Bill Saltmarsh joins me to discuss where a modern CDO gets the inspiration to “operate in the producty way” in his domain, which is healthcare. Now Vice President of Enterprise Data and Transformation and the Chief Data Officer at Children’s Mercy Kansas City, his early days as an analyst revealed a gap between what stakeholders asked for vs. the outcomes they sought. This convinced him that data teams need to pause, ask better questions, and prioritize meaningful outcomes over quickly churning out dashboards and reports. Bill and I discuss how a producty mindset can be embedded across an organization. He also talks about why data leaders must set firm expectations. We explore the personal and cultural shifts needed for analysts and data scientists to embrace design, facilitation, and deeper discovery, even when it initially seems to slow things down. We also examine how to define value and ROI in healthcare, where a data team's impact is often indirect.  By tying data efforts to organizational OKRs and investing in governance, strong data foundations, and data literacy, he argues that analytics, data, and AI can drive better decisions, enhance patient care, and create durable organizational value. Highlights/ Skip to: What led Bill Saltmarsh to run his team at Children’s Mercy “the producty way” (1:42)  The kinds of environments Bill worked in prior that influenced his current management philosophy (4:36) Why data teams shouldn’t be report factories (6:37)  Setting the standard at the leadership level vs the everyday work (10:53) How Bill is skilling and hiring for non-technical skills (i.e. product, design, etc) (13:51)  Patterns that data professionals go through to know if they’re guiding stakeholders correctly (20:54)  The point when Bill has to think about the financial side of the hospital (26:30) How Bill thinks about measuring the data team’s  contributions to the hospital’s success (30:28) Bill’s philosophy on generative AI (36:00) Links Bill Saltmarsh on LinkedIn

In this final part of my three-episode series on accelerating sales and adoption in B2B analytics and AI products, I unpack a growing challenge in the age of generative AI: what to do when your product automates a major chunk of a user’s workflow only to reveal an entirely new problem right behind it. Building on Part I and Part II, I look at how AI often collapses the “front half” of a process, pushing the more complex, value-heavy work directly to users. This raises critical questions about product scope, market readiness, competitive risks, and whether you should expand your solution to tackle these newly surfaced problems or stay focused and validate what buyers will actually pay for. I also discuss why achieving customer delight—not mere satisfaction—is essential for earning trust, reducing churn, and creating the conditions where customers become engaged design partners. Finally, I highlight the common pitfalls of DIY product design and why intentional, validated UX work is so important, especially when AI is changing how work gets done faster than ever.   Highlights/ Skip to: Finishing the journey: staying focused, delighting users, and intentional UX (00:35) AI solves problems—and can create new ones for your customers—now what? (2:17) Do AI products have to solve your customers’ downstream “tomorrow” problems too before they’ll pay? (6:24)  Questions that reveal whether buyers will pay for expanded scope (6:45) UX outcomes: moving customers from satisfied to delighted before tackling new problems  (8:11) How obtaining “delight” status in the customer’s mind creates trust, lock-in, and permission to build the next solution (9:54) Designing experiences with intention (not hope) as AI changes workflows (10:40) My “Ten Risks of DIY Product Design…” — why DIY UX often causes self-inflicted friction (11:46)   Links Listen to part I: Episode 182 and part two: Episode 183 Read: “Ten Risks of DIY Product Design On Sales And Adoption Of B2B Data Products”  Stop guessing what is blocking your own product’s adoption and sales: Schedule a Design-Eyes Assessment with me, and in 90 minutes, I'll diagnose whether you're facing a design problem, a product management gap, a positioning issue, or something else entirely. You'll walk away knowing exactly what's standing between your product and the traction you need—so you don't waste time and money on product design "improvements" that won't move your critical KPIs.

