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In-Ear Insights: What is AI Decisioning?
In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI decisioning, the latest buzzword confusing marketers.
You will learn the true meaning of AI decisioning and the crucial difference between classical AI and generative AI for making sound business choices. You’ll discover when AI is an invaluable asset for decision support and when relying on it fully can lead to costly mistakes. You’ll gain practical strategies, including the 5P framework and key questions, to confidently evaluate AI decisioning software and vendors. You will also consider whether building your own AI solution could be a more effective path for your organization. Watch now to make smarter, data-driven decisions about adopting AI in your business!
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-what-is-ai-decisioning.mp3
Download the MP3 audio here.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
**Christopher S. Penn – 00:00**
In this week’s In-Ear Insights, let’s talk about a topic that is both old and new. This is decision optimization or decision planning, or the latest buzzword term AI decisioning. Katie, you are the one who brought this topic to the table. What the heck is this? Is this just more expensive consulting speak? What’s going on here?
**Katie Robbert – 00:23**
Well, to set the context, I’m actually doing a panel for the Martech organization on Wednesday, September 17, about how AI decisioning will change our marketing. There are a lot of questions we’ll be going over, but the first question that all of the panelists will be asked is, what is AI decisioning? I’ll be honest, Chris, it was not a term I had heard prior to being asked to do this panel. But, I am the worst at keeping up with trends and buzzwords.
When I did a little bit of research, I just kind of rolled my eyes and I was like, oh, so basically it’s the act of using AI to optimize the way in which decisions are made. Sort of. It’s exactly what it sounds like.
**Katie Robbert – 01:12**
But it’s also, I think, to your point, it’s a consultant word to make things sound more expensive than they should because people love to do that. So at a high level, it’s sticking a bunch of automated processes together to help support the act of making business decisions. I’m sure that there are companies that are fully comfortable with taking your data and letting their software take over all of your decisions without human intervention, which I could rant about for a very long time.
When I asked you this question last week, Chris, what is AI decisioning? You gave me a few different definitions. So why don’t you run through your understanding of AI decisioning?
**Christopher S. Penn – 02:07**
The big one comes from our friends at IBM. IBM used to have this platform called IBM Decision Optimization. I don’t actually know if it still exists or not, but it predated generative AI by about 10 years. IBM’s take on it, because they were using classical AI, was: decision optimization is the use of AI to improve or validate decisions.
The way they would do this was you take a bunch of quantitative data, put it into a system, and it basically would run a lot of binary tree classification. If this, then that—if this, then that—to try and come out with, okay, what’s the best decision to make here? That correlates to the outcome you care about. So that was classic AI decisioning from 2010-2020. Really, 2010-2020.
**Christopher S. Penn – 03:06**
Now everybody and their cousin is throwing this stuff at tools like ChatGPT and stuff like that. Boy, do I have some opinions about that—about why that’s not necessarily a great idea.
**Katie Robbert – 03:19**
What I like—the description you gave, the logical flow of “if this, then that”—is the way I understand AI decisioning to work. It should be a series of almost like a choose-your-own-adventure points: if this happens, go here; if this happens, go here. That’s the way I think about AI-assisted. I’m going to keep using the word assisted because I don’t think it should ever take over human decisioning. But that’s one person’s opinion. But I like that very binary “if this, then that” flow.
So that’s the way you and I agree it should be used. Let’s talk about the way it’s actually being used and the pros and cons of what the reality is today of AI decisioning.
**Christopher S. Penn – 04:12**
The way it’s being used or the way people want to use it is to fully outsource the decision-making to say, “AI, go and do this stuff for me and tell me when it’s done.” There are cases where that’s appropriate. We have an entire framework called the TRIPS framework, which is part of the new AI strategy course that you can get at TrustInsights AI strategy course. Katie teaches the TRIPS framework: Time, Repetitiveness, Importance, Pain, and Sufficient Data.
What’s weird about TRIPS that throws people off is that the “I” for importance means the less important a task is, the better a fit it is for AI—which fits perfectly into AI decisioning. Do you want to hand off completely a really important decision to AI? No. Do you want to hand off unimportant decisions to AI? Yes. The consequences for getting it wrong are so much lower.
**Christopher S. Penn – 05:05**
Imagine you had a GPT you built that said, “Where do we want to order lunch from today?” It has 10 choices, runs, and spits out an answer. If it gives you a wrong answer—wrong answer out of 10 places you generally like—you’re not going to be hugely upset. That is a great example of AI decisioning, where you’re just hanging out saying, “I don’t care, just make a decision. I don’t even care—we all know the places are all good.” But would you say, “Let’s hand off our go-to-market strategy for our flagship product line”? God, I hope not.
