On today's Promoted Episode of Experiencing Data, I’m talking with Lucas Thelosen, CEO of Gravity and creator of Orion, an AI analyst transforming how data teams work. Lucas was head of PS for Looker, and eventually became Head of Product for Google’s Data and AI Cloud prior to starting his own data product company. We dig into how his team built Orion, the challenge of keeping AI accurate and trustworthy when doing analytical work, and how they’re thinking about the balance of human control with automation when their product acts as a force multiplier for human analysts.

In addition to talking about the product, we also talk about how Gravity arrived at specific enough use cases for this technology that a market would be willing to pay for, and how they’re thinking about pricing in today’s more “outcomes-based” environment. 

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Are you a member of the Data Product Leadership Community? After our chat, I invited Lucas to come give a talk about his journey of moving from “data” to “product” and adopting a producty mindset for analytics and AI work. He was more than happy to oblige. Watch for this in late 2025/early 2026 on our monthly webinar and group discussion calendar.

Note: today’s episode is one of my rare Promoted Episodes. Please help support the show by visiting Gravity’s links below:

“The whole point of data and analytics is to help the business evolve. When your reports make people ask new questions, that’s a win. If the conversations today sound different than they did three months ago, it means you’ve done your job, you’ve helped move the business forward.”

“Accuracy is everything. The moment you lose trust, the business, the use case, it's all over. Earning that trust back takes a long time, so we made accuracy our number one design pillar from day one.”

“Language models have changed the game in terms of scale. Suddenly, we’re facing all these new kinds of problems, not just in AI, but in the old-school software sense too. Things like privacy, scalability, and figuring out who’s responsible.”

“Most people building analytics products have never been analysts, and that’s a huge disadvantage. If data doesn’t drive action, you’ve missed the mark. That’s why so many dashboards die quickly.”

“Re: collecting feedback so you know if your UX is good: I generally agree that qualitative feedback is the best place to start, not analytics [on your analytics!] Especially in UX, analytics measure usage aspects of the product, not the subject human experience. Experience is a collection of feelings and perceptions about how something went.”

In this episode, I’m exploring the mindset shift data professionals need to make when moving into analytics and AI data product management. From how to ask the right questions to designing for meaningful adoption, I share four key ways to think more like a product manager, and less like a deliverables machine, so your data products earn applause instead of a shoulder shrug.

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“Too many analytics teams are rewarded for accuracy instead of impact. Analysts give answers, and product people ask questions.The shift from analytics to product thinking isn’t about tools or frameworks, it’s about curiosity.It’s moving from ‘here’s what the data says’ to ‘what problem are we actually trying to solve, and for whom?’That’s where the real leverage is, in asking better questions, not just delivering faster answers.”

“We often mistake usage for success.Adoption only matters if it’s meaningful adoption. A dashboard getting opened a hundred times doesn’t mean it’s valuable... it might just mean people can’t find what they need.Real success is when your users say, ‘I can’t imagine doing my job without this.’That’s the level of usefulness we should be designing for.”

“The most valuable insights aren’t always the ones people ask for.

“Here’s a simple but powerful shift for data teams: know who your real customer is.

Content coming soon. 

In this episode, I sat down with tech humanist Kate O’Neill to explore how organizations can balance human-centered design in a time when everyone is racing to find ways to leverage AI in their businesses. Kate introduced her “Now–Next Continuum,” a framework that distinguishes digital transformation (catching up) from true innovation (looking ahead). We dug into real-world challenges and tensions of moving fast vs. creating impact with AI, how ethics fits into decision making, and the role of data in making informed decisions. 

Kate stressed the importance of organizations having clear purpose statements and values from the outset, proxy metrics she uses to gauge human-friendliness, and applying a “harms of action vs. harms of inaction” lens for ethical decisions. Her key point: human-centered approaches to AI and technology creation aren’t slow; they create intentional structures that speed up smart choices while avoiding costly missteps.

"I think the ethics and the governance and all those kinds of discussions [about the implications of digital transformation] are all very big word - kind of jargon-y kinds of discussions - that are easy to think aren't important, but what they all tend to come down to is that alignment between what the business is trying to do and what the person on the other side of the business is trying to do."

