’re starting to see people refer to human in the loop, just as we did with machine translation, which is another place where you can look for patterns. What is AI adoption gonna look like? Go look at machine translation. Sometimes it’s good enough, sometimes it needs a human in the loop. Sometimes if you’re translating, let’s say, literary fiction, it’s maybe not that well suited, because it’s just not going to pick up on the kinds of things you need to pick up as a literary translator.

AP: Yeah, yeah, I agree. Is this going to be a feature that you accept as part of whatever suite of tools that you’re using? It’s just built in and there it is. So let’s talk now about something a little more complicated and I think maybe a little more dangerous with AI and that’s intellectual property. It has always been a problem, and there are all kinds of lawsuits flying about with different content creators claiming that different AI engines are stealing their copyrighted content, that sort of thing. And I don’t think that haze, that cloud has really been removed at this point. It’s still a problem that we need to address.

SO: Yeah, it’s a huge question mark. And, you know, it’s terrifying from the point of view of if I use AI and the AI injects something into my content or into my code that is that belongs to somebody else that’s copyrighted by somebody else. What’s that going to look like? What’s going to happen? And I have seen, you know, differing opinions on this from all sorts of people in our industry, in adjacent industries, from attorneys, non-attorneys, everybody has an opinion on this. And the thing is that the responses are, they just run the gamut from, do not use under any circumstances because we could get ourselves in trouble to eh, whatever, YOLO, it’ll be fine. I saw a comment just the other day along the lines of, well, I can’t believe that people would get sued for this because everybody’s doing it essentially. And I mean, they might be right. I’m not saying they’re wrong, but remember Napster? I mean, they got taken down. 

AP: Yes, they did.

SO: We now have streaming and those kinds of things, but the original one that was kind of the unlicensed in a pirate version, really, did get taken out. And I haven’t the slightest idea whether the regime that we’re under right now is going to end up like, you know, a Napster or like a Spotify. Not a clue.

AP: Yeah, yeah. And this conversation on IP intellectual property kind of ties into something else I’m going to talk about too. And that’s the regulatory angle. Different governments are taking a look at this. And I think that’s absolutely worth discussing as well.

SO: Yeah, again, I think more questions than answers, but just in the last, say, two months, the European Union has passed an AI act, which divides AI into risk categories based on what kinds of things it is doing. And so they’re banning certain kinds of AI, they are regulating certain kinds of AI, and then they’re allowing certain other kinds, you know, but they’ve basically said, if it’s in the highest risk category, then you have to follow these kinds of rules, or maybe it’s not allowed at all. China has taken a different approach. The US has so far done nothing in terms of regulations. 

AP: Nothing.

SO: We’ve talked about it, but we haven’t done anything. So it’s quite likely that at least in the short term, the regulatory schemes will be different in different locations, in different countries. And then just in the past week or so, I bumped into a pretty interesting article that was talking about GDPR, the European Privacy Regulation. And basically, under GDPR, you have certain kinds of rights. You have the right to be forgotten. You have the right to be taken out of a database, and somebody has anonymously sued OpenAI because when they go into OpenAI and they say more or less, “What is my birthday?” It gives them the wrong answer. So this is apparently a, again, anonymous but public figure, and we’ll put the article in the show notes. So this anonymous public figure is suing AI on the grounds that it reports an incorrect birthdate for that person and you have the right to have your data be correct under GDPR. Well, OpenAI’s response to this lawsuit is along the lines of it is impossible for us to correct that, right? Because there’s not an underlying database that says John Smith date of birth X. It’s just generative, which is sort of the crux of the whole issue here. But the legal footing, the legal argument appears to be that under GDPR you can’t say, oh, I’m sorry, it’s impossible for me to correct that fact. So you’re just gonna have to deal with it. And so we’re gonna have a really interesting collision between the content that generative AI creates, which may or may not be factual. That’s not a thing, right? And GDPR, which is an established law. And I have the slightest idea where that’s going.

AP: Yeah. And I think too, in this vacuum or with this absence of regulations in some countries, you’re going to see companies then make their own rules. And a lot of them have telling employees when they, what they can and cannot do with AI, which really, like I said, in the absence of there being any kind of rules to help kind of create a baseline, it makes sense, especially if you’re trying to be very careful about liability, putting out incorrect information or using copyrighted information that you shouldn’t be, it would make sense for you to protect your bottom line by basically instituting your own guidelines for how you can and cannot use AI.

SO: And if you look at social media where the platform is basically not responsible for the content that people are putting on it, right? So if I’m on LinkedIn, let’s say, and I put something on LinkedIn and then somebody else reads it, if what I’ve said is problematic, they’re gonna sue me, not LinkedIn, right? LinkedIn is not responsible. And with AI right now and generative AI, who’s responsible? If I go and I generate something using generative AI, and then I publish it in some way, and then I guess I assert copyright on it, which is a whole other can of worms because I can’t right now under current law. But if I do that, then if what I post is wrong and legally problematic, so it’s, I don’t know, defamatory or something, then like who gets sued? Do you sue OpenAI for being incorrect? Do you sue me? Do you sue the platform I put it on? Like who is responsible when the AI gets the content wrong? Is it me because I didn’t validate it or correct it or clean it up? If we build out a chatbot that’s AI-driven, that’s generating information and you know, we’ve already seen this use case legally. You know, the company is going to be responsible for the information that the chatbot is putting out if the chatbot is sitting on the company website. But if it’s impossible to be sure that the chatbot’s gonna be right, what do you do with that?

AP:  Yeah. And it’s been established as of last night before we recorded this, the big Met Gala happened. And apparently there were two quite realistic photos or images of Katy Perry in two different dresses. How she pulled that off, I don’t know, at on the steps at the Met Gala. So and the problem is a lot of the social media platforms absolutely could not, they just didn’t do anything. These photos just exploded. 

SO: Right. Because they were fake, right?

AP: They were fake. 100% generative AI fake. And even her mother was fooled by it, apparently. And Katy Perry’s response was, “I was working, so no, I was not there.” But it just goes to show you that you’re right. Once these images got out there, they exploded on social media, and those platforms really are not equipped to handle flagging of that or even removing it at this point.

SO: “I didn’t know you were going to the Met Gala.”

AP: Exactly.

SO: Yeah, I’ve seen a decent number of it largely in AI news coverage where, you know, a New York Times or Washington Post will put up an image and they’ll put a slug on it or a caption that says this is AI-generated. And usually, they watermark it. So it’ll be actually in the image, not on a caption below, but in coverage of AI itself. And for example, talking about this deep fake or, you know, the one where the Photoshop UK princes, princesses, that one. They carefully labeled the photo itself as altered on the photo so that people would know when they were reading the news story what they were dealing with. But of course, you know, that’s not going to happen on social media, where it’s just going to fly around the world faster than anything. And so, yeah, I think I don’t know. I mean, I’m saying I don’t know a lot. That’s where we are. We don’t know.

AP: We don’t. Yeah.

SO: We don’t know what’s going to happen. Things are changing very quickly. The legal and regulatory and risk scenarios are completely unclear. I did want to touch on one other sort of more practical matter. We’ve seen a lot of complaints recently, and I think I’ve experienced this personally, and I think you have as well, that search, like Google search, Bing search, all the traditional search is actually getting worse. 

AP: Oh, 100%. Yeah.

SO: You search and you get bad, you know, just junky results, and you can’t find the thing you’re actually looking for. And the basic reason that that’s happening is that the internet, the worldwide web has been flooded with AI-generated content at a scale that has completely overwhelmed the search algorithms, such that they are unable to sort through all this stuff and actually give you good information. I mean, we did at one point, a year ago, have a scenario where if you had a pretty good idea of what you were looking for and you typed in the right search phrase, you would get some pretty decent results and you could find what you were looking for. And now it’s just junk, which has to do with AI-generated content that is micro-targeting SEO phrases. And ultimately, I think this means, well, it’s going to be a war between the search engine algorithms and the AI-generated content. But I suspect that search and SEO as we know it today is done because it won’t win this. And then people are like, “Oh, I like it a lot better when I go to ChatGPT, and it gives me this nice conversational paragraph of response,” notwithstanding the fact that that paragraph of response probably isn’t super accurate.

AP: But it’s so chatty and friendly.

SO: Uh-huh. So I’m not terribly optimistic about that one either. And so what does this mean if you are a company that produces important and high-stakes content, like all of our clients, basically? What does that mean to you? And I think it means that you’re going to be looking hard at a walled garden approach, right? To say, if you are on our site, and you are behind a login on our site, we have curated that information, we have vetted it, we have approved it, and you can rely on it. If you go out there, you know, in the big wide world, there’s no telling what you’re gonna find out there. And that implies that I have to know who I’m buying from so that I can go to the right place and get the right information. And I’ve already found myself doing this. Instead of going to a big general purpose, e-commerce buy things site, such as the one I’m carefully not mentioning, I find myself saying, oh, I need a new stand mixer. I like KitchenAids. I’ll go to their site and buy it there. And so I’m buying direct from brands that I’m familiar with and that I know because that feels safer than going to the great big site that has a little bit of everything, including a stunning array of what seems to be problematic counterfeit and or knockoff kinds of things. So instead, yeah, but so if I don’t know the brand, if I don’t know the brand, then what? Like, how do I find the right thing if I don’t know where to start already?

AP: The same as true of information. Right. Yeah, I don’t think that’s going to be fixed anytime soon and it’s probably going to get worse after this podcast, in fact.

SO: So I’m concerned. Yeah, and you know, as a parting gift of, I guess, fear, we will put it in the show notes using a gift link. But there was an article that appeared in the Washington Post about a month ago, maybe two, having to do with apps for identifying wild mushrooms when you’re foraging. So this already seems kind of like a high-risk activity to me, just generally going out in the forest and looking for mushrooms that you’re going to forage and hope you get it right and you pick the really delicious one and not the one that’s gonna kill you. And Alan’s making faces at me because he hates mushrooms.

AP: I have the solution for this problem. Don’t eat them. But that’s not helpful. Yeah.

SO: Yes, you have a really simple solution. But for those of us who do like mushrooms and don’t want to die, there are a whole bunch of apps out there. And so there was some research done in apparently Australia on mushroom identification apps, which are apparently AI-driven, which seems like kind of not a good idea. However, what they found was that the best of the AI-driven apps was 44% accurate. And I wish for my mushroom identification app to be a whole lot more than 44% accurate, especially in Australia where everything kills you!

AP: So a 56% chance of poisoning yourself. That’s excellent. Great.

SO: Yeah, or at least of getting it wrong. But again, it’s Australia. And so if it’s wrong, it’s probably going to kill you because that’s Australia. So yeah, that’s not good. And that feels like a not acceptable outcome here. So I don’t know where this is going, but I am pretty concerned.