**Katie Robbert – 05:46**
It’s funny you say that because this morning I was using Gemini to create a go-to-market strategy for our flagship product line. However, with the huge caveat that I was not using generative AI to make decisions—I was using it to organize the existing data we already have.
Our sales playbook, our ICPs, all the different products—giving generative AI the context that we’re a small sales and marketing team. Every tactic we take needs to be really thoughtful, strategic, and impactful. We can’t do everything. So I was using it in that sense, but I wasn’t saying, “Okay, now you go ahead and execute a non-human-reviewed go-to-market strategy, and I’m going to measure you on the success of it.” That is absolutely not how I was using it.
**Katie Robbert – 06:46**
It was more of—I think the use case you would probably put that under is either summarization first and then synthesis next, but never decisioning.
**Christopher S. Penn – 07:00**
Yeah, and where this new crop of AI decisioning is going to run into trouble is the very nature of large language models—LLMs. They are language tools, they’re really good at language. So a lot of the qualitative stuff around decisions—like how something makes you feel or how words are used—yes, that is 100% where you should be using AI.
However, most decision optimization software—like the IBM Decision Optimization Project product—requires quantitative data. It requires an outcome to do regression analysis against. Behind the scenes, a lot of these tools take categorical data—like topics on your blog, for example—and reduce that to numbers so they can do binary classification. They figure out “if this, then that; if this, then that” and come up with the decision. Language models can’t do that because that’s math.
So if you are just blanket handing off decisioning to a tool like ChatGPT, it will imitate doing the math, but it will not do the math. So you will end up with decisions that are basically hallucinations.
**Katie Robbert – 08:15**
For those software companies promoting their tools to be AI decision tools or AI decisioning tools—whatever the buzz term is—what is the caution for the buyer, for the end user? What are the things we should be asking and looking for? Just as Chris mentioned, we have the new AI strategy course. One of the tools in the AI strategy course—or just the toolkit itself, if you want that at a lower cost—is the AI Vendor cheat sheet. It contains all the questions you should be asking AI vendors.
But Chris, if someone doesn’t know where to start and their CMO or COO is saying, “Hey, this tool has AI decisioning in it, look how much we can hand over.” What are the things we should be looking for, and what should we never do?
**Christopher S. Penn – 09:16**
First things I would ask are: “Show me your system map. Show me your system architecture map.” It should be high level enough that they don’t worry about giving away their proprietary secret sauce. But if the system map is just a big black box on a sheet of paper—no good.
Show me how the system works: how do you handle qualitative data? How do you handle quantitative data? How do you blend the two together? What are broadly the algorithm families involved? At some point, you should probably have binary classification trees in there. At some point, you should have regression analysis, like gradient boosting, in there. Those would be the technical terms I’d be looking for in a system map for decisioning software. Let me talk to an engineer without a salesperson present. That’s my favorite.
**Christopher S. Penn – 10:05**
And if a company says, “No, no, we can’t do”—clearly, then, there’s a problem because I know I’m going to ask the engineer something that “doesn’t do that.” What are you talking about? That is always the red flag for me. If you will not let me talk to an actual engineer with no salesperson present—no minder or keeper present—then, yeah, you’re not doing the right things.
The thing to not do is the common-sense thing, which is: don’t sign for a system until you’ve had a chance to evaluate. If you don’t know how to evaluate a system like that, ask for help. Ask: you can join our free Slack group. Go to analytics for Marketers, Trust Insights, AI analytics for Marketers.
**Christopher S. Penn – 10:51**
You can ask questions in there of all of us, like, “Hey, has anyone heard of this software?” We had someone share a piece of software last week in the chat, and people said, “What do you think about this?” I offered my opinion, which is: “Hey, this is going to be gathering very personal data, and their data protection clauses in their terms of service are really not strong.” So perhaps don’t use the software.
Of course, if something you want to have handled privately, you’re always welcome to work with Trust Insights. We will help you do these evaluations. That’s what we’re really good at. But those would be my things. The other big thing, Katie, I would ask you as the people person is—
**Christopher S. Penn – 11:33**
How do you know when a salesperson or a company rep is just bullshitting you?
**Katie Robbert – 11:40**
I get asked that question a lot, and there’s definitely an art to it. But the most simple response to that is: Can they give you direct answers, or not? Do they actually respond with, “I don’t know, but let me look into that for you”? Some people are really bad at BSing, so they’ll kind of talk in circles and never really get to the point and answer your question. So that’s an obvious tell.
There are a lot of people who are very good at BSing and do it with confidence, making you feel like, “Oh, well, they must be telling the truth.” Look how authoritative they are in their answer.
**Katie Robbert – 12:26**
So it’s on you—the end user, the potential buyer—to come ready with the list of questions that are important to you. I think that’s really the thing: they might be BSing everybody else. Great, let them. That’s not your problem. Your main focus is what is important to you.