In this second part of my three-part series (catch Part I via episode 182), I dig deeper into the key idea that sales in commercial data products can be accelerated by designing for actual user workflows—vs. going wide with a “many-purpose” AI and analytics solution that “does more,” but is misaligned with how users’ most important work actually gets done.   To explain this, I will explain the concept of user experience (UX) outcomes, and how building your solution to enable these outcomes may be a dependency for you to get sales traction, and for your customer to see the value of your solution. I also share practical steps to improve UX outcomes in commercial data products, from establishing a baseline definition of UX quality to mapping out users’ current workflows (and future ones, when agentic AI changes their job). Finally, I talk about how approaching product development as small “bets” helps you build small, and learn fast so you can accelerate value creation.    Highlights/ Skip to: Continuing the journey: designing for users, workflows, and tasks (00:32) How UX impacts sales—not just usage and  adoption(02:16) Understanding how you can leverage users’ frustrations and perceived risks as fuel for building an indispensable data product (04:11)  Definition of a UX outcome (7:30) Establishing a baseline definition of product (UX) quality, so you know how to observe and measure improvement (11:04 ) Spotting friction and solving the right customer problems first (15:34) Collecting actionable user feedback (20:02) Moving users along the scale from frustration to satisfaction to delight (23:04) Unique challenges of designing B2B AI and analytics products used for decision intelligence (25:04) Quotes from Today’s Episode One of the hardest parts of building anything meaningful, especially in B2B or data-heavy spaces, is pausing long enough to ask what the actual ‘it’ is that we’re trying to solve. People rush into building the fix, pitching the feature, or drafting the roadmap before they’ve taken even a moment to define what the user keeps tripping over in their day-to-day environment.   And until you slow down and articulate that shared, observable frustration, you’re basically operating on vibes and assumptions instead of behavior and reality.   What you want is not a generic problem statement but an agreed-upon description of the two or three most painful frictions that are obvious to everyone involved, frictions the user experiences visibly and repeatedly in the flow of work.   Once you have that grounding, everything else prioritization, design decisions, sequencing, even organizational alignment suddenly becomes much easier because you’re no longer debating abstractions, you’re working against the same measurable anchor.   And the irony is, the faster you try to skip this step, the longer the project drags on, because every downstream conversation becomes a debate about interpretive language rather than a conversation about a shared, observable experience. __ Want people to pay for your product? Solve an *observable* problem—not a vague information or data problem. What do I mean? “When you’re trying to solve a problem for users, especially in analytical or AI-driven products, one of the biggest traps is relying on interpretive statements instead of observable ones.   Interpretive phrasing like ‘they’re overwhelmed’ or ‘they don’t trust the data’ feels descriptive, but it hides the important question of what, exactly, we can see them doing that signals the problem.   If you can’t film it happening, if you can’t watch the behavior occur in real time, then you don’t actually have a problem definition you can design around.   Observable frustration might be the user jumping between four screens, copying and pasting the same value into different systems, or re-running a query five times because something feels off even though they can’t articulate why.   Those concrete behaviors are what allow teams to converg