AP: Yeah. So as we wrap up, there are some good things to talk about, especially, there are a few. Sarah was whispering, “Are there? Are there?” Or made a face. There are, I mean, on the content-creation side, I think there have been some tools that have added some useful features, much like the spell-checker analogy that you talked about. But there are still so many unanswered questions in regard to intellectual property and legal risk. All of those things are still way up in the air. A lot of countries are trying to adjust by taking a look at regulations, but you know, those aren’t in place yet. So we’re in, we’re at a crossroads, I think, and we’ve still got to pay a lot of attention to what’s going on with AI right now.

SO: Yeah, you know, there’s some really, there’s some really nifty tools out there. It’s also worth pointing out that there have been tools that use machine learning and AI that are already out there. They just weren’t, it wasn’t AI front and center. Now everything, as you said, put an AI on it because you can get sales that way, and you can get attention. But there are a lot of companies that are doing some really interesting and really difficult work with this. And I want to, you know, I’m not against any of this stuff. I just want to make sure that we use these tools in a way that, you know, maximizes the good outcomes and minimizes the, “Oops, I ate the wrong mushroom.”

AP: Yeah. Fatal mistakes. Not a fan. Not a fan at all. Well, I think we’ll wrap it up on that cheery note about eating poisonous mushrooms on the Content Strategy Experts podcast. We go places, folks. We will talk about almost anything on this, not just content. So thank you for listening to the Content Strategy Experts podcast brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes for relevant links.

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In episode 165 of The Content Strategy Experts Podcast, Sarah O’Keefe and guest Patrick Bosek of Heretto discuss how the role of customer self service is evolving in the age of AI.

I think that this comes back to the same thing that it came back to at every technological shift, which is more about being ready with your content than it is about having your content in the perfect format, system, set of technologies, or whatever it may be. The first thing that I think either of us will say, and a lot of people in the industry will tell you, is that you need to structure your content.

— Patrick Bosek

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Disclaimer: This is a machine-generated transcript with edits.

Sarah O’Keefe: Welcome to the Content Strategy Experts Podcast brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize, and distribute content in an efficient way. In this episode, we talk with Patrick Bosek about the changing role of content in self service and whatever the opposite of self service is, maybe just service. Hi, everyone. I’m Sarah O ‘Keefe, and I’ve got Patrick Bosek, the CEO of Heretto with me today. Hey, Patrick!

Patrick Bosek: Hey Sarah, it’s good to be here. I guess to be back, technically.

SO: Yeah, you’ve been here one or two times before, so I’m going to cut to the chase here. And our topic today is self-service content and how things are changing in self-service content. So talk a little bit about that. What’s going on?

PB: Well, I think to talk about self-service content, we have to talk about what’s changing in, I think, self service more generally, which you kind of alluded to in the idea of, you what is the opposite of self service, right? So the landscape is, as I see it, is very interesting today, because historically we had what was very obviously self service, and then what we had was very obviously not self service. So I guess just people or service or something—people service maybe. And for the most part, self service was content, right? So if you went someplace and you read something and you figured it out on your own, over the last decade or so, self services started to involve a little more action. You can go to a McDonald’s and self-service order a coffee today. We can talk about it in a minute whether or not we think that’s a good idea or a bad idea. But now as we’re getting into the age of true…intelligent, if you want to call them, virtual assistants, AI, those types of things. Now we’re in a place where the things that were very traditionally handled by humans. So helping you figure out what you really mean, helping you dig through something when you’re not exactly sure where you’re finding, finding it, or if it is possible, those types of questions are actually performing actions. Some of those are going to continue to bleed over into systems. So now self service isn’t just content anymore that you go and look up and then you yourself go and figure it out and do something. There’s going to be this mixing between these two things where the service that’s provided by automated systems is going to perform some of the things that humans were performing. They’re going to need a bunch of content in order to be able to do this properly. And probably also as instructions, you’ve got to teach these things what to do somehow. And as we all see with the way that we interact with them, you use words, you don’t use programming languages as much. So content plays a role in its traditional form. It continues to play a role in training people. It also plays a role in establishing what this new generation of systems that are going to help us perform actions and learn things and answer questions, what those things are going to do.

SO: So it feels like we’re adding another dimension to this, because when mobile apps first came out, the big development there was that they were contextually aware. So you can get your app to tell you what the weather is at your location because it knows where you are or it can know where you are. And it feels like this is a similar kind of thing that some of this is more a matter of not just, “Hey, here’s a page with some instructions,” but rather, you know, let me, let the system do some work around what your context is and what some of your knowledge is and adapt accordingly.

PB: I think there’s absolutely an aspect. So I would actually put that in the category of just like maybe even traditional personalization. You know, feed metadata in those are things about yourself and then you perform some type of a matching or a computation. And then you feed content back. So that’s, that’s effectively personalization out of its core. Like here’s some things about me. Okay. Then those match to some things about the content. Give me just the content that matters to me. I think where this starts to become new and really interesting is where you start to have systems, so probably, you know, AI-based systems that are actually not just like filtering or personalizing content, they’re actually manipulated in content or they’re manipulating your journey with the content. And one of the places that, you know, we are seeing more of that I think is an interesting place for this is in learning. So you start to think about how learning systems and self service have worked for a long time. And as much as there’s been like micro learning content, and then there’s been more organic things like that kind of stuff, by and large learning has been linear when it comes to self service. Here’s a guide, here’s a course, whatever it may be. And it didn’t matter if you didn’t need to know a good chunk of it or if you already knew a good chunk of it or whatever it may be. Like you went through that, right? That’s how that works. So that, I mean, it’s how colleges, college courses work. It’s how, it’s how learning works in general. Like unless you’re sitting across from a tutor, like learning is linear by and large, when it’s being taught. Well, with AI, all of a sudden we can deploy systems at scale potentially where you can always be sitting across from a tutor. The entire paradigm around how it is that we, you know, air quotes here, “self service,” if we still want to call it that, the things that we need to learn can fundamentally change.

SO: So what does it look like for us sitting in the content universe when customer experience is moving in this direction towards this, I guess, more sophisticated self service? Not just here’s what we have, deal with it, but rather here is information or learning or a chunk of content or whatever that is adapting to that person’s requirements. I think it is, right?

PB: In certain circumstances, it certainly has the ability to be adapting to people’s requirements. I think that’s the thing that we will absolutely be seeing. But more generally, what does content need to look like to give you the range of things you might want to do as an organization in this new paradigm? And I think that this comes back to the same thing that it came back to at every technological shift, which is more about being ready with your content than it is about having your content in the perfect format or the perfect system or the perfect set of technologies or whatever it may be. So the first thing that I think either of us will say, a lot of people in the industry will tell you is like, you need to structure your content. And I do think that the story for this on the learning side, on the traditional self service side, on the AI-agent side, and I think even on the people service side of things still does start there. But I don’t think it’s because self service is intrinsically something which is powered by structured content. What I think is that structured content really just gives you, the organization, you, the content creator, a lot more control over what goes into these systems no matter the range of intelligence that they have. And that means that you have input control on the experiences. And as we all know, LLMs, even when they’re backed by like RAG, retrieval augmented generation, or other systems that are meant to kind of keep these things fenced in, they’re black boxes. And the bigger the box, the blacker the box. So, the strategy, if you look out over the industry, there’s a lot of very sophisticated stuff, but some of the stuff that works the best is input control. And that’s where I think that structured content is really gonna be a key element of this, no matter how far in the future you look.

SO: Yeah, and I mean, my explanation of this to people, which I’m sure makes the actual AI experts cry, is AI likes patterns. And so if you feed it content that follows consistently the same pattern, you greatly improve your odds of getting good output from what you’re putting into the system. When you have stuff that’s not well organized or structured or anything else, you know, garbage in, garbage out—you’re gonna get a mess.

PB: So I think there’s that is true. There are caveats, but the thing that remains true at the center of that is that if you don’t have very precise control over what goes in, you lose an enormous amount of control over what comes out. So even there’s, there’s such things like overtraining, right? Where you can actually get AI that will produce, less high-quality results with certain quantities or certain types of training. And what you end up with in those circumstances is that like, okay, well, if your stuff is just a bunch of stuff and you stuff it all into an AI system. 

SO: That was excellent.

PB: It’s good, right? I gotta have some fun. So you don’t have any ability to say, okay, well, these pieces really, these are the ones that are impacting our outputs. Let’s pull this out, or even just iterate in an intelligent way. So, which part of the corpus, which part of the things that we put into this system are the ones that are having the negative impact? Retraining becomes a much more complicated process. And at the same time, when we’re looking out over deploying across multiple experiences, right? So, you know, let’s take the learning and reference. So, you know, probably speaking documentation portals, whatever they may be. Some people call them knowledge bases in certain circumstances. Those are the two obvious things, because in the past they’ve been highly bifurcated, even though they use a lot of the similar information underneath the hood. Well, if you’re trying to build AI-backed much more like personalized learning systems. Well, you can’t have the content in those systems being different than the reference content, because when you go and you look at the stretch of things that go into that stuff, well, if you have the AI system telling your, user something which is wholly inaccurate. And you can’t pull it back to the rest of the stuff that’s published on the internet, you can get highly divergent results, and you could end up in a circumstance where you have no ability to actually deploy these things properly. So you can’t have one set, which is very cottage based, like, you know, we will go in and we craft these things and one set, which is highly structured. And then you power all of the learning, the intelligent learning systems of the structured stuff, because it’s going to be easier. So you have to find a way to pull these things together, and then use the mechanisms underneath the content to put the right inputs into the right places.

SO: And we’ve been talking for years and years and years about problems with silos and how they’re an outgrowth of the organization itself, right? You’ve got a learning organization and a documentation organization and a tech support organization. They’re all producing content into their respective silos. And the question becomes, if organizationally that’s what the company looks like, then it is almost impossible to rip those silos apart or put them together, destroy them to collaborate across them because the org chart doesn’t encourage it or even makes it impossible potentially. And so then you’ve got different terminology being used by the same company but in different departments, which is really common. And then what? So we’re back to, you know, the fundamental truth that when you have a website as a company, even if you segment that website into like, oh, learning.xyz.com and docs.xyz.com and KB or support.xyz.com, your customers don’t care, right? I mean, they’re not interested in the fact that you have three separate organizations that all hate each other. That’s just not on their list of things they care about.