Believe it or not, it’s going to start with getting your thoughts organized. The best way to do that is with the 5P framework. So, if you’re looking at AI decisioning software: What is the purpose? Why do we think we need AI decisioning software? What problem is it solving if we have AI decisioning software? That’s one of the first questions you ask the software vendors: “This is the problem I’m looking to solve. Talk to me about how you solve that problem and give me examples of how you solved that problem with other people.”
**Katie Robbert – 13:24**
And it’s okay to ask for references too. So you can say, “Hey, can I contact your other customers and talk to them about their experience using your software?” That’s a great way to cut through the BS. If they say, “No, we can’t do that”—that’s a huge red flag—because they want to sell as much product as possible. If they’re not willing to, or if there are NDAs in place, or whatever it is, they need to be able to explain why you can’t talk to their other customers who they’ve solved the same problem for.
Next is People. Think about it internally and externally. Internally: who’s using this software, who’s setting it up, who’s maintaining it, who’s accepting the outcomes, who’s doing the QA on it? Externally, from their side: who is your support system? Do they have 24/7 support?
**Katie Robbert – 14:19**
Is there a software license agreement you would need to sign to get support? Or are they just going to throw you to a cycle of never-ending chatbots that keep pointing you back to their FAQs and don’t actually answer your question?
Third is Process. How are we integrating this system into our existing tech stack? What does it look like to disrupt the existing tech stack with new software that takes in data? Does it take in our existing data? Do we have to do something different? Basically, outlining the different data formats and the systems you have for the sales rep, and saying, “This is what we have. Will your AI decisioning software fit within our existing process?”
This leads into Platform. These are the tools in our tech stack. Is there a natural integration, or will we have to set up external third-party integrations? Do we have to develop against APIs to get the data in, to get the data out? Those are not overly technical questions. Those are questions anyone should be able to answer, and that you should be able to understand the response to.
Lastly is Performance. How do we know this solved a problem? If your purpose for bringing in AI decisioning is efficiency or increased sales—that’s the metric you need to hold this piece of software to.
**Katie Robbert – 15:51**
Then ask the sales guy: “Let’s say we do a trial run of your software and it doesn’t do what it needs to do. How do you back your system out of our tech stack? How do you extract our data from your cloud servers? How do you just go away and pretend this never happened? What’s your money-back guarantee for performance?”
Those are basic, high-level questions. So use the 5P’s to get yourself organized. But those are the questions you should be asking any software vendor—AI or otherwise. But with AI decisioning—where the tool is meant to take the decisions out of your hands and do it for you—you want to make sure—100% sure—that you are confident in the decisions it’s making.
**Christopher S. Penn – 16:40**
One of the best things you can do—and we’ve covered this on previous Trust Insights Live Streams—is looking at qualitative data that exists on the internet from places like G2 Crowd, Capterra, Reddit, et cetera, and looking at the reviews for the software. For example, this is one company I know that makes decisioning software. We’re not going to share the name here, but when I looked at their reviews on Capterra, one of the reviews said it’s very expensive, it’s tricky to implement—and this was a big one.
The company regularly updates their software, but their updates do not align with our organizational needs. So the software drifts out of alignment and makes changes to decisioning software that we did not request.
**Katie Robbert – 17:30**
That’s a huge problem.
**Christopher S. Penn – 17:31**
That’s a real big problem. So if someone is out there on stage talking about their company’s AI decisioning software, and you look at the reviews, you might say, “It seems some of your customers say the decision-making process for how you do change management needs a little upgrade there, buddy.”
**Katie Robbert – 17:52**
Again, it’s not unreasonable to ask for referrals. Especially now, where there are so many software vendors to choose from—think about it like real estate, it’s a buyer’s market. You have no shortage of options. So how do you make the best decisions? One of those ways is talking to other people who have tried the software, left a review, or purchased the software and locked into a three-year agreement.
Ask if you can talk to them and get their opinions of how it went; how was the implementation; how is the support? In terms—you know, Chris, to your point—how often is the company making updates, and how well are they at not only communicating the updates, but what does it break? Because the sales team of the software, they’re going to tell you, “Here’s my talking points. Don’t go off script. I have a commission I need to meet for Q4.” So once they sell, it’s out of their hands. That’s now development and customer support’s problem.
**Christopher S. Penn – 19:13**
One of the things I would recommend people do—and this goes right along with the 5P’s—is, after you’ve documented how you currently make decisions and what you want the system to do. Set up a deep research project—or several, if it’s a big-ticket expense—and have generative AI build you the short list of. See, here are the companies that meet this criteria. Here’s how we make decisions: we have this data; we want to do it like this. Give it a prompt.
Something along the lines of, “You’re going to build a short list of companies that make AI decisioning software that meets these criteria, that is at this rough price point or range you’re willing to spend. These are the outcomes we’re looking for.”
