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By Brian T. O’Neill from Designing for Analytics
5
3939 ratings
The podcast currently has 200 episodes available.
The relationship between AI and ethics is both developing and delicate. On one hand, the GenAI advancements to date are impressive. On the other, extreme care needs to be taken as this tech continues to quickly become more commonplace in our lives. In today’s episode, Ovetta Sampson and I examine the crossroads ahead for designing AI and GenAI user experiences.
While professionals and the general public are eager to embrace new products, recent breakthroughs, etc.; we still need to have some guard rails in place. If we don’t, data can easily get mishandled, and people could get hurt. Ovetta possesses firsthand experience working on these issues as they sprout up. We look at who should be on a team designing an AI UX, exploring the risks associated with GenAI, ethics, and need to be thinking about going forward.
Sometimes DIY UI/UX design only gets you so far—and you know it’s time for outside help. One thing prospects from SAAS analytics and data-related product companies often ask me is how things are like in the other guy/gal’s backyard. They want to compare their situation to others like them. So, today, I want to share some of the common “themes” I see that usually are the root causes of what leads to a phone call with me.
By the time I am on the phone with most prospects who already have a product in market, they’re usually either having significant problems with 1 or more of the following: sales friction (product value is opaque); low adoption/renewal worries (user apathy), customer complaints about UI/UX being hard to use; velocity (team is doing tons of work, but leader isn’t seeing progress)—and the like.
I’m hoping today’s episode will explain some of the root causes that may lead to these issues — so you can avoid them in your data product building work!
In today’s episode, I’m joined by John Felushko, a product manager at LabStats who impressed me after we recently had a 1x1 call together. John and his team have developed a successful product that helps universities track and optimize their software and hardware usage so schools make smart investments. However, John also shares how culture and value are very tied together—and why their product isn’t a fit for every school, and every country. John shares how important customer relationships are , how his team designs great analytics user experiences, how they do user research, and what he learned making high-end winter sports products that’s relevant to leading a SAAS analytics product. Combined with John’s background in history and the political economy of finance, John paints some very colorful stories about what they’re getting right—and how they’ve course corrected over the years at LabStats.
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In today’s episode, I’m going to perhaps work myself out of some consulting engagements, but hey, that’s ok! True consulting is about service—not PPT decks with strategies and tiers of people attached to rate cards. Specifically today, I decided to reframe a topic and approach it from the opposite/negative side. So, instead of telling you when the right time is to get UX design help for your enterprise SAAS analytics or AI product(s), today I’m going to tell you when you should NOT get help!
Reframing this was really fun and made me think a lot as I recorded the episode. Some of these reasons aren’t necessarily representative of what I believe, but rather what I’ve heard from clients and prospects over 25 years—what they believe. For each of these, I’m also giving a counterargument, so hopefully, you get both sides of the coin.
Finally, analytical thinkers, especially data product managers it seems, often want to quantify all forms of value they produce in hard monetary units—and so in this episode, I’m also going to talk about other forms of value that products can create that are worth paying for—and how mushy things like “feelings” might just come into play ;-) Ready?
Due to a technical glitch that ended up unpublishing this episode right after it originally was released, Episode 151 is a replay of my conversation with Zalak Trivdei from this past March . Please enjoy our chat if you missed it the first time around!
Thanks,
Brian
Original Episode: https://designingforanalytics.com/resources/episodes/139-monetizing-saas-analytics-and-the-challenges-of-designing-a-successful-embedded-bi-product-promoted-episode/
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Email: [email protected]
LinkedIn: https://www.linkedin.com/in/trivedizalak/
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About Promoted Episodes on Experiencing Data: https://designingforanalytics.com/promoted
“Last week was a great year in GenAI,” jokes Mark Ramsey—and it’s a great philosophy to have as LLM tools especially continue to evolve at such a rapid rate. This week, you’ll get to hear my fun and insightful chat with Mark from Ramsey International about the world of large language models (LLMs) and how we make useful UXs out of them in the enterprise.
Mark shared some fascinating insights about using a company’s website information (data) as a place to pilot a LLM project, avoiding privacy landmines, and how re-ranking of models leads to better LLM response accuracy. We also talked about the importance of real human testing to ensure LLM chatbots and AI tools truly delight users. From amusing anecdotes about the spinning beach ball on macOS to envisioning a future where AI-driven chat interfaces outshine traditional BI tools, this episode is packed with forward-looking ideas and a touch of humor.
Guess what? Data science and AI initiatives are still failing here in 2024—despite widespread awareness. Is that news? Candidly, you’ll hear me share with Evan Shellshear—author of the new book Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics—about how much I actually didn’t want to talk about this story originally on my podcast—because it’s not news! However, what is news is what the data says behind Evan’s findings—and guess what? It’s not the technology.
In our chat, Evan shares why he wanted to leverage a human approach to understand the root cause of multiple organizations’ failures and how this approach highlighted the disconnect between data scientists and decision-makers. He explains the human factors at play, such as poor problem surfacing and organizational culture challenges—and how these human-centered design skills are rarely taught or offered to data scientists. The conversation delves into why these failures are more prevalent in data science compared to other fields, attributing it to the complexity and scale of data-related problems. We also discuss how analytically mature companies can mitigate these issues through strategic approaches and stakeholder buy-in. Join us as we delve into these critical insights for improving data science project outcomes.