PB: So I mean, this goes back to the classic, like don’t ship your org chart, which is, yeah, obviously, right. So obviously we’ve been doing this in content for ever, basically. I do think that it’s gonna be, we’re gonna be forced to change because, you know, again, you go back to the idea of like, if you contradict yourself in your learning content and your docs content and it’s being read as written or as built and presented to a human being. Human beings are incredibly flexible creatures. We can go in and be like, oh, well, okay, fine. So like they didn’t update that piece, those dummies, they should have done this, but I understand what’s going on. But when you put an abstraction layer over that, now you have a system that just basically does what it’s told, you know, by and large, or understands what it’s, what it’s educated in, and what it’s told, it’s not going to have that same intuition. That’s a very human thing, even at this point in time. I don’t, I don’t really see the current generation of LLMs getting to a point of having that style of intuition. I mean, the thing that just happened with the ASCII art is a great example, right? And it’s a little bit divergent here, but bear with me. So people figured out how to hack these AI systems by going and asking them questions with ASCII art, which in one sense shows their brilliance because they’re able to understand it. But in the other sense, it shows their lack of intuition because they were like, oh, well, this doesn’t apply to my rules. Who cares? Right? And whereas a human being would have been like, oh, I’m still not allowed to talk about bombs. Right? It doesn’t matter if you’re in a theater, if you write, don’t yell bomb in ASCII or in Sans Serif, people understand that it’s still talking about the same general concept. So this is the same thing that you run into where you can’t have these discrepancies and stretch a single system over the top of these. And you can’t have a really strong customer experience that properly educates people, properly answers questions, and all those types of things, unless they’re joined together.

SO: Yeah, apparently in addition to ASCII art, if you use Morse code, that will also work around all the guardrails, which sounds fun. So, okay, so in our couple of minutes that are left here, how does Heretto and CCMSs in general, but Heretto specifically, how do they play into this?

PB: Yeah, sure. So that’s a great question and one I appreciate you asking it for obvious reasons. I’m going to answer the CCMS part first, then I’m going to answer the Heretto part. So CCMSs as platforms, and I think this is probably true for pretty much every major CCMS in the industry or in the space. They’re going to give you the ability to manage more structured content at a higher velocity and a higher level of governance. Now they’re all going to be able to do this, you know, to different efficacies, right? So some are going to do it better or worse for your particular circumstance. But broadly speaking, that’s why you buy a CCMS. A bunch of content, you want to be able to have your per author or per information developer content be higher than it is, and you wanna make sure that you have the proper amount of governance so that what you deploy is what should be deployed, right? It has to be good enough, it has to meet the criteria, especially today. So those are the things that you build into the process in the CCMS. And then, obviously, they help you track things like localizations as well, but I would broadly put that in the same bucket. So as we’re looking at, how does this relate to the future of the customer experience, you know, be it directly with the content or be it derived from the content through some intermediary system like AI. It’s the governance piece, and it’s also the quantity piece. You have to have enough to be able to answer all the cases to be able to touch all the learning points to be able to educate and guide these systems in all the proper ways. And now, because you don’t have that human intuition as your last fail save, the level of governance has gone up a bar. You have to be able to have much better governance on your content to be able to control inputs. So I think that this is a new age of CCMS. And it’s funny because we have seen an acceleration in interest from people and an acceleration in interest that’s educated where people are coming. And they’re like, we have to get this in order, because we realize that if we don’t have our hands around this, we’re gonna have a huge mess at the end of the toolchain. So I do think that people are more aware today, you know, it’s probably still relatively niche in the grand scheme of things, but there’s a growing awareness that the having the right systems in your content operations ecosystem to produce the right outcomes down the chain is gonna be critical. And CCMS for this style of content is 90% of the time gonna be the best place to start. Not to say there’s not other ways to get there. On the Heretto question, Heretto does all that stuff like the other CCMSs. Obviously there’s some aspects of collaboration and things like that that we think we do better. But I think the key thing as it relates to the future of these technologies that Heretto provides that you don’t really get in other CCMS technologies is our ability to efficiently and agilely deploy content into specific pods. So we have static publishing, we have the ability to generate HTML, PDF, all that kind of stuff, like all the other CCMSs. But we also have the ability to dynamically deploy content into an API layer where the content is in its own little pod. So you can kind of deploy as many little content APIs as you want. Most organizations have one big API that they deploy that powers an entire doc site. It can be tens of thousands or more, you know, topics. But you also have the ability to say, all right, I want to just deploy this here, and this API is only going to have this content in it. And that comes back to the critical aspect of the bigger the block box, the blacker the box as it relates to AI systems. So you don’t go hook your AI system up to the totality of, you know, your large API that serves your web experience or general web experience. You hook your AI up to this specific pod that only has these specific things in it. And therefore, you know exactly what’s going into that AI system, you know, be it for something that’s more like RAG-based, which is really just like search and summarize, I think it’s a much better name for it. Or BSL which is more training-based. So I think that’s kind of the critical piece that Heretto offers right now that is not present in other systems that, you know, relates to what we’re talking to today anyways.

SO: Alright, I mean, that seems like a good place to leave it, because basically you’re saying, hey, this stuff is coming. All these things are changing, and here’s a helpful roadmap for how to get there. Any closing words before I wrap this up?

PB: No, other than this is a really exciting time to be part of content. You know, we’ve both been here for a little while, and we’ve seen a lot of changes in this industry. But this is certainly unique. There is no doubt that the acceleration understanding and the change in the landscape over the last, you know, year, 18 months, I would say has been unprecedented. You know, I think you see that all the way from, you know, the types of experiences that we want to start deploying that we believe are possible, but we’re not totally sure based on new technologies. And then also the change in approach to things like learning content, seeing organizations suddenly starting to say, okay, so like PowerPoint is not our primary method of training people. Like our primary method of training people is going to be dynamic digital experiences. And we need to be prepared for that. And then whatever comes after that, you know, I think that this is a shift that, I’ve been waiting for, you’ve been waiting for, for a long time. I mean, like, for gosh sake, like we implemented the experience for DITA learning and training. What is it? One dot one or something like that, like 10 years ago. And we’ve had a couple of customers that have used it across that time, but it’s just been recently that we’ve had more and more people coming in and starting to use it. And there has, there seemed to be a bit of a Renaissance in the understanding around these things. So, I don’t know, this is just, it feels very new. It feels very fresh again, which I think is part of the structured content cycle. And this is the fun part of the cycle. So I’m enjoying it.

SO: Yeah, I would I would agree with that. So I’ll leave it there. Patrick, thanks for being here. Always good to see you. And with that, thank you for listening to the content strategy experts podcast brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes.

The post Self-service content in the age of AI with Patrick Bosek appeared first on Scriptorium.

In episode 164 of The Content Strategy Experts Podcast, Alan Pringle and special guest Chris Hill of DCL talk about where you can find redundancy in your learning content, what causes it, and how a single source reuse strategy can eliminate duplication.

You really start to run into trouble when you need to make version two, and you discover a problem with version one. If I’m making some marketing materials, maybe I need to use some information from the engineering team or from the manuals for whatever product I’m marketing. I might just copy that information over and put it into my marketing materials. Then, when we go to produce our training for that particular product, we might say, “Okay, I need that stuff. I’m gonna copy that from wherever I can find it,” which might be from marketing or engineering depending on where I look and who I know better or which repository is easier for me to get to. The problem here is that if anybody has made any edits along the way, they have to ensure that those edits are propagated through all these departments. And that doesn’t always happen. 

— Chris Hill

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Transcript:

Disclaimer: This is a machine-generated transcript with edits.

Alan Pringle: Welcome to the Content Strategy Experts Podcast brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize, and distribute content in an efficient way. In this episode, we talk with guest Chris Hill of DCL about learning content and where you can find redundant duplicated content, what causes it, and how a reuse strategy can eliminate that duplication. Hey everyone, I am Alan Pringle and we have a guest here today, Chris Hill of DCL. Hey Chris, how are you doing?

Chris Hill: Doing well, thank you, Alan. It’s nice talking to you.

AP: Great, yes as always. Chris, tell folks out there a little bit about yourself, DCL, and your role there if you would.

CH: Sure. DCL stands for Data Conversion Laboratory. And so we got our start doing data conversion, which is moving content between formats. And over the last, let’s see, that started in the 80s, if you can imagine a tech company starting in the 80s. 

AP: Yes, I can. I am of an age, yes.

CH: So since then, we’ve expanded out into lots of areas, but basically any kind of content transformation, workflows, content enrichment, all sorts of activities around content. So that’s our key theme. I joined DCL about four years ago, and I’ve actually been in the content management space for a good more than 20 years now, and have a lot of experience with both migrating from, you know, using tools like Word and such, and then moving into a content management system. I actually managed, product managed a content management system and then got into conversion. And as part of my job here, I oversee a product called Harmonizer, which is our tool for doing content analysis and specifically reuse analysis to find places where content is redundant, duplicated, and help users figure out what they need to do to improve that situation.

AP: Well, in this conversation today, I think we’re going to tap into all the wisdom that you bring to the table with your background and content and your experience at DCL identifying reuse. And let’s start with just the concept of redundant content. And there are lots of ways to describe this. And I’ve heard it referred to several different ways. Redundant content, duplicated content, overlapping content. If you would kind of give people a bird’s eye view of what we’re talking about here.

CH: So we’re really talking about any place where you’ve got similar or exactly the same content reproduced. And you usually know you’re doing this because anytime you hit that Control C and Control V or choose the copy paste menu, if you’re a menu person, anytime you’re doing that, you’re creating redundant content. And, you know, it’s usually the easiest way to get it done if you’re working in a tool like Microsoft Word or a word processor or a desktop publishing environment or something like that. Generally, you copy stuff from one document to another. And that can be fine for version one of those documents. Where you start to really run into trouble is when you need to make version two, and you discover a problem with version one. So if I’m making some marketing materials, maybe I need to use some information from the actual engineering team or from the manuals for whatever product I’m marketing. I might just copy that engineering data or whatever information over and put it into my marketing materials. And then when we go to produce our training for that particular product, we might say, okay, I need that stuff. I’m gonna copy that from wherever I can find it, which might be their marketing or it might be engineering depending on where I look and who I know better or which repository is easier for me to get to. And the problem with that is that if anybody’s made any edits along the way, they have to ensure that those edits are propagated through all these departments. And that doesn’t always happen. 

AP: It usually does not happen. You’re being kind.

CH: Yes, I am. So when the engineers find out, oops, we made an error here in the technical manual, we better fix that or somebody is gonna do the wrong procedure or come out with a bad result, they might fix their manual, but. Are they aware that there’s all this marketing material with that stuff in it? Are they aware that the education team actually copied the stuff from marketing? They may not have even talked to the engineers to tell them they were using that content. And so what happens is you pretty soon have a sort of information entropy where things start to go fall apart and the information gets out of sync and you may have inaccurate information through various departments that no one can really trace. So that’s kind of where I see this grow and it’s pretty much a natural feature of using computers and the more traditional desktop application approach to creating content.