**Christopher S. Penn – 19:58**
You should use review sites like G2 Crowd and Capterra, discussion forums like Reddit, and customer service messages—all to identify which platform is the best fit for our criteria. Create a list in descending order by goodness of fit, and make sure the software and the company have made substantial updates to their software in the last 365 days. Today’s date is whatever. Put that in as a generative AI deep research prompt. Put it in ChatGPT, put it in Gemini, put it in Perplexity.
Get a few different reports, merge them together, and see which vendors make the cut—which vendors are the best fit for your company for what’s going to be a very big, very expensive, and very painful process. Because decisioning software is big and painful. You will be surprised.
**Christopher S. Penn – 20:51**
When you go into that sales call, to your point, Katie, when the sales guy is trying to make his commission, you can say, “Here’s the criteria. Here’s what AI research came up with. Tell me what here is true and what is not.” Or even better, have generative AI build the list of questions for the salesperson so you can really dig down to the specifics.
And I guarantee that the first response for half the questions will be, “I need to check with our sales engineer on that.” You can say, “Great, why don’t you go ahead and do that?” Their incentive is not to help you succeed.
**Katie Robbert – 21:39**
And here’s the thing: This is not a knock at AI decisioning software. What we’re trying to do is make sure that you—the end user, the buyer—go into the process with both eyes open and that you’re fully prepared so that when you make a decision, when you make a commitment and purchase a piece of enterprise software, you feel confident with the decision you’ve made. I know, ironic!
We’re talking about human decision and AI decisioning, but the same is true of getting the AI decisioning software ready to make decisions. You would do all this due diligence and research, and you would want to understand your process. When the AI software takes over the decisioning, why not do the same amount of preparation for going into choosing which software is going to do this for you?
**Katie Robbert – 22:34**
It’s a huge undertaking integrating a new piece of tech into your existing environment. There’s no sugarcoating it. It’s not as simple as just plug it in and go. That’s what a lot of vendors—for better or worse—would have you believe. That it’s a seamless integration that does not exist. Turnkey integration—it does not exist. That is a huge myth we can bust.
If you are just starting tomorrow and it is your first piece of software ever, and there’s no other software to integrate it with, there is still no such thing as seamless integration because you still have to set it up. You still have to give it data that’s got to come from somewhere. There is no such thing as seamless integration. I will go on record: I will die on that hill.
**Christopher S. Penn – 23:30**
One other thing that is worth considering these days: if you have done the 5P’s and you know your decision processes cold—you know them like the back of your hand. In today’s world of generative AI, you might be better served building it yourself with generative AI tools. You might not need a vendor to spend $3 million a year with for what is essentially some gradient boosted trees and some language model processing.
You might want to evaluate whether to buy or build, whether build is the better choice for your organization. As generative AI tools get better and more capable, building becomes more feasible and reasonable, even for less technical organizations. There is still expertise required.
**Christopher S. Penn – 24:27**
To be clear, you still need subject matter expertise, but if you have developers already in your company—or you have a developer agency or something like that—you might want to put that on the table. You might not have to buy it. Especially since the cost of these systems keeps going up and up, and the brand-name ones don’t start for less than seven figures.
**Katie Robbert – 24:54**
It’s a huge expense. And here’s the thing, I hate this phrase, but “in this economy”—because, guess what, there’s always issues in the economy. But in this economy, spending seven figures is not a small decision to make. So you really want to make sure you’re making the right decision.
**Christopher S. Penn – 25:13**
Exactly. So ironic!
**Katie Robbert – 25:17**
I know.
**Christopher S. Penn – 25:18**
That’s what AI decisioning is: using artificial intelligence as part of a decision-making system—using both classical and generative AI appropriately for their areas of expertise. Don’t mix the two up, like generative AI should not be allowed to do math. You really have to do your homework before you make a decision about whether it’s buy or build. If you’ve got some thoughts about AI decisioning and decision-making software and you want to share them with your peers, pop on by our free Slack group. Go to Trust Insights AI analytics for Marketers, where over 4,000 other marketers are asking and answering each other’s questions every single day.
**Christopher S. Penn – 26:00**
Wherever you watch or listen to the show—if there’s a channel you’d rather have it on—said go to Trust Insights AI TI podcast, where you can find our show in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.
**Speaker 3 – 26:18**
Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of Truth, Acumen, and Prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.
**Speaker 3 – 26:47**
Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights’ services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams.
Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights Podcast, the Inbox Insights newsletter, the “So What?” Livestream, webinars, and keynote speaking.
**Speaker 3 – 27:56**
What distinguishes Trust Insights is their focus on delivering actionable insights—not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. This commitment to clarity and accessibility—data storytelling—extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.
Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.
Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.
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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI decisioning, the latest buzzword confusing marketers.
You will learn the true meaning of AI decisioning and the crucial difference between classical AI and generative AI for making sound business choices. You’ll discover when AI is an invaluable asset for decision support and when relying on it fully can lead to costly mistakes. You’ll gain practical strategies, including the 5P framework and key questions, to confidently evaluate AI decisioning software and vendors. You will also consider whether building your own AI solution could be a more effective path for your organization. Watch now to make smarter, data-driven decisions about adopting AI in your business!
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-what-is-ai-decisioning.mp3
Download the MP3 audio here.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
**Christopher S. Penn – 00:00**
In this week’s In-Ear Insights, let’s talk about a topic that is both old and new. This is decision optimization or decision planning, or the latest buzzword term AI decisioning. Katie, you are the one who brought this topic to the table. What the heck is this? Is this just more expensive consulting speak? What’s going on here?
**Katie Robbert – 00:23**
Well, to set the context, I’m actually doing a panel for the Martech organization on Wednesday, September 17, about how AI decisioning will change our marketing. There are a lot of questions we’ll be going over, but the first question that all of the panelists will be asked is, what is AI decisioning? I’ll be honest, Chris, it was not a term I had heard prior to being asked to do this panel. But, I am the worst at keeping up with trends and buzzwords.
When I did a little bit of research, I just kind of rolled my eyes and I was like, oh, so basically it’s the act of using AI to optimize the way in which decisions are made. Sort of. It’s exactly what it sounds like.
**Katie Robbert – 01:12**
But it’s also, I think, to your point, it’s a consultant word to make things sound more expensive than they should because people love to do that. So at a high level, it’s sticking a bunch of automated processes together to help support the act of making business decisions. I’m sure that there are companies that are fully comfortable with taking your data and letting their software take over all of your decisions without human intervention, which I could rant about for a very long time.
When I asked you this question last week, Chris, what is AI decisioning? You gave me a few different definitions. So why don’t you run through your understanding of AI decisioning?
**Christopher S. Penn – 02:07**
The big one comes from our friends at IBM. IBM used to have this platform called IBM Decision Optimization. I don’t actually know if it still exists or not, but it predated generative AI by about 10 years. IBM’s take on it, because they were using classical AI, was: decision optimization is the use of AI to improve or validate decisions.
The way they would do this was you take a bunch of quantitative data, put it into a system, and it basically would run a lot of binary tree classification. If this, then that—if this, then that—to try and come out with, okay, what’s the best decision to make here? That correlates to the outcome you care about. So that was classic AI decisioning from 2010-2020. Really, 2010-2020.
**Christopher S. Penn – 03:06**
Now everybody and their cousin is throwing this stuff at tools like ChatGPT and stuff like that. Boy, do I have some opinions about that—about why that’s not necessarily a great idea.
**Katie Robbert – 03:19**
What I like—the description you gave, the logical flow of “if this, then that”—is the way I understand AI decisioning to work. It should be a series of almost like a choose-your-own-adventure points: if this happens, go here; if this happens, go here. That’s the way I think about AI-assisted. I’m going to keep using the word assisted because I don’t think it should ever take over human decisioning. But that’s one person’s opinion. But I like that very binary “if this, then that” flow.
So that’s the way you and I agree it should be used. Let’s talk about the way it’s actually being used and the pros and cons of what the reality is today of AI decisioning.
**Christopher S. Penn – 04:12**
The way it’s being used or the way people want to use it is to fully outsource the decision-making to say, “AI, go and do this stuff for me and tell me when it’s done.” There are cases where that’s appropriate. We have an entire framework called the TRIPS framework, which is part of the new AI strategy course that you can get at TrustInsights AI strategy course. Katie teaches the TRIPS framework: Time, Repetitiveness, Importance, Pain, and Sufficient Data.
What’s weird about TRIPS that throws people off is that the “I” for importance means the less important a task is, the better a fit it is for AI—which fits perfectly into AI decisioning. Do you want to hand off completely a really important decision to AI? No. Do you want to hand off unimportant decisions to AI? Yes. The consequences for getting it wrong are so much lower.
**Christopher S. Penn – 05:05**
Imagine you had a GPT you built that said, “Where do we want to order lunch from today?” It has 10 choices, runs, and spits out an answer. If it gives you a wrong answer—wrong answer out of 10 places you generally like—you’re not going to be hugely upset. That is a great example of AI decisioning, where you’re just hanging out saying, “I don’t care, just make a decision. I don’t even care—we all know the places are all good.” But would you say, “Let’s hand off our go-to-market strategy for our flagship product line”? God, I hope not.