Ready for more ideas about UX for AI and LLM applications in enterprise environments? In part 2 of my topic on UX considerations for LLMs, I explore how an LLM might be used for a fictitious use case at an insurance company—specifically, to help internal tools teams to get rapid access to primary qualitative user research. (Yes, it’s a little “meta”, and I’m also trying to nudge you with this hypothetical example—no secret!) ;-) My goal with these episodes is to share questions you might want to ask yourself such that any use of an LLM is actually contributing to a positive UX outcome Join me as I cover the implications for design, the importance of foundational data quality, the balance between creative inspiration and factual accuracy, and the never-ending discussion of how we might handle hallucinations and errors posing as “facts”—all with a UX angle. At the end, I also share a personal story where I used an LLM to help me do some shopping for my favorite product: TRIP INSURANCE! (NOT!)
Let’s talk about design for AI (which more and more, I’m agreeing means GenAI to those outside the data space). The hype around GenAI and LLMs—particularly as it relates to dropping these in as features into a software application or product—seems to me, at this time, to largely be driven by FOMO rather than real value. In this “part 1” episode, I look at the importance of solid user experience design and outcome-oriented thinking when deploying LLMs into enterprise products. Challenges with immature AI UIs, the role of context, the constant game of understanding what accuracy means (and how much this matters), and the potential impact on human workers are also examined. Through a hypothetical scenario, I illustrate the complexities of using LLMs in practical applications, stressing the need for careful consideration of benchmarks and the acceptance of GenAI's risks.
I also want to note that LLMs are a very immature space in terms of UI/UX design—even if the foundation models continue to mature at a rapid pace. As such, this episode is more about the questions and mindset I would be considering when integrating LLMs into enterprise software more than a suggestion of “best practices.”
Highlights/ Skip to:
Quotes from Today’s Episode
Ben Shneiderman is a leading figure in the field of human-computer interaction (HCI). Having founded one of the oldest HCI research centers in the country at the University of Maryland in 1983, Shneiderman has been intently studying the design of computer technology and its use by humans. Currently, Ben is a Distinguished University Professor in the Department of Computer Science at the University of Maryland and is working on a new book on human-centered artificial intelligence.
I’m so excited to welcome this expert from the field of UX and design to today’s episode of Experiencing Data! Ben and I talked a lot about the complex intersection of human-centered design and AI systems.
In our chat, we covered:
The software engineering teams creating AI systems have got real work to do. They need the right kind of workflows, engineering patterns, and Agile development methods that will work for AI. The AI world is different because it’s not just programming, but it also involves the use of data that’s used for training. The key distinction is that the data that drives the AI has to be the appropriate data, it has to be unbiased, it has to be fair, it has to be appropriate to the task at hand. And many people and many companies are coming to grips with how to manage that. This has become controversial, let’s say, in issues like granting parole, or mortgages, or hiring people. There was a controversy that Amazon ran into when its hiring algorithm favored men rather than women. There’s been bias in facial recognition algorithms, which were less accurate with people of color. That’s led to some real problems in the real world. And that’s where we have to make sure we do a much better job and the tools of human-computer interaction are very effective in building these better systems in testing and evaluating. - Ben (6:10)
Every company will tell you, “We do a really good job in checking out our AI systems.” That’s great. We want every company to do a really good job. But we also want independent oversight of somebody who’s outside the company — someone who knows the field, who’s looked at systems at other companies, and who can bring ideas and bring understanding of the dangers as well. These systems operate in an adversarial environment — there are malicious actors out there who are causing trouble. You need to understand what the dangers and threats are to the use of your system. You need to understand where the biases come from, what dangers are there, and where the software has failed in other places. You may know what happens in your company, but you can benefit by learning what happens outside your company, and that’s where independent oversight from accounting companies, from governmental regulators, and from other independent groups is so valuable. - Ben (15:04)
There’s no such thing as an autonomous device. Someone owns it; somebody’s responsible for it; someone starts it; someone stops it; someone fixes it; someone notices when it’s performing poorly. … Responsibility is a pretty key factor here. So, if there’s something going on, if a manager is deciding to use some AI system, what they need is a control panel, let them know: what’s happening? What’s it doing? What’s going wrong and what’s going right? That kind of supervisory autonomy is what I talk about, not full machine autonomy that’s hidden away and you never see it because that’s just head-in-the-sand thinking. What you want to do is expose the operation of a system, and where possible, give the stakeholders who are responsible for performance the right kind of control panel and the right kind of data. … Feedback is the breakfast of champions. And companies know that. They want to be able to measure the success stories, and they want to know their failures, so they can reduce them. The continuous improvement mantra is alive and well. We do want to keep tracking what’s going on and make sure it gets better. Every quarter. - Ben (19:41)
Google has had some issues regarding hiring in the AI research area, and so has Facebook with elections and the way that algorithms tend to become echo chambers. These companies — and this is not through heavy research — probably have the heaviest investment of user experience professionals within data science organizations. They have UX, ML-UX people, UX for AI people, they’re at the cutting edge. I see a lot more generalist designers in most other companies. Most of them are rather unfamiliar with any of this or what the ramifications are on the design work that they’re doing. But even these largest companies that have, probably, the biggest penetration into the most number of people out there are getting some of this really important stuff wrong. - Brian (26:36)
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