AP: Absolutely, and you’ve really kind of covered the big picture here really well. And what I want to do is kind of move just a little bit away from that and talk now about, especially for people in the learning and training space, where they might see some of that content overlap. And you’ve kind of touched on one. Anytime that you have a new version of the product or service that you are creating content for, it’s very common to just copy and paste the previous version, create a new file set, and then make your edits and updates in there. There’s one scenario right there where you have used copy and paste, and there’s a very good chance there’s a lot of overlapping information between those two versions that probably really should be maybe one set of files instead of duplicated content. So that’s one example I can think of immediately off the top of my head. Where are some other places where learning and training people might see this duplication of content?

CH: Yeah, so, well, the copy and paste happens for a lot of different reasons. Sometimes you’ll have product diagrams that somebody has or engineering schematics or something like that that need to be part of multiple divisions. And you’ll see that stuff sort of get copied around, if you will. I think you make a very good point about new versions of the product or because even, even in the case where you wrote perfect content the first time, if you ever could do that. And I wrote it perfectly well for the current release of the product. When the product is upgraded or changed in some way or a new revision is released. When you make those changes, that doesn’t mean all the old product disappears. People are still accessing that older content.

And if you start to find issues in the older content that get addressed maybe through your user support, is that getting pushed up to the newer stuff? Because if the newer stuff did what you described, which is I copied it, I may not even know that that’s now inaccurate in the new release of the manual. Or vice versa, it could be someone using the new product who identifies a problem with our documentation, and we go back and we neglect to fix the old ones, well then all the users of the older product are going to run into that issue sooner or later.

AP: Yeah, and then you’ve got this whole layer too. What if you were delivering to all of these different delivery targets, different delivery formats, you’re using Microsoft Word over here to create perhaps more study guides or scripts or something like that. Then you’re also using PowerPoint over here to create slides. You are copying and pasting perhaps into some kind of software. That will help you with simulations or more audio video kinds of things. So in addition to what you and I’ve just talked about with the different versions, how those are out of sync, if you were copying and pasting content into all of these different tools that create these different delivery types, then this problem is multiplying rapidly because you’re gonna have to go in and touch all of that source for all of those different delivery targets; your Word files, your PowerPoint files, your Articulate content, whatever else. So it kind of can explode pretty quickly in your face.

CH: It sure does. And we haven’t even touched on if you’re in different countries translating to different languages, what do you do about all the translated content? And that can quickly overwhelm you as well.

AP: Exactly. Yeah, so basically this problem becomes exponential, both from say, the versions of your product to service, across the different delivery targets that you’re dealing with. And then if you have to localize that content, all of the, shall we say, bad behaviors that are in your, or let’s call them inefficient behaviors, that’s less judgmental. 

CH: There you are.

AP: Yeah, less judgy. These inefficient, behaviors are then duplicated in every single language that you crank out. So yeah, it’s very ripe for inefficiency. It’s very ripe for errors because it is unfair to expect a human being to go through and keep track of all of this. So things go sideways and you even touched on something else a little earlier.

CH: For sure, yes.

AP: And then in some cases, you are pulling content from other departments, other content creating groups. And then that’s another layer of this exponential explosion where if you’ve changed something and someone quote, borrowed that from your group, are you sure they’re gonna know that you changed that? Or you fixed it when they copied and pasted it into their version? And then think about the poor end users, the content consumers who were getting this information they’re probably not getting a consistent picture at all about what you’re talking about because of all this copy and paste all over the place. It’s a mess. Yeah. So let’s go on and try and put a more positive spin on this mess and start talking about the process for identifying this duplicated content. 

CH: That’s good.

AP: So what can people do to start kind of taking the pulse of this problem?

CH: I think a lot of it depends on your resources and your organization’s commitment, but there’s always things you can do, whether they’re smaller efforts or larger efforts. So, you know, at the very BMW view or let’s say Cadillac view, if you’re from the 80s like me. You would probably have a huge budget to be able to implement a whole new set of tools and workflows that allowed you to use all sorts of technologies to do what’s called single-source publishing. And that’s where you author in a format neutral format. And then you take those pieces and really you’re creating sort of Legos of content, you could imagine.

AP: I call them puzzle pieces, so yeah. Yep.

CH: There you go, puzzle pieces, Legos. And they fit together in lots of different ways. You can put them together for training. You can put the little pieces together for your user manuals. You can put some of the pieces together for your marketing materials. But the key is that you’re using the same piece in all of those places. And what these sort of advanced tools allow you to do is keep track of all those pieces. Use those pieces in all those multiple places and then still create your deliverables out of those pieces. So instead of authoring directly in PowerPoint when you’re writing a course or writing in Word when you’re writing a manual or maybe working on HTML when you’re creating your website instead of creating content in those sort of single-use formats you create your content in a neutral format and then you have it output to those formats. 

AP: Exactly.

CH: And so you still can deliver those end formats that you need to actually put out in the world, but you’re doing it from a single source of truth. And that’s that single content repository. Now that’s the ideal, that’s the perfect one.

AP: Yeah, and you’re not, and you are not going to get to what you just described overnight. You are not going to snap your fingers and that’s going to happen. So yeah, I think you’re headed kind of where my brain was. And that is you can start small with this and you don’t even have to think about tools. You can start very small and start thinking about, you know, where is this duplicated information? Just trying to ferret it out. And one way you can do that, you as a content creator have a very good idea of what is in your set of training content. You’re the people who are creating it. You know where the bodies are buried, where things are going wrong, where you’ve noticed that there’s this duplication. You could also work with a consultant like me who has been doing this kind of stuff for years and can help you by asking the right questions and maybe trigger some things in your brain. Oh yeah, I didn’t think about that. But there is also technology out there like your Harmonizer tool that can help people start to identify that reuse. And I think it’s worth noting it doesn’t have to be things that are exactly the same. Your tool can help find things that are fuzzy matches that are sort of the same because that’s equally valuable as well. And I want you to talk a little bit about how that process works because I think that’s important.

CH: Sure. So the tool we developed, which was actually kind of a companion to our conversion work, is we had the same problems. People come to us and bring those content reuse problems, and they would ask us if we could help them in some way, because when they’re converting content, even if they’re going to move to some neutral format or they’re just moving from, say, Word to FrameMaker or FrameMaker to something else. That was a lot of the work we were doing, but they would bring us lots of duplicated content. And sometimes at that conversion stage is a good time to nip that a little bit or make some headway against those duplications. So we developed Harmonizer as a tool that was very format neutral. It just basically extracts all the text from whatever content you have and puts those into blocks and then it compares every single text block to every other text block and it’ll tell you which ones are all the same, which ones are close and that close can be pretty far apart actually. So I could do things like if I had a sentence and I told you when you go to the store pick up some milk and then somewhere else I tell you to pick up some milk when you are at the store. Those aren’t exactly the same. And in fact, if you do a word-by-word analysis, they’re completely different. But if you do a harmonizer style analysis, we use some linguistic algorithms to be able to tell that linguistically, those are essentially the same thing, or at least very close in what they’re describing, even though the words and the letters are all in a different order.

AP: It’s the intent of that sentence, basically. Yeah. Yeah.

CH: Very much, yeah. So we detect that as well and put that into groups. So then you can look and you can say, okay, I’ve got this block of text. It says this. Here’s all the places Harmonizer will highlight where they’re different, sort of like a diff tool so that you can see, oh, I use the word or here and I use the word and in this other place. Or maybe I used one version of our product name, in some of the content and I am using a different version of the product name in another part of the content. Or maybe I’m comparing two products and their manuals are 75 % the same content just every now and then the product name is mentioned and that has to be different. All of those things can really illuminate why you have duplication. It can also help you find those places where maybe you’ve made corrections in one place and haven’t got to those other places because you might see, oh, this paragraph is the same except we added a warning at the bottom, do not do something. We better tell everyone else that warning in all the other formats that we’ve created. So that’s kind of what Harmonizer does. It’s not a magic bullet. It gives you a very large, well, if you have a lot of content, it’ll give you a large report if you’ve got a modest amount, you’ll get a modestly sized report. It’ll scale to whatever amount of content you want to feed it. What we do is we use it very strategically. And for instance, we can use it to identify just why you have maybe close but not matching content. So maybe you’re using inconsistent wording in different places. We can identify maybe if you have already some standard content, we can identify if there are places where maybe it varies in ways you didn’t expect. So you can check your standard content libraries if you need to. There’s all kinds of ways it can be used, but at its core, it’s again, just giving you those matches and helping you see, really shining a light on where your content is as far as redundancy.

AP: And one thing point I want to make here is it really doesn’t matter what tools you’re using to create content. This work you can do is not dependent necessarily on those tools. Like I said, you yourself can kind of do a self-service thing where you start to think more deliberately about where you think content is. You can work with a consultant who can help you figure this out. You can use a tool like Harmonizer to help dive deeper and really find this content. So there are all these layers that you can do. And the first layer is you can start thinking about that yourself. So there’s a lot of options there. So once you have started to identify this duplicated content through whatever those methods are that we just talked about, it’s time to get into a reuse strategy. And you’ve already touched on this really well. The core of that reuse strategy is you have a single source of truth for every piece of content, every piece of information, there is one version, one format-neutral version that you can then pull into all your different delivery targets and all your different types of content. So that’s kind of the core of that. Once you know where that duplication is, you can start coming up with this more formal reuse strategy. And I think you also pointed out to the copy and paste that is like the morning light going off, you’ve got duplicated content. There’s copying and pasting going on. That’s what you want to try to eliminate with the single source of truth. Give people a little idea of the benefit of the single source of truth. And I’m talking about both for content creators and for the content consumers because it falls on both sides, the benefit of that single source of truth.

CH: For sure it does. Content creators know this. We’ve already touched on when there’s a problem found or a change needed in the documentation. Maybe the product’s changing or was updated. If it’s software, who knows? Maybe we’ve added a new menu item. So we need to add that to the documentation. Well, if we’ve got a single source for everything and everyone draws from that source, we update that source and it will flow out into all the other channels without any real effort. Now, you can simulate this with your copy-paste activities, but you have to really formalize how you do copy-paste. So you’ve got to only copy from say the source of truth, not from each other or something like that as a starting point. If you can’t actually implement true, single source tool chain. Another area though where this really impacts is on the quality of the content you’re delivering to your readers and your consumers. We all know and I deal with this all of the time and this should be make everyone feel a little bit better that even a giant company like Microsoft has this problem. I work in SharePoint quite a lot. And SharePoint has a lot of different versions. It’s been around forever. One of the biggest challenges I have is when I go look for answers, and this isn’t to pick on Microsoft, by the way. Every software company, you could probably find some of this. 

AP: Absolutely.