**Katie Robbert – 05:46**
It’s funny you say that because this morning I was using Gemini to create a go-to-market strategy for our flagship product line. However, with the huge caveat that I was not using generative AI to make decisions—I was using it to organize the existing data we already have.
Our sales playbook, our ICPs, all the different products—giving generative AI the context that we’re a small sales and marketing team. Every tactic we take needs to be really thoughtful, strategic, and impactful. We can’t do everything. So I was using it in that sense, but I wasn’t saying, “Okay, now you go ahead and execute a non-human-reviewed go-to-market strategy, and I’m going to measure you on the success of it.” That is absolutely not how I was using it.
**Katie Robbert – 06:46**
It was more of—I think the use case you would probably put that under is either summarization first and then synthesis next, but never decisioning.
**Christopher S. Penn – 07:00**
Yeah, and where this new crop of AI decisioning is going to run into trouble is the very nature of large language models—LLMs. They are language tools, they’re really good at language. So a lot of the qualitative stuff around decisions—like how something makes you feel or how words are used—yes, that is 100% where you should be using AI.
However, most decision optimization software—like the IBM Decision Optimization Project product—requires quantitative data. It requires an outcome to do regression analysis against. Behind the scenes, a lot of these tools take categorical data—like topics on your blog, for example—and reduce that to numbers so they can do binary classification. They figure out “if this, then that; if this, then that” and come up with the decision. Language models can’t do that because that’s math.
So if you are just blanket handing off decisioning to a tool like ChatGPT, it will imitate doing the math, but it will not do the math. So you will end up with decisions that are basically hallucinations.
**Katie Robbert – 08:15**
For those software companies promoting their tools to be AI decision tools or AI decisioning tools—whatever the buzz term is—what is the caution for the buyer, for the end user? What are the things we should be asking and looking for? Just as Chris mentioned, we have the new AI strategy course. One of the tools in the AI strategy course—or just the toolkit itself, if you want that at a lower cost—is the AI Vendor cheat sheet. It contains all the questions you should be asking AI vendors.
But Chris, if someone doesn’t know where to start and their CMO or COO is saying, “Hey, this tool has AI decisioning in it, look how much we can hand over.” What are the things we should be looking for, and what should we never do?
**Christopher S. Penn – 09:16**
First things I would ask are: “Show me your system map. Show me your system architecture map.” It should be high level enough that they don’t worry about giving away their proprietary secret sauce. But if the system map is just a big black box on a sheet of paper—no good.
Show me how the system works: how do you handle qualitative data? How do you handle quantitative data? How do you blend the two together? What are broadly the algorithm families involved? At some point, you should probably have binary classification trees in there. At some point, you should have regression analysis, like gradient boosting, in there. Those would be the technical terms I’d be looking for in a system map for decisioning software. Let me talk to an engineer without a salesperson present. That’s my favorite.
**Christopher S. Penn – 10:05**
And if a company says, “No, no, we can’t do”—clearly, then, there’s a problem because I know I’m going to ask the engineer something that “doesn’t do that.” What are you talking about? That is always the red flag for me. If you will not let me talk to an actual engineer with no salesperson present—no minder or keeper present—then, yeah, you’re not doing the right things.
The thing to not do is the common-sense thing, which is: don’t sign for a system until you’ve had a chance to evaluate. If you don’t know how to evaluate a system like that, ask for help. Ask: you can join our free Slack group. Go to analytics for Marketers, Trust Insights, AI analytics for Marketers.
**Christopher S. Penn – 10:51**
You can ask questions in there of all of us, like, “Hey, has anyone heard of this software?” We had someone share a piece of software last week in the chat, and people said, “What do you think about this?” I offered my opinion, which is: “Hey, this is going to be gathering very personal data, and their data protection clauses in their terms of service are really not strong.” So perhaps don’t use the software.
Of course, if something you want to have handled privately, you’re always welcome to work with Trust Insights. We will help you do these evaluations. That’s what we’re really good at. But those would be my things. The other big thing, Katie, I would ask you as the people person is—
**Christopher S. Penn – 11:33**
How do you know when a salesperson or a company rep is just bullshitting you?
**Katie Robbert – 11:40**
I get asked that question a lot, and there’s definitely an art to it. But the most simple response to that is: Can they give you direct answers, or not? Do they actually respond with, “I don’t know, but let me look into that for you”? Some people are really bad at BSing, so they’ll kind of talk in circles and never really get to the point and answer your question. So that’s an obvious tell.
There are a lot of people who are very good at BSing and do it with confidence, making you feel like, “Oh, well, they must be telling the truth.” Look how authoritative they are in their answer.
**Katie Robbert – 12:26**
So it’s on you—the end user, the potential buyer—to come ready with the list of questions that are important to you. I think that’s really the thing: they might be BSing everybody else. Great, let them. That’s not your problem. Your main focus is what is important to you.