" I've often heard the term digital transformation used almost interchangeably with the term innovation. And I think that that's a grave disservice that we do to those two concepts because they're very different. Digital transformation, to me, seems as if it sits much more comfortably on the earlier side of the Now-Next Continuum. So, it's about moving the past to the present… Innovation is about standing in the present and looking to the future and thinking about the art of the possible, like you said. What could we do? What could we extract from this unstructured data (this mess of stuff that’s something new and different) that could actually move us into green space, into territory that no one’s doing yet? And those are two very different sets of questions. And in most organizations, they need to be happening simultaneously."

"The reason I chose human-friendly [as a term] over human-centered partly because I wanted to be very honest about the goal and not fall back into, you know, jargony kinds of language that, you know, you and I and the folks listening probably all understand in a certain way, but the CEOs and the folks that I'm necessarily trying to get reading this book and make their decisions in a different way based on it."

“We love coming up with new names for different things. Like whether something is “cloud,” or whether it’s like, you know, “SaaS,” or all these different terms that we’ve come up with over the years… After spending so long working in tech, it is kind of fun to laugh at it. But it’s nice that there’s a real earnestness [to it]. That’s sort of evergreen [laugh]. People are always trying to genuinely solve human problems, which is what I try to tap into these days, with the work that I do, is really trying to help businesses—business leaders, mostly, but a lot of those are non-tech leaders, and I think that’s where this really sticks is that you get a lot of people who have ascended into CEO or other C-suite roles who don’t come from a technology background.” 

–Kate O’Neill

"My feeling is that if you're not regularly doing ethnographic research and having a lot of exposure time directly to customers, you’re doomed. The people—the makers—have to be exposed to the users and stakeholders.  There has to be ongoing work in this space; it can't just be about defining project requirements and then disappearing. However, I don't see a lot of data teams and AI teams that have non-technical research going on where they're regularly spending time with end users or customers such that they could even imagine what the art of the possible could be.”

In this episode, I talk with Ilya Preston, co-founder and CEO of PAXAFE, a logistics orchestration and decision intelligence platform for temperature-controlled supply chains (aka “cold chain”). Ilya explains how PAXAFE helps companies shipping sensitive products, like pharmaceuticals, vaccines, food, and produce, by delivering end-to-end visibility and actionable insights powered by analytics and AI that reduce product loss, improve efficiency, and support smarter real-time decisions.

Ilya shares the challenges of building a configurable system that works for transportation, planning, and quality teams across industries. We also discuss their product development philosophy, team structure, and use of AI for document processing, diagnostics, and workflow automation. 

"Our initial vision for what PAXAFE would become was 99.9% spot on. The only thing we misjudged was market readiness—we built a product that was a few years ahead of its time."

"As an industry, pharma is losing $40 billion worth of product every year because decisions are still based on warehouse intuition about what works and what doesn’t. In production, the problem is even more extreme, with roughly $800 billion lost annually due to temperature issues and excursions."

"With our own design, our initial hypothesis and vision for what Pacaf could be really shaped where we are today. Early on, we had a strong perspective on what our customers needed—and along the way, we fell in love with our own product and design.."

"We spent months perfecting risk scores… only to hear from customers, ‘I don’t care about a 71 versus a 62—just tell me what to do.’ That single insight changed everything."

"If you’re not talking to customers or building a product that supports those conversations, you’re literally wasting time. In the zero-to-product-market-fit phase, nothing else matters, you need to focus entirely on understanding your customers and iterating your product around their needs..”

"Don’t build anything on day one, probably not on day two, three, or four either. Go out and talk to customers. Focus not on what they think they need, but on their real pain points. Understand their existing workflows and the constraints they face while trying to solve those problems."

This is part two of the framework; if you missed part one, head to episode 175 and start there so you're all caught up. 

In this episode of Experiencing Data, I continue my deep dive into the MIRRR UX Framework for designing trustworthy agentic AI applications. Building on Part 1’s “Monitor” and “Interrupt,” I unpack the three R’s: Redirect, Rerun, and Rollback—and share practical strategies for data product managers and leaders tasked with creating AI systems people will actually trust and use. I explain human-centered approaches to thinking about automation and how to handle unexpected outcomes in agentic AI applications without losing user confidence. I am hoping this control framework will help you get more value out of your data while simultaneously creating value for the human stakeholders, users, and customers.