CH: But I go looking in the content for something, I’ll read it one way in one place. I’ll read something a little bit different about the same feature in another place and Sometimes they’re just describing them in two different ways Because maybe one was written by the engineering team and one was written by the marketing department Maybe another version was written by the training department. So that’s going to happen. But then I also run into a lot of places where it’s not easy to tell when this stuff was even created. So it might be very old stuff that I’m looking at that no longer is even applicable. All these issues become simplified if you’re doing that single source of truth because you can start tying together a strategy to deal with that. When you just publish stuff out there and it all gets sort of thrown out in a fire hose to your consumers, that can become a very big challenge for them when there are all these inconsistencies in different language styles or different ways of writing the information.

AP: And if people are using all the different content that’s available out on your website to make a purchasing decision, and it doesn’t just have to be the marketing content, they can be looking at the product content. They can be looking at the publicly available training content. If they are getting mixed messages, different information that should be talking basically about the same thing, that can be a huge turnoff and it can hurt you financially because people will be like, I’m not comfortable buying this product or service because I’m getting mixed messages in the content that’s available out here. The bottom line is people don’t care what department or what your organization is like, what your hierarchy is, what your tree is, whatever you want to call it for your different departments and your management. They don’t care about that. They just want a consistent message, consistent information and they want to get it from wherever they find it and they want to be sure that it’s the same message they get regardless of what quote, department’s content that they’re touching.

CH: Yeah, when I’m working with your product, your product is really what I, how I see you. I see you through the product. I don’t see you through your departments and your channels and whatever organizational structure you’ve created to manage your company. So I think that’s a really important point you make to really make sure that that product experience is consistent and clean. And doing this, you know, even if all you’re doing is just trying to make things more consistent than addressing the redundancy issue can help just in ensuring that we’re presenting that unified view of our product to the world.

AP: This conversation really has probably given people a lot of food for thought. There’s a lot to think about when we’re talking about this duplicated content, redundant content. If we want to kind of back up a little bit and give people maybe one or two pieces of advice on where to get started, even if it’s starting small, what are some things that people can start to do now to start thinking about this bigger picture of duplication, reuse, single source of truth? Any recommendations there?

CH: For sure. So the first thing is you’ve got to tame a little bit of your Wild West if you’ve got that of content. So if I can just go on the corporate network and start willy-nilly looking around and copying and pasting stuff out of anywhere I can find it, which is sometimes the case, that’s probably a big area where you’re creating a lot of content entropy. So you need to think about that. And it may just be even a training issue. It may be a network organization issue. But you should start considering how you can make the authoritative repository accessible to everyone and then limit where they’re getting stuff to that authoritative repository. You don’t have to implement a whole new content management system and tool chain to do that. You can do that using permissions, using training, and having regular contact between the groups that create the content that’s getting copied around. So making sure there’s some interface between them so that they can coordinate and know that they need to coordinate these content activities. A lot of times that simple piece just gets overlooked because a lot of companies really treat content as kind of the afterthought. I’ve built the product, okay, hurry up, make a manual, do some training, do whatever, because we’re product-focused. And so it’s kind of natural, but you really need to see your content as an integral part of that product that you’re delivering so you can get started with just using the tools that you have and working on the processes and the consistency with which you apply those tools. You can also start strategizing for the future. So you can look at, okay, maybe we need to figure out, first of all, how much money is it costing us to copy and paste a lot? Again, knowing how much duplication you have, and then you can put estimates on, okay, if I’ve got all this amount of duplication, how much does it cost to make a change to this manual if we are to ensure that it gets to all the delivery channels, including marketing, training, all the languages, all the manuals? How much does a change cost? Once you start quantifying that, you might find out there’s a better budget than you think for working on this problem. 

Again, you’re going to have to look at your content itself and figure out how much redundancy there is. So planning that strategy, figuring out how much it’s costing you, all of that can be very helpful, I think. And then ongoing maintenance. How are we going to maintain it? I’ve been to a lot of organizations where they’ll do a big push to clean things up and they’ll say, okay, we’re going to hire someone, we’re gonna get some new tools going and man, we’ve fixed it, right? And so they fix it. And then three years later, they’re in the same boat they were in because they didn’t really follow up on that. They didn’t plan to maintain the content. Nobody was charged with the duty to ensure that we were adhering to the strategies that the tools were providing and nobody really had the responsibility to look at that stuff. So if you’re going to make the investment, you have to also have the follow-through. And a lot of times that involves consultants, because let’s be honest, if this is the first time I ever do this, I’m not going to do it very well. And I usually don’t get a chance to do this 100 times. I’m not going to do this over and over in my organization.

AP: Yeah.

CH: But if you find a consultant, you can find someone that’s done this 100 times for a lot of different organizations. And they already know where all the pitfalls are and where all the trouble is. And they’ll help steer you in the right direction the first time. Because you don’t get a lot of bites at this apple. Like, your company’s not going to say, oh, just keep working on content reuse for the rest of time. They’re going to want to see some progress.

AP: Exactly. Yeah, and it comes down to return on investment. You do not do these kinds of things for fun. You’re doing them for business reasons, and business reasons include making money and getting a return on investment on any kind of investment in technology, and that includes content technology.

CH: Absolutely.

AP: Chris, this has been very helpful. I think this is a good place to wrap up. Thank you so much for your insights. I think you’ve given people a whole lot to think about.

CH: Well, I appreciate the conversation.

AP: Thank you for listening to the Content Strategy Experts Podcast brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes for relevant links.

The post How reuse eliminates redundant learning content with Chris Hill (podcast) appeared first on Scriptorium.

If you’ve taken the courses at LearningDITA.com and you’re interested in starting a DITA project, check out episode 163 of The Content Strategy Experts Podcast where Bill Swallow and Sarah O’Keefe talk about the steps you can take to get funding.

“Showing up with cookies never hurts, but what is your executive’s motivation from a business point of view? What are they trying to accomplish in their goals for this next quarter or month or year, and so on? You need to show them, assuming that you can, that moving to structured content, moving to DITA, and changing tools is going to help achieve those business goals.”

— Sarah O’Keefe

Related links:

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Transcript:

Disclaimer: This is a machine-generated transcript with edits.

Bill Swallow: Welcome to the Content Strategy Experts Podcast brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize, and distribute content in an efficient way. In this episode, we talk to you about next steps after LearningDITA, how to get your boss to sign off on a DITA project. Hey everybody, I’m Bill Swallow.

March 2025 update: We have moved LearningDITA to a new platform. The Introduction to DITA course is still free, and you can sign up for courses at store.scriptorium.com.

Sarah O’Keefe: And I’m Sarah O ‘Keefe, hi.

BS: So yeah, we’re going to talk a little bit about what to do after you’ve completed your learningDITA.com courses and you have some DITA knowledge under your belt. So I guess we’ll start off with completing the courses. You pretty much have a working DITA environment then after you complete all of the courses on learningDITA .com, you have a batch of topics, a batch of tasks, a batch of other types of files, a map, you have a publishing scenario, you have some reuse going on. So it kind of makes for a neat little proof of concept package. But now that you’ve got that, how do you bring it to management? How do you sell it as we want to move forward in this direction?

SO: We’re assuming, of course, that I think a lot of the people that do LearningDITA, they come for different reasons. And a big chunk of it is just, I need to learn this because I want to be more marketable. I want to get a new job. I want to have a chance at the jobs that require DITA information or DITA knowledge.

BS: Right.

SO: But I guess what we’re focused on here today is this question of, all right, so you’ve run through the courses and you’ve decided that this is potentially a good idea for your company, for your employer. You really want to advocate for “we need to move our content into DITA because it feels like this is a good idea for this particular organization.” So what do you do next? And at that point, you’re right, you’ve got a proof of concept and that may be enough to show to your peers and maybe your immediate manager just to let them look at that. Almost certainly the next thing they’re gonna ask you for though is to take some of your content because the learning ditto ducklings are only gonna get you so far. They’re gonna say, well, how does this apply to our content? So probably you’re gonna have to go off and do some…

BS: Haha.

SO: …test topics or some test content using your actual live content. I think that’s probably step one. The key thing though, I think to recognize is that DITA and structured content is not just a tool. You’re not going to your manager and saying, hey, I need $500 for a piece of software or even a thousand.

BS: Mm-hmm.

SO: You can of course do this potentially with source control, which is at least free in theory, free but not cheap, right?

BS: Haha.

SO: And, but the effort of, for example, taking five or 10 or 50,000 pages of word content and moving it into DITA is really significant. And so you’re talking about a big, you know, departmental or even enterprise effort to make this happen. And so the bottom line is that somebody needs to agree that this is a good use of your time and or resources, which means you need an executive sponsor.

BS: Right. So how would you start moving up the chain to have those discussions in order to, I guess, get to an executive sponsor or how would you frame a pitch to an executive sponsor then? If you are convinced this is the right direction to move in, you have your proof of concept, you are hopefully moving some of your actual real-world content into DITA to beef up that proof of concept to pitch. What are the things we need to start thinking about?

SO: I think that many of us that live in this technical writing, technical communication world tend to be really interested in new technology. Something new comes along, we’re like, oh, this is so cool and I can’t wait to use it and I can’t wait to apply it and it is delightful and fun and new and different and nerdy. That pitch, you know, look at this, this is so cool. That doesn’t work unless you’re selling AI, then it pretty much works. But the question you have to ask is who is the person that’s going to fund this project? And the more money you need, the higher up the chain you’re going to have to go. And what motivates that person from a business point of view?

BS: Haha.

SO: Right? I mean, showing up with cookies and things never hurts, but what is their motivation from a business point of view? What are they trying to accomplish in their goals for this next quarter or month or year or whatever? And you need to show them, assuming that you can, that moving to structured content, moving to DITA, changing tools is going to help achieve those business goals.

BS: Mm-hmm.

SO: So the number one most obvious way to do this is to show cost avoidance, right? There’s a decent amount of research that says that if you’re using desktop publishing tools, you’re probably spending something like 50% of your time doing formatting work as opposed to content work. And the formatting automation that you have with structured content, gets you out of that formatting work. So you can basically say, hey, we were spending 50% of our time on formatting. Instead, we’re going to write some transforms, and then we’ll be done. We’ll have push-button processing, which is a pretty clear cost avoidance and a pretty clear gain. And we have a calculator for this that addresses looking at those issues, which we’ll make sure to put in the show notes.

BS: Mm-hmm.

SO: But there are other levers that may matter more to your executives and to your leaders than cost avoidance. Time to market is a big one.

BS: Mm-hmm.