Believe it or not, it’s going to start with getting your thoughts organized. The best way to do that is with the 5P framework. So, if you’re looking at AI decisioning software: What is the purpose? Why do we think we need AI decisioning software? What problem is it solving if we have AI decisioning software? That’s one of the first questions you ask the software vendors: “This is the problem I’m looking to solve. Talk to me about how you solve that problem and give me examples of how you solved that problem with other people.”
**Katie Robbert – 13:24**
And it’s okay to ask for references too. So you can say, “Hey, can I contact your other customers and talk to them about their experience using your software?” That’s a great way to cut through the BS. If they say, “No, we can’t do that”—that’s a huge red flag—because they want to sell as much product as possible. If they’re not willing to, or if there are NDAs in place, or whatever it is, they need to be able to explain why you can’t talk to their other customers who they’ve solved the same problem for.
Next is People. Think about it internally and externally. Internally: who’s using this software, who’s setting it up, who’s maintaining it, who’s accepting the outcomes, who’s doing the QA on it? Externally, from their side: who is your support system? Do they have 24/7 support?
**Katie Robbert – 14:19**
Is there a software license agreement you would need to sign to get support? Or are they just going to throw you to a cycle of never-ending chatbots that keep pointing you back to their FAQs and don’t actually answer your question?
Third is Process. How are we integrating this system into our existing tech stack? What does it look like to disrupt the existing tech stack with new software that takes in data? Does it take in our existing data? Do we have to do something different? Basically, outlining the different data formats and the systems you have for the sales rep, and saying, “This is what we have. Will your AI decisioning software fit within our existing process?”
This leads into Platform. These are the tools in our tech stack. Is there a natural integration, or will we have to set up external third-party integrations? Do we have to develop against APIs to get the data in, to get the data out? Those are not overly technical questions. Those are questions anyone should be able to answer, and that you should be able to understand the response to.
Lastly is Performance. How do we know this solved a problem? If your purpose for bringing in AI decisioning is efficiency or increased sales—that’s the metric you need to hold this piece of software to.
**Katie Robbert – 15:51**
Then ask the sales guy: “Let’s say we do a trial run of your software and it doesn’t do what it needs to do. How do you back your system out of our tech stack? How do you extract our data from your cloud servers? How do you just go away and pretend this never happened? What’s your money-back guarantee for performance?”
Those are basic, high-level questions. So use the 5P’s to get yourself organized. But those are the questions you should be asking any software vendor—AI or otherwise. But with AI decisioning—where the tool is meant to take the decisions out of your hands and do it for you—you want to make sure—100% sure—that you are confident in the decisions it’s making.
**Christopher S. Penn – 16:40**
One of the best things you can do—and we’ve covered this on previous Trust Insights Live Streams—is looking at qualitative data that exists on the internet from places like G2 Crowd, Capterra, Reddit, et cetera, and looking at the reviews for the software. For example, this is one company I know that makes decisioning software. We’re not going to share the name here, but when I looked at their reviews on Capterra, one of the reviews said it’s very expensive, it’s tricky to implement—and this was a big one.
The company regularly updates their software, but their updates do not align with our organizational needs. So the software drifts out of alignment and makes changes to decisioning software that we did not request.
**Katie Robbert – 17:30**
That’s a huge problem.
**Christopher S. Penn – 17:31**
That’s a real big problem. So if someone is out there on stage talking about their company’s AI decisioning software, and you look at the reviews, you might say, “It seems some of your customers say the decision-making process for how you do change management needs a little upgrade there, buddy.”
**Katie Robbert – 17:52**
Again, it’s not unreasonable to ask for referrals. Especially now, where there are so many software vendors to choose from—think about it like real estate, it’s a buyer’s market. You have no shortage of options. So how do you make the best decisions? One of those ways is talking to other people who have tried the software, left a review, or purchased the software and locked into a three-year agreement.
Ask if you can talk to them and get their opinions of how it went; how was the implementation; how is the support? In terms—you know, Chris, to your point—how often is the company making updates, and how well are they at not only communicating the updates, but what does it break? Because the sales team of the software, they’re going to tell you, “Here’s my talking points. Don’t go off script. I have a commission I need to meet for Q4.” So once they sell, it’s out of their hands. That’s now development and customer support’s problem.
**Christopher S. Penn – 19:13**
One of the things I would recommend people do—and this goes right along with the 5P’s—is, after you’ve documented how you currently make decisions and what you want the system to do. Set up a deep research project—or several, if it’s a big-ticket expense—and have generative AI build you the short list of. See, here are the companies that meet this criteria. Here’s how we make decisions: we have this data; we want to do it like this. Give it a prompt.
Something along the lines of, “You’re going to build a short list of companies that make AI decisioning software that meets these criteria, that is at this rough price point or range you’re willing to spend. These are the outcomes we’re looking for.”