Building B2B analytics and AI tools that people will actually pay for and use is hard. The reality is, your product won’t deliver ROI if no one’s using it. That’s why first principles thinking says you have to solve the usage problem first. In this episode, I’ll explain why the key to user adoption is designing with the flow of work—building your solution around the natural workflows of your users to minimize the behavior changes you’re asking them to make. When users clearly see the value in your product, it becomes easier to sell and removes many product-related blockers along the way. We’ll explore how product design impacts sales, the difference between buyers and users in enterprise contexts, and why challenging the “data/AI-first” mindset is essential. I’ll also share practical ways to align features with user needs, reduce friction, and drive long-term adoption and impact. If you’re ready to move beyond the dashboard and start building products that truly fit the way people work, this episode is for you.   Highlights/Skip to:  The core argument: why solving for user adoption first helps demonstrate ROI and facilitate sales in B2B analytics and AI products  (1:34) How showing the value to actual end users—not just buyers—makes it easier to sell your product (2:33) Why designing for outcomes instead of outputs (dashboards, etc) leads to better adoption and long-term product value (8:16) How to “see” beyond users’ surface-level feature requests and solutions so you can solve for the actual, unspoken need—leading to an indispensable product (10:23) Reframing feature requests as design-actionable problems (12:07)  Solving for unspoken needs vs. customer-requested features and functions (15:51) Why “disruption” is the wrong approach for product development (21:19)   Quotes:  “Customers’ tolerance for poorly designed B2B software has decreased significantly over the last decade. People now expect enterprise tools to function as smoothly and intuitively as the consumer apps they use every day.  Clunky software that slows down workflows is no longer acceptable, regardless of the data it provides. If your product frustrates users or requires extra effort to achieve results, adoption will suffer. Even the most powerful AI or analytics engine cannot compensate for a confusing or poorly structured interface. Enterprises now demand experiences that are seamless, efficient, and aligned with real workflows.    This shift means that product design is no longer a secondary consideration; it is critical to commercial success.  Founders and product leaders must prioritize usability, clarity, and delight in every interaction. Software that is difficult to use increases the risk of churn, lengthens sales cycles, and diminishes perceived value. Products must anticipate user needs and deliver solutions that integrate naturally into existing workflows.  The companies that succeed are the ones that treat user experience as a strategic differentiator. Ignoring this trend creates friction, frustration, and missed opportunities for adoption and revenue growth. Design quality is now inseparable from product value and market competitiveness.  The message is clear: if you want your product to be adopted, retain customers, and win in the market, UX must be central to your strategy.” —   “No user really wants to ‘check a dashboard’ or use a feature for its own sake. Dashboards, charts, and tables are outputs, not solutions. What users care about is completing their tasks, solving their problems, and achieving meaningful results.  Designing around workflows rather than features ensures your product is indispensable. A workflow-first approach maps your solution to the actual tasks users perform in the real world.  When we understand the jobs users need to accomplish, we can build products that deliver real value and remove friction. Focusing solely on features or data can create bloated products that users ignore or struggle to use.  Outputs

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.  Incidentally, one thing I didn’t know when I first agreed to consider having Gravity and Lucas on my show was that Lucas has been a long-time proponent of data product management and operating with a product mindset. In this episode, he shares the “ah-hah” moment where things clicked for him around building data products in this manner. Lucas shares how pivotal this moment was for him, and how it helped accelerate his career from Looker to Google and now Gravity.If you’re leading a data team, you’re a forward-thinking CDO, or you’re interested in commercializing your own analytics/AI product, my chat with Lucas should inspire you!     Highlights/ Skip to: Lucas’s breakthrough came when he embraced a data product management mindset (02:43) How Lucas thinks about Gravity as being the instrumentalists in an orchestra, conducted by the user (4:31) Finding product-market fit by solving for a common analytics pain point (8:11) Analytics product and dashboard adoption challenges: why dashboards die and thinking of analytics as changing the business gradually (22:25) What outcome-based pricing means for AI and analytics (32:08) The challenge of defining guardrails and ethics for AI-based analytics products [just in case somebody wants to “fudge the numbers”] (46:03) Lucas’ closing thoughts about what AI is unlocking for analysts and how to position your career for the future  (48:35) Special Bonus for DPLC Community Members 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: Quotes from Today’s Episode “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.” — Lucas  “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.”— Lucas  “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.” — Brian “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.”— Lucas “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 h

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. Highlights/ Skip to: Why shift to analytics and AI data product management (00:34) From accuracy to impact and redefining success with AI and analytical data products  (01:59) Key Idea 1: Moving from question asker (analyst) to problem seeker (product) (04:31) Key Idea 2: Designing change management into solutions; planning for adoption starts in the design phase (12:52) Key Idea 3: Creating tools so useful people can’t imagine working without them. (26:23) Key Idea 4: Solving for unarticulated needs vs. active needs (34:24) Quotes from Today’s Episode “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.Solving active problems is good, it’s necessary. But the big unlock happens when you start surfacing and solving latent problems, the ones people don’t think to ask for.Those are the moments when users say, ‘Oh wow, that changes everything.’That’s how data teams evolve from service providers to strategic partners.” “Here’s a simple but powerful shift for data teams: know who your real customer is.Most data teams think their customer is the stakeholder who requested the work…But the real customer is the end user whose life or decision should get better because of it.When you start designing for that person, not just the requester, everything changes: your priorities, your design, even what you choose to measure.” Links Need 1:1 help to navigate these questions and align your data product work to your career? Explore my new Cross-Company Group Coaching at designingforanalytics.com/groupcoaching For peer support: the Data Product Leadership Community where peers are experimenting with these approaches. designingforanalytics.com/community