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The value of AI isn’t just about technical capability, it’s based in large part on whether the end-users will actually trust and adopt it. If we don’t design for trust from the start, even the most advanced AI can fail to deliver value."

"In agentic AI, knowing when to automate is just as important as knowing what to automate. Smart product and design decisions mean sometimes holding back on full automation until the people, processes, and culture are ready for it."

"Sometimes the most valuable thing you can do is slow down, create checkpoints, and give people a chance to course-correct before the work goes too far in the wrong direction."

"Reruns and rollbacks shouldn’t be seen as failures, they’re essential safety mechanisms that protect both the integrity of the work and the trust of the humans in the loop. They give people the confidence to keep using the system, even when mistakes happen."

"You can’t measure trust in an AI system by counting logins or tracking clicks. True adoption comes from understanding the people using it, listening to them, observing their workflows, and learning what really builds or breaks their confidence."

"You’ll never learn the real reasons behind a team’s choices by only looking at analytics, you have to actually talk to them and watch them work."

"Labels matter, what you call a button or an action can shape how people interpret and trust what will happen when they click it."

In this episode of Experiencing Data, I introduce part 1 of my new MIRRR UX framework for designing trustworthy agentic AI applications—you know, the kind that might actually get used and have the opportunity to create the desired business value everyone seeks! One of the biggest challenges with both traditional analytics, ML, and now, LLM-driven AI agents, is getting end users and stakeholders to trust and utilize these data products—especially if we’re asking humans in the loop to make changes to their behavior or ways of working. 

In this episode, I challenge the idea that software UIs will vanish with the rise of AI-based automation. In fact, the MIRRR framework is based on the idea that AI agents should be “in the human loop,” and a control surface (user interface) may in many situations be essential to ensure any automated workers engender trust with their human overlords.  

By properly considering the control and oversight that end users and stakeholders need, you can enable the business value and UX outcomes that your paying customers, stakeholders, and application users seek from agentic AI. 

Using use cases from insurance claims processing, in this episode, I introduce the first two of five control points in the MIRRR framework—Monitor and Interrupt. These control points represent core actions that define how AI agents often should operate and interact within human systems:

 …and in a couple weeks, stay tuned for part 2 where I’ll wrap up this first version of my MIRRR framework. 

In this episode of Experiencing Data, I chat with Irina Malkova who is the VP of AI Engineering and VP of Data and Analytics for Tech and Product at Salesforce. Irina shares how her teams are reinventing internal analytics, combining classic product data work with cutting-edge AI engineering—and her recent post on LinkedIn titled “AI adoption moves at the speed of user trust,” having a strong design-centered perspective, inspires today’s episode. (I even quoted her on this in a couple recent product design conference talks I gave!)  In today’s drop, Irina shares how they’re enabling analytical insights at Salesforce via a Slack-based AI agent, how they have changed their AI and engineering org structures (and why), the bad advice they got on organizing their data product teams, and more. This is a great episode for senior data product and AI executives managing complex orgs and technology environments who want to see how Salesforce is scaling AI for smarter, faster decisions.

Todd Olson joins me to talk about making analytics worth paying for and relevant in the age of AI. The CEO of Pendo, an analytics SAAS company, Todd shares how the company evolved to support a wider audience by simplifying dashboards, removing user roadblocks, and leveraging AI to both generate and explain insights. We also talked about the roles of product management at Pendo. Todd views AI product management as a natural evolution for adaptable teams and explains how he thinks about hiring product roles in 2025. Todd also shares how he thinks about successful user adoption of his product around “time to value” and “stickiness” over vanity metrics like time spent. 

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Today on the podcast, I interview AI researcher Tony Zhang about some of his recent findings about the effects that fully automated AI has on user decision-making. Tony shares lessons from his recent research study comparing typical recommendation AIs with a “forward-reasoning” approach that nudges users to contribute their own reasoning with process-oriented support that may lead to better outcomes. We’ll look at his two study examples where they provided an AI-enabled interface for pilots tasked with deciding mid-flight the next-best alternate airport to land at, and another scenario asking investors to rebalance an ETF portfolio. The takeaway, taken right from Tony’s research, is that “going forward, we suggest that process-oriented support can be an effective framework to inform the design of both 'traditional' AI-assisted decision-making tools but also GenAI-based tools for thought.” 

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