SO: Can we get this stuff done faster? Not necessarily cheaper, but can we get it out the door faster? Or do you have these delays of, oh, I still have to reformat it, and oh, now all my numbering is wrong, and I have to go back through and fix everything, and it’s just a nightmare. Can we get a competitive advantage? Can we do a better job of supporting the brand? Can we do a better job of translation localization and expanding what we’re doing? You need to really understand where’s the business going and why? What direction is it going in? It is really, really common after a merger to have a situation where you have two or three or 15 mutually incompatible content development systems. And what you really need to do is bring them all together in the same way that the products are being brought together and the company is being brought together into one unified thing so that you can sell, you know, products A, B, and C, which used to belong to different companies as a unified set. Well, you need the content to also be a unified set, which pushes you towards a unified approach. And if that’s, you know, if DIDA can solve that for you, then that’s a story that’s gonna be compelling to a leader who’s dealing with merger headaches.

BS: Mm-hmm.

BS: So finding a way to kind of translate what you need to do to expedite and streamline your work to align with the company goals, or at least the goals of essentially the person with the money who’s going to make this effort happen.

SO: Yeah, and know, expedite and streamline is really useful and in and of itself doing your job more efficiently as opposed to less efficiently is generally a good idea. I think it goes beyond that into additional factors. When we talk about, so did a reuse is a great example, right? You walk into this presentation and you’re like, Conrefs are the coolest thing ever and look at these keys and look at what I can do with scope, right? And you’re…

BS: Haha.

SO: …you’re the people you’re presenting to are like what what what they have no context they don’t care if they’re software engineers you can maybe talk to them about like object-oriented things and how you can you know whatever but no you walk in there and you say okay um you know how we have this problem where your content over in this bucket contradicts the content over in this other bucket. And the reason is that we copy and paste from A to B, and then we update A, but we don’t update B. And they’re like, oh, yes. And then if right now you’re going to bring up that Air Canada issue with their chatbot that had incorrect information, which is a great example of this, where almost certainly what happened was that the chatbot was fed a bunch of information that wasn’t kept up to date.

BS: Mm-hmm. Yeah, they have no context for what you’re talking about.

SO: Or they were fed the incorrect information to begin with, well, that shouldn’t happen. You should have a single place where you stash all of that information and then you just push it to all of your endpoints, such as a chat bot. And, you know, solving those kinds of problems so that the company doesn’t get embarrassed slash sued slash held liable for making mistakes with their content is valuable. And that is you know, different and arguably more important than we can do it better, faster, cheaper.

BS: Mm-hmm. So yeah, it’s really avoiding those risks that you have in producing content where you can have inconsistencies. If you’re doing things right, you will write once, use everywhere by reference so that the same copy goes out in every place it needs to go. Are there any other things that we should really start looking at with regard to risk management there?

SO: One of the biggest challenges with making a change in tools, whether DITA or anything else at all, is that people, people who are not consultants really hate change. Actually, we hate change too. We’re just in the business of inflicting it on other people, but when the shoe is on the other foot and somebody’s advising us, we’re just as bad as all of you. Hi, everyone. Yeah. It’s pretty bad.

BS: Mm-hmm.

BS: That is true. That is entirely true.

SO: So, okay, so we all hate change, right? Change is bad. And change is perceived as being risky, right? Because there’s the thing I’m doing right now, which I know how to do, and I know where the problems are, and I know it’s inefficient, but I know how to get around it. It’s all known. 

BS: Mm-hmm.

SO: And when you walk up to me and say, hey, I found this cool new way of doing content and it’s gonna be awesome and we’re gonna solve all these problems and it’s gonna be so great. My reaction as a human is A, I don’t believe you and B, this sounds like change and change is bad. So what you have to do is you have to convince me that making the change is less risky than not making the change. 

BS: Mm-hmm. Yep.

SO: And, there’s a lot of things you can do to mitigate that, but probably the biggest one is to start small and do like a proof of concept and show some stuff and say, look, you know, we have this ongoing problem and I’ve solved it over here and look at how this just works. And I made this little update and look, it percolated into five different locations automagically and isn’t this cool. So to start to build that confidence and that trust and that knowledge, that understanding of the techniques or the technology or you know the thing that you’re trying to convince people to use to switch to. But the unknown, whatever that unknown is, is always going to be perceived as being riskier than the known, even when the known is bad. Like known bad is actually easier than unknown good.

BS: Mm-hmm.

BS: Mm-hmm, right, because you’re asking people to take a step forward in the dark.

SO: Right. And, you know, the dark is bad and I don’t like it. So now there you get into other issues. We’re talking here mostly about how do you deal with leadership and leadership is looking at it and saying, you know, is the risk worth it? Is the funding worth it? They have X amount of funding, some number. They have one hundred dollars and you’re asking for 50. But there’s eight other people also asking for fifty dollars and they have to pick.

BS: Mm-hmm.

SO: You know, two that are going to get $50 a piece out of their eight projects. So your pitch has to be, you know, you’re competing almost certainly for limited resources within your organization. So it has to be a good pitch. I mean, you have to make a compelling argument and, you know, con -KeyRefs are really cool is not actually a compelling argument.

BS: Yeah, how does it impact the bottom line of the company?

SO: Yeah, how can I fix these issues that we are wrestling with as an organization? We have localization problems, stuff we don’t, you know, we’re not managing our content properly and we get all these problems in localization. We’ve got writing issues, our warnings are not standardized and that’s gotten us into trouble because, you know, we got sued and these two documents didn’t agree with each other and they pointed out the discrepancy and that had real-world implications. We’re having trouble delivering content that complies with the EU directives, the machiner directive or the product documentation directives, because we don’t have enough control over the content that we’re delivering. Those are conversations that need to happen, and underlying that is, and so if we use DITA and we redo reuse and we do this and this and this and we automate our formatting,

BS: Mm-hmm.

SO: We can address these issues, but you have to start with the business problem and not with the feature.

BS: Mm-hmm. Right. Yeah, Conrefs are cool is definitely not a selling point out of the gate.

SO: I mean, it works for me, but you know.

BS: Well, but if you frame it the right way and get the executives on board with the business reasons for moving, you might actually get the executives saying, hey, con refs are cool.

SO: Right, now the big challenge here is that, you know, we’re talking about leadership as this amorphous thing, but it turns out that what’s gonna happen almost certainly is that the priorities change as you go up the line. So your tech com manager has one set of priorities and a vision or a, you know, amount of stuff that they’re looking at. 

BS: Mm-hmm.

SO: And the director above that is looking at something different because tech com is just a part of their responsibilities. And the VP above that, again, so you have to understand what messaging is going to work at every level in the organization. And accordingly, provide the proper message or a message that is going to work. 

BS: Mm-hmm.

SO: So ultimately, this comes down to know your audience, know who you’re talking to and what their priorities are and figure out how, whether and how. I mean, we should start with, does this actually fit into the game plan? I mean, is this the right solution for your organization? If you’re convinced it is, then how do you communicate that in a way that is understandable to your non-interested in content leadership?

BS: And then magic happens.

SO: And then magic happens.

BS: So that’s a big leap from doing a LearningDITA proof of concept course, more or less, to doing an executive pitch. And I know we covered a lot of ground here, and there are a lot of things that we still have not even discussed. But I guess in the interest of time, we do have a lot of resources available to you to start thinking in this direction, being able to put that pitch together, get the data that backs up your position that you do need to move if you are looking for a move into DITA. So we will put a bunch of these resources in the show notes. Sarah, do you have any particular ones in mind you’d want to share?

SO: So I mentioned the Content Ops ROI Calculator, and we’ll get that in there. There’s also a chapter that I wrote called the Business Case for Content Ops, which sort of goes through all of these different factors and the risk management issues. We haven’t really touched on compliance, but that’s another key factor that tends to play into this. That is available both on our site and then, you know, the larger Content Ops book is out there now and available for free. So there’s a whole bunch of interesting stuff in there that might be of use. So we’ll post all of that and links to some of the white papers that are floating around that may be of use to our listeners. And beyond that, if you’re, you know, working on building this case out, I would say feel free to reach out to us and we’ll do the best we can to help.

BS: And that sounds like a good place to close. Thank you, Sarah. And thank you for listening to the content strategy experts podcast brought to you by Scriptorium. For more information, visit scriptorium.com or check the show notes for relevant links.

The post What’s next after LearningDITA? (podcast) appeared first on Scriptorium.

In episode 162 of The Content Strategy Experts Podcast, Bill Swallow and Christine Cuellar discuss the benefits of single sourcing as part of your content strategy through the example of two things they love: coffee and beer.

“We know companies that have moved away from a do-it-yourself approach because they had maybe two or three different people putting in half to almost full-time work on the publishing system and not on other facets of the company’s core business or the writing. They were simply there to keep everything working. It just blows my mind that on a scale where you have hundreds of writers contributing content, you are saying, Okay, you three people are going to be solely responsible for keeping this thing up and running so that they can produce their content, rather than having a system that’s designed to keep itself up and running.”

— Bill Swallow

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Transcript:

Disclaimer: This is a machine-generated transcript with edits.

Christine Cuellar: Welcome to the Content Strategy Experts Podcast brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage, structure, organize, and distribute content in an efficient way. In this episode, we’re talking about how you can brew a better content strategy through single sourcing. Hi, I’m Christine Cuellar.

Bill Swallow: and I’m Bill Swallow.

CC: Hey, Bill, thanks for being here today. 

BS: Hey, thanks.

CC: So what I mean by brewing a better content strategy is that both Bill and I really love coffee. Right now for both of us, we’re recording it fairly early times in the morning. So actually we’re heavily reliant on coffee and other caffeinated sources to enable this conversation. Also, Bill, I know you like homebrewing beer. I like drinking beer. I have no idea how to homebrew, but I do enjoy beer as well. So we just thought that beer, coffee, drinks in general actually have some good analogies for single sourcing, which can be part of your content strategy. And it’s something that’s been coming up more and more in a lot of conversations with clients and people that are interested in content strategy so we thought this would be a good topic for today. So Bill I’m gonna kick it over to you for our first really big-picture question. First of all what is single sourcing? What do we mean when we say that? Let’s kick it off there.

BS: All right, so in a nutshell, single sourcing is writing content once for multiple purposes. It’s about as simple as you can get. It could be authoring centrally, it could be authoring collectively in a group or centrally as a single person for a wide variety of publishing needs, whether it be for different audiences, different output types, or what have you.

CC: Okay, yeah, that’s great. So how does, what are some ways that single sourcing can start to mimic drinks? Coffee, beer, any of that?

BS: We could take the example of multiple output formats. So traditionally with single sourcing, we’ve been doing that since I think the mid-90s. I remember working in. Oh, that’s based in the name. I remember working in Doc to help back in, I think it was 1996, to produce online help and written manuals from the same source using a very high-tech convention called RTF, which is basically the backbone of Microsoft Word at the time.

CC: Ooh.