**Christopher S. Penn – 19:58**
You should use review sites like G2 Crowd and Capterra, discussion forums like Reddit, and customer service messages—all to identify which platform is the best fit for our criteria. Create a list in descending order by goodness of fit, and make sure the software and the company have made substantial updates to their software in the last 365 days. Today’s date is whatever. Put that in as a generative AI deep research prompt. Put it in ChatGPT, put it in Gemini, put it in Perplexity.
Get a few different reports, merge them together, and see which vendors make the cut—which vendors are the best fit for your company for what’s going to be a very big, very expensive, and very painful process. Because decisioning software is big and painful. You will be surprised.
**Christopher S. Penn – 20:51**
When you go into that sales call, to your point, Katie, when the sales guy is trying to make his commission, you can say, “Here’s the criteria. Here’s what AI research came up with. Tell me what here is true and what is not.” Or even better, have generative AI build the list of questions for the salesperson so you can really dig down to the specifics.
And I guarantee that the first response for half the questions will be, “I need to check with our sales engineer on that.” You can say, “Great, why don’t you go ahead and do that?” Their incentive is not to help you succeed.
**Katie Robbert – 21:39**
And here’s the thing: This is not a knock at AI decisioning software. What we’re trying to do is make sure that you—the end user, the buyer—go into the process with both eyes open and that you’re fully prepared so that when you make a decision, when you make a commitment and purchase a piece of enterprise software, you feel confident with the decision you’ve made. I know, ironic!
We’re talking about human decision and AI decisioning, but the same is true of getting the AI decisioning software ready to make decisions. You would do all this due diligence and research, and you would want to understand your process. When the AI software takes over the decisioning, why not do the same amount of preparation for going into choosing which software is going to do this for you?
**Katie Robbert – 22:34**
It’s a huge undertaking integrating a new piece of tech into your existing environment. There’s no sugarcoating it. It’s not as simple as just plug it in and go. That’s what a lot of vendors—for better or worse—would have you believe. That it’s a seamless integration that does not exist. Turnkey integration—it does not exist. That is a huge myth we can bust.
If you are just starting tomorrow and it is your first piece of software ever, and there’s no other software to integrate it with, there is still no such thing as seamless integration because you still have to set it up. You still have to give it data that’s got to come from somewhere. There is no such thing as seamless integration. I will go on record: I will die on that hill.
**Christopher S. Penn – 23:30**
One other thing that is worth considering these days: if you have done the 5P’s and you know your decision processes cold—you know them like the back of your hand. In today’s world of generative AI, you might be better served building it yourself with generative AI tools. You might not need a vendor to spend $3 million a year with for what is essentially some gradient boosted trees and some language model processing.
You might want to evaluate whether to buy or build, whether build is the better choice for your organization. As generative AI tools get better and more capable, building becomes more feasible and reasonable, even for less technical organizations. There is still expertise required.
**Christopher S. Penn – 24:27**
To be clear, you still need subject matter expertise, but if you have developers already in your company—or you have a developer agency or something like that—you might want to put that on the table. You might not have to buy it. Especially since the cost of these systems keeps going up and up, and the brand-name ones don’t start for less than seven figures.
**Katie Robbert – 24:54**
It’s a huge expense. And here’s the thing, I hate this phrase, but “in this economy”—because, guess what, there’s always issues in the economy. But in this economy, spending seven figures is not a small decision to make. So you really want to make sure you’re making the right decision.
**Christopher S. Penn – 25:13**
Exactly. So ironic!
**Katie Robbert – 25:17**
I know.
**Christopher S. Penn – 25:18**
That’s what AI decisioning is: using artificial intelligence as part of a decision-making system—using both classical and generative AI appropriately for their areas of expertise. Don’t mix the two up, like generative AI should not be allowed to do math. You really have to do your homework before you make a decision about whether it’s buy or build. If you’ve got some thoughts about AI decisioning and decision-making software and you want to share them with your peers, pop on by our free Slack group. Go to Trust Insights AI analytics for Marketers, where over 4,000 other marketers are asking and answering each other’s questions every single day.
**Christopher S. Penn – 26:00**
Wherever you watch or listen to the show—if there’s a channel you’d rather have it on—said go to Trust Insights AI TI podcast, where you can find our show in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.
**Speaker 3 – 26:18**
Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of Truth, Acumen, and Prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.
**Speaker 3 – 26:47**
Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights’ services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams.
Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights Podcast, the Inbox Insights newsletter, the “So What?” Livestream, webinars, and keynote speaking.
**Speaker 3 – 27:56**
What distinguishes Trust Insights is their focus on delivering actionable insights—not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. This commitment to clarity and accessibility—data storytelling—extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.
Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.
Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.
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