BS: So that was fun. I had many nightmares about RTF coding. I solved problems in my dreams using RTF. It was a scary time. Yeah, I was essentially fully immersed, let’s say that. But in many ways, to take the same analogy, you’re producing a wide variety of, or you’re producing a core set.

CC: That’s when you know it’s really stressful. Yeah, that’s not good.

BS: of stuff that needs to go to many different places. And it’s a lot like, let’s say a coffee roaster since it’s early in the morning and we want to talk about coffee. A coffee roaster is going not going to sit there and roast a pound of beans, put it in a bag, and then send it off, and then roast a pound of beans, put it in a bag, send it off. You know, they’re going to roast, you know, a ton of beans, 10 tons of beans, however, you know, many that they can fit into their roaster.

CC: Yep.

BS: and do it all at once. A couple of things that allow them to do, one, it streamlines the process and speeds things up because now they have a wealth of products that they can then put in large bags for distribution to restaurants or cafes or what have you. They can put it in smaller bags and send it out to the grocery stores. They can do their online mail orders for coffee that way. Five-pound bags, one-pound bags what have you or they can even grind it up themselves put it in k-pods and people can destroy the planet with those I’m not a big fan of those the k-cups and pods but It also helps them create a more homogenous product because they’re working at a very large scale So and they’re they’re producing things in very large batches So with all their beans together in one roaster, they are able to produce a very consistent product that way.

CC: Yeah, that’s great. That’s a great analogy and that definitely makes a lot of sense. So when it comes to both coffee and beer, there’s a, you know, the commercial option that you just outlined, which is really helpful. And there’s also usually a DIY component. I mean, you can home brew beer, you can home brew coffee, of course, and do that in a bunch of different ways. Like it can just be your coffee pot or you can get all fancy and do, you know, all the little like other fancy things you can do with it, all of which I’ve done and they’re all leaving my brain at this moment. French roast, okay, that’s one. Anyways, it should have been more top of mind. But is that also an option for content?

BS: It can be. Looking at a commercial solution versus a DIY or DIY approach, it’s not so much a question of which approach do you prefer to take. Because I mean, yes, I’m a hobbyist when it comes to brewing beer. But I still, and really for the past 10 years, I stopped brewing because there were just so many high-quality

CC: Okay.

BS: Options on the market at that point. I’m like, why am I spending my time doing this when I can just go to the store and pick up? one of a thousand different types of beer but you really need to look at it from the standpoint of how much money do you have to spend on a commercial product versus how much time and commitment do you have to doing it yourself if you do it yourself. 

CC: Yeah.

BS: The results can vary, but if you put the time and energy into it, you can produce some amazing results, but there is always a hidden cost of time and labor. When I used to actively brew, I brewed with a buddy of mine and we would do it every Monday night. So he would either come to my place with his equipment or I would go to his place with my equipment. And from like six o ‘clock until about midnight, we would be either brewing beer, cleaning equipment, bottling beer, doing whatever. It was a commitment. I mean, it was six hours a week, and literally it was every week. Unless we had something going on and we took a bye week, we were doing that every single week because there is always something that needs to be done in the process.

CC: Hmm. Yeah. Yeah, that’s a big time commitment. And I like that you mentioned that not only was there a big time commitment in actually brewing the beer, but also the cleanup, also the prep work. Yeah, I like that there’s this there’s other factors that you don’t think about that also are involved in doing it yourself? Is that also something that applies to, you know, single sourcing and content strategy? Are there a lot of factors that can come into play?

BS: Oh, absolutely. I mean, if you’re a homebrewer, you have to enjoy the monotony of cleaning. And it’s the same thing if you’re doing it yourself with putting together a publishing system and an authoring system that relies on, let’s say, open source tools and a lot of human care and feeding. You have to really enjoy the monotonous. 

CC: Hmm.

BS: Droning kind of day-to-day maintenance work. You know, when you’re brewing, it’s, it literally is 90% cleaning, 10% brewing. Because I mean, you start, you know, you have to have everything completely sanitized. And once you get the pot boiling, you know, it’s, it’s doing its thing for about an hour. You know, you might be adding, you know, some hops here and there, or some other flavoring agents, depending on the type of beer you’re producing. 

CC: Wow.

BS: But, largely, you’re just waiting for an hour. So while you’re waiting, you’re cleaning other stuff that you’re gonna need later in the process. And then you take five minutes to move to that next step, and then you have to wait for the beer to cool down. So then there’s another round of cleaning. Okay, all the stuff that I used to make this batch of beer now needs to get cleaned. And then you go to put it into the fermenter, and now you have to clean everything else. And the cycle just continues.

CC: Yeah, oh wow.

BS: It’s the same thing with these with, you know, with a do-it-yourself approach. And it’s not to say that it’s wrong or that it’s not ideal, because you can learn quite a lot in a do-it-yourself environment. But it does come at a cost. You know, you’re going to spend I actually we know companies that have, you know, moved away from a do-it-yourself response, because they had, you know, maybe two or three different people putting in half to almost full-time work on the publishing system and not on other facets of the company’s core business or the writing or what have you. They were simply there to kind of keep everything working. And it just blows my mind that on a scale where you have hundreds of writers contributing content that you are saying, okay, you three people are going to be solely responsible for keeping this thing up and running so that they can produce their content rather than having a system that’s designed to keep itself up and running.

CC: Yeah. Would you say that because it sounds like with a DIY approach, it can work, but it has to be very intentional and you have to be very realistic, like you said, about the cost and the time that’s involved. And I can see from like a coffee analogy. Do companies, I guess, default or kind of slide into a DIY approach without really thinking about it. Because I could see with like coffee, I do love enjoy, okay, with coffee, I do enjoy attempting to make lattes and, you fun stuff with my espresso machine, which I have like a really crappy one right now, but it’s really fun to play with. And I’ve practiced a lot with it. But still, the best cup that I make does not compare to like, basically every one of our local coffee shops here, I would 100 %… enjoy their stuff more than what I make. It’s just fun to play with. But it’s not realistic for me to go and buy, you know, the best coffee from one of the local places every single day. So instead, I have a coffee machine and instead I brew stuff here at home, you know, every day for my regular coffee addiction. And then when I want to be fancy, I go to a coffee shop. But just, you know, I don’t have the capacity to go somewhere else every single day. So that’s kind of why I’ve just

BS: Yeah.

CC: Not really thinking about it, slid into a DIY approach. Is that also something that happens with companies that they kind of DIY until they realize there is a different way to do it? Is it kind of a default method if that makes sense?

BS: Yes and no. And the decision as to why a company might choose to do it themselves rather than purchase a more packaged or commercial solution. It really varies. You know, you have some companies that, yes, they started out small. They hired someone perhaps who had some serious technical chops and was able to put together something very, very, very slick.

CC: Okay.

BS: But you know, they were the only ones who really knew how it worked. And as they hired more writers, you have varying degrees of varying degrees of, I guess, capability and willingness to learn how this thing works.

CC: Mmm.

BS: You know, so if, you know, for example, let’s say that they’re doing Markdown and they have all of these, you know, different scripts that run and fire off and they produce, you know, all these different outputs. It’s very, very slick. I’ve seen lots of implementations like that and that, you know, they’re actually pretty cool. But, you know, as you hire more people,

CC: Hmm. Oh yeah, that’s true.

BS: You start getting into, why do I have to write and mark down? I always keep forgetting to use this character instead of this character when starting a bulleted list. Or I always forget to, you know, close off the end of my, you know, my title or what have you, anything like that. You know, why can’t I use Microsoft Word? Why can’t we move to just using HTML? Why can’t we move to XML? Like, you know, you start getting a lot of that pushback and the pushback may not be direct.

CC: Mm.

BS: So you have cases at that point where you have quality slips starting to make their way into the core content set. And that’s where things get a little hairy. But to go back to your analogy, making coffee at home, you can have, there are plenty of really, really good espresso machines out there that you can buy for home, but it will never compete with that $8,000 Italian espresso maker that your cafe, you know, your choice, you know, the cafe has in town. You know that they, you know, they paid a ton of money for and they’ve spent hours and hours and dollars and dollars to train their staff on how to appropriately use it and clean it to produce that same, you know, questionably but perfect cup of coffee every single time.

CC: Yeah.

BS: You know, same thing with buying coffee, you know, buying beans or buying grounds. You know, these companies you buy and you know, people will laugh. I make the same comments about, you know, certain beer manufacturers. But, you know, you buy something like Folgers. It’s not, you know, in my opinion, it’s not the world’s best coffee. You know, I just don’t like, you know, what it tastes like.

CC: Yes.

BS: But every single time you buy a, they’re not tins anymore, are they? I think they’re more like plastic jugs of coffee. But you buy a jug of coffee and it’s always gonna be the same every single time. And you can say the same thing about Budweiser. People may say, oh, Budweiser, why would you ever drink that? It’s horrible. It’s like, yes, but it is absolutely consistent. You can buy a Budweiser anywhere in the United States, in the world.

CC: They’ve evolved. Yeah, yeah, yeah.

BS: Open it up and it will taste exactly the same.

CC: That’s true. Yeah, that’s true.

BS: You know, there are really no differences there. And they spend quite a lot of time and energy into ensuring that that product is consistent from every single batch that’s made in every single location across the world because they have breweries all across the world that produce this stuff because shipping it from one location around the world is just not gonna work. So all of these different locations have their equipment set up just the right way. Their chemists work, yes chemists, their chemists are working to make sure that the pH balance is perfect every step along the way as that beer is being produced. So otherwise, if you’re brewing yourself at home, your equipment may vary. I’ve put stuff together literally with duct tape and string.

CC: Hmm.

BS: I made it, I made a shower head out of a nine-inch tin foil pie pan 

CC: Hahaha! Wow. Yeah, that’s a DIY way.

BS: Because, it was available, you know, to sparge or to clean my grain as it was, being run off as the beer was being run through. Or, you know, even if you roast your own beans at home, you know, the level of quality is going to vary, you know, because you are likely using your, your oven to do that roasting. And if you step away for a minute too long, or if you didn’t get the temperature setting quite right, so if you don’t have a digital temperature setting, or maybe your heating element is a little futsy, so sometimes it might be 310 degrees, sometimes it might be 332, who knows? There are lots of elements that can go wrong in a do-it-yourself environment.

CC: Yeah, that’s true. And like you mentioned earlier that a lot of that comes down to the people, not only the equipment that you’re using, but also the people. Like, do they know what they’re doing? Do they know why they’re doing it? And especially as you introduce more people, like you mentioned, if it’s you and your buddy that are brewing beer together, that’s another person that’s been added. And, you know, in a scenario where one person’s not as interested or, you know, just doesn’t know as much about the process that can really change things and vice versa. Like if you have two people that both really know what they’re doing and both really enjoy it, that can lead to a really good output. 

BS: It can vary because yes, we both knew exactly what we were doing and But you know you start biting heads. I want to do it this way No, I want to do it this way if we do it this way. You’re gonna get this result I don’t believe you I think if we do it this way, we’ll get this result and yeah, we’ve had You know, we actually tried it and tried two different techniques of brewing the same beer and they came out very very different so

CC: That’s true, yeah.

BS: You know, it is what it is. But yeah, it all comes down to that quality control element, you know, and, you know, generally when you have a bigger commercial system, you can kind of get there a lot quicker. Now, it’s not going to do everything for you, but it’s got the pieces already laid out and it’s got some recommended workflows and processes for using that system to produce consistent results. Whereas with a do-it-yourself, you’re kind of left at your own devices and how well you document your stuff and how well you regulate it.

CC: Absolutely. Yeah, which can is it in and of itself is another time commitment. Yeah. So we’ve talked a lot about consistency, which is a really important element of this, but also personalization is another important value that you can get out of single sourcing. How does that? So let’s say if we put it in our coffee or beer analogy, let’s say you’re personalizing your packaging for, you know, different restaurants and different cafes or whatever. How does single sourcing make that more effective or what does that look like?

BS: Well, at the core of it, like for example, if you’re putting stuff out to cafes and restaurants, you’re typically not going to use the same level of pomp and flash on your branding packaging that you would if it was going to a grocery store. Because you want that product to pop off the shelf in the grocery store and catch people’s eyes, whereas at the restaurants and so forth, as long as the logo’s on the bag so they know they got the right thing.

CC: Yeah.

BS: It’s usually just a pretty nondescript bag with a description of what’s inside it. But in the end of the day, you’re not producing different product for these different groups. You’re producing the same product that’s going out to many different people, depending on who it needs to go to. So you may have one conveyor belt that takes the beans down to where they dump them into 25-pound bags or 50-pound bags. And then you have this other conveyor belt that goes off and does the one-pounders.

CC: Mm-hmm.

BS: And so it’s really streamlining from that. You’ve spent the time to build this, I guess, storage heap of beans that you then are distributing to many different people. So at that point, you’re taking from that same source and you’re partitioning it off as you need to for multiple different consumers.

CC: Mm. That’s true.

BS: Same thing with single sourcing. I mean, you have a core collective of content that ideally is all written in the same tone and voice. Aside from all the mechanics of how content gets produced, it needs to be written in the same tone and voice for…

to be able to blend and remix and be able to send it out to different audiences so that it doesn’t sound like, you know, eight different people, even though eight different people may have written the content, it doesn’t sound like eight different people wrote different parts of whatever it is you’re delivering. It’s a little jarring to go from…

CC: Hmm. Yeah.

BS: You know, one style of writing to another within the same paragraph or within the same, you know, chapter of a book or, you know, series of topics in an online help system. It can get very distracting. So, you know, in that case, you do need some attention toward how all these people are developing the content and what tone and voice they’re using. But aside from that, with regard to packing your…

CC: Yeah.

BS: You know, packaging your output from a content standpoint, you have things like, you know, templates that drive the look and feel of what the various outputs are going to look like. So templates or, you know, style sheets or what have you to produce these things. But also behind the scenes, you have other conventions such as variables, conditions. Perhaps you’re leveraging some form of reuse. So that you can kind of mix and match your content, turn things on and off depending on, oh, this is going out to, you know, an advanced user or this is going out for our, this is going out for our premium product. And this one’s going out for our base-level product. And base level product has features A, B, and C, but our premium has features D and E also tacked on. That type of thing. So you’re not rewriting content for these.

CC: Mm.

BS: You know, very many different outputs, but rather you are pulling from a single, you know, managed source of content and, you know, mixing and remixing and turning things on and off to produce that desired result.

CC: Yeah, absolutely. And then so taking that a step further, when you when it’s time to start selling your coffee or selling your beer in a location in a different country or different region, what happens then in that localization process? I mean, I’m assuming all of that is involved plus more. Yeah.

BS: Oh, plus more, because then you have language on the packaging and so forth that needs to change. But more importantly, with any kind of food-based product, and particularly with alcohol, there are different rules that govern how things can be sold, what you can say, what you can’t say on the packaging. We’re pretty loosey-goosey here in the United States where you can say anything. You can put out a package that is the same size product and say now 20% more. And you look, it was a 16-ounce box before, it’s a 16-ounce box now, but now it says 20% more.

CC: What? Maybe they meant air, 20% more air in the package.

BS: I guess, I guess, but you start going overseas and the nutrition labels need to change. You have to take very different stances when you’re listing ingredients. There are certain claims you can and cannot make on the packaging and in the advertising. And when it comes to alcohol, particularly, there are different rules that govern what can go into it that can be then passed off to a consumer and what you have to disclose and what you can’t disclose. And I go back to one of these things, and it’s not so much a governing rule anymore as far as you know how strict it is but there’s the Reinheitsgebot I hope I am pronouncing that right but it’s basically the German purity rule for beer and it basically governs and says that beer can only be made of three components water barley and hops and they omitted yeast even though yeast is what does the fermenting process because at the time they created the law, they didn’t really know about it. But yeah, essentially those four ingredients are the only things that can go into beer for Germany Not so much a rule anymore, but it’s it’s it’s an example of you know, if you were to produce something And call it something. Yes, if you were to produce beer and call it beer, but you’re making beer with Barley and corn and rice, you know, so something that let’s say Budweiser does. Would that technically be beer? Maybe not in Germany. So what do you call it? How do you package it? Can you sell it? Again, it’s not so much a thing anymore. It’s more of a historic note, but it kind of shows the differences in what you can do and what you can say in different countries. 

CC: Oh, okay, interesting.

BS: Likewise, when you go to different, when you publish for different locales, you have not only different languages, but you have different fonts that you have to consider. You have different complete character sets, you know, so, you know, there’s, you know, the more the Latin character set that we use throughout the United States and throughout Western Europe. We start going more into Eastern Europe and you start getting into needing to use a Cyrillic alphabet. 

CC: Mm-hmm.

BS: Certainly you move into Asia and now you’re starting to look into, oh, I’m going to need you know a completely different character set a double bite character set to put these things together and You know in some places you’re gonna have to change the complete layout of your content as it as it gets published because you know certain languages they go from right to left, not left to right, so that’s a completely different change and you know a lot of that you bake you hopefully are baking into the infrastructure that you are driving your content production with and not doing this by hand every time you need to send something out.

CC: Yeah, I can’t even imagine. Yeah, that would be a lot. Well, so for organizations that may not have adopted this single-sourcing approach yet, what are some factors? I mean, we’ve talked about a lot of them, but what are some either factors or like pain points or experiences they may be having that signal, hey, maybe it’s time to start thinking about this? How would you sum up those indicators?

BS: I think the biggest indicator is that you have a very overworked team of people who are spending their time on everything but their core job. So their core job should be producing content, developing content. It should not be formatting and reformatting content to produce it.

CC: Hmm. Yeah.

BS: You know, it certainly should not be copying and pasting content from one place to another and then making sure that any change to that copy and pasted content is reflected in the two or eight or 16 or 150 different places they pasted it into last time. You know, that’s a lot of busy work and you know, a lot of things that I hear, especially from small teams, is that they reach a point where they are so busy.

CC: Yeah.

BS: And making so little progress on new content development because they are spending all their time, you know, prepping for publishing, prepping for publishing, you know, prepping for publishing literally should be content is done. And that should be your prep for publishing. It shouldn’t be, okay, now let’s apply this template and let’s reformat everything. And now let’s send it off to the translator and oh, we got it back. Okay, now we have to reformat it so that it fits in this language because German is now, you know, eight pages instead of five. You know, it shouldn’t be fixing these things. Those are things that really should be handled automatically and, you know, allow the content developers to do what they were hired to do, which is develop the content.

CC: Yeah, exactly. Yeah, allow them to be able to do what not only you hire them to do, but I’m thinking that they’re more passionate about. That’s where their passion is. That’s why they’re here. I could see that being very discouraging if you’re passionate about the content and you spend almost all your time on formatting and other stuff. That sounds awful. And it would be discouraging. And I’m assuming leads to burnout, lead to, you know, high turnover.

BS: Yeah.

CC: Because you’re not getting to do what you want. You want to write content.

BS: True, although some people do thrive in that environment and they love that they love the fiddly bits, you know, and, you know, you’re not going to make them happy by taking that away. But then again, it’s like, you know, as you know, your company is growing, you’re producing more stuff, you need to produce more content, you need to do it quicker, you need to do it at a higher quality. You know, you’re you’re publishing at a higher volume, you’re adding more languages. You know, at that point, it’s like, do I keep that person happy?

CC: Oh, yeah.

BS: Or do I focus on what we need to get done?

CC: Yeah, fair. And maybe they can have some say or you can include them in what’s what the big vision is. But yeah, like you said, that you can’t always just make one person happy with the system. There’s all these other people that may also not be happy because of, you know, not having an efficient process and a way to pump out a lot of content at scale in a way that’s still quality.

BS: Mm-hmm.

CC: Still consistent. Yeah.

BS: Yeah, and there is a risk there as well because those who put together the DIY approach, they may love that. You know, I mean, that that’s something that they built from the ground up. That’s their baby, you know, and you’re taking their baby away. That can lead to some big problems. 

CC: Yeah, makes sense.

BS: You know, either you lose that person who has all the publishing knowledge, even though you may be transitioning away from that system, they kind of know how it was set up and they know they know where the I hate to use the analogy, but they know where the bodies are buried in their infrastructure, and what made it tick. And you don’t want to lose that knowledge. Instead, you want to try to hopefully work with them to stand up the new one and give them some governance over how that runs. That might be an approach. But it does get tricky.

CC: Yeah, it makes sense because at the end of the day, it’s still about people. The people that you’re working with, you wanna make sure that they’re, the people on the team that are creating the content, it’s still about them, it’s still about the people at the other end of the screen or book or whatever, whatever kind of content you’re writing. Yeah, it’s still about people and people are complicated. We are.

BS: That’s putting it lightly.

CC: Yeah, that’s my deep wisdom for the day. That’s what comes from five cups of coffee in the morning. And on that note, I think we have exhausted every part of this beverage analogy for single sourcing and content strategy, but it was really helpful even for me to hear. I mean, I knew some of this, but there was a lot of this that I hadn’t thought of in terms of something very tangible like drinking coffee or drinking beer. So thanks Bill for exploring this with me. I also just love talking about coffee anytime it’s possible. So yeah, it’s great.

BS: It was fun.

CC: Well, yeah, thank you so much for being here and thank you for listening to the content strategy experts podcast brought to you by Scriptorium. For more information, visit scriptorium .com or check the show notes for relevant links.

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