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.     Highlights/ Skip to: How Kate approaches discussions with executives about moving fast, but also moving in a human-centered way when building out AI solutions (1:03) Exploring the lack of technical backgrounds among many CEOs and how this shapes the way organizations make big decisions around technical solutions (3:58)  FOMO and the “Solution in Search of a Problem” problem in Data (5:18)  Why ongoing ethnographic research and direct exposure to users are essential for true innovation (11:21)  Balancing organizational purpose and human-centered tech decisions, and why a defined purpose must precede these decisions (18:09) How organizations can define, measure, operationalize, and act on ethical considerations in AI and data products (35:57) Risk management vs. strategic optimism: balancing risk reduction with embracing the art of the possible when building AI solutions (43:54) Quotes from Today’s Episode "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." –Kate O’Neill     " 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."–Kate O’Neill     "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."–Kate O’Neill     “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 genuin

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.  Highlights/ Skip to:   Intro to Paxafe  (2:13)   How PAXAFE brings tons of cold chain data together in one user experience (2:33) Innovation in cold chain analytics is up, but so is cold chain product loss. (4:42) The product challenge of getting sufficient telemetry data at the right level of specificity to derive useful analytical insights (7:14)  Why and how PAXAFE pivoted away from providing IoT hardware to collect telemetry (10:23) How PAXAFE supports complex customer workflows, cold chain logistics, and complex supply chains (13:57) Who the end users of PAXAFE are, and how the product team designs for these users (20:00) Pharma loses around $40 billion a year relying on ‘Bob’s intuition’ in the warehouse. How Paxafe balances institutional user knowledge with the cold hard facts of analytics (42:43) Lessons learned when Ilya’s team fell in love with its own product and didn’t listen to the market  (23:57) Quotes from Today’s Episode "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." –IIya "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."-IIya "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.." -IIya "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."-IIya "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..”-IIya "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." -IIya Links PAXAFE: https://www.paxafe.com/ LinkedIn for Ilya Preston: https://www.linkedin.com/in/ilyapreston/ LinkedIn for company: https://www.linkedin.com/company/paxafe/

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. Highlights / Skip to: Introducing the MIRRR UX Framework (1:08) Designing for trust and user adoption plus perspectives you should be including when designing systems. (2:31) Monitor and interrupt controls let humans pause anything from a single AI task to the entire agent (3:17) Explaining “redirection” in the example context of use cases for claims adjusters working on insurance claims—so adjusters (users) can focus on important decisions. (4:35)  Rerun controls: lets humans redo an angentic task after unexpected results, preventing errors and building trust in early AI rollouts (11:12) Rerun vs. Redirect: what the difference is in the context of AI, using additional use cases from the insurance claim processing domain  (12:07) Empathy and user experience in AI adoption, and how the most useful insights come from directly observing users—not from analytics (18:28) Thinking about agentic AI as glue for existing applications and workflows, or as a worker  (27:35) Quotes from Today’s Episode 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." Quotes from Today’s Episode Part 1: The MIRRR UX Framework for Designing Trustworthy Agentic AI Applications 

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: Monitor – enabling appropriate transparency into AI agent behavior and performance Interrupt – designing both manual and automated pausing mechanisms to ensure human oversight remains possible when needed  …and in a couple weeks, stay tuned for part 2 where I’ll wrap up this first version of my MIRRR framework.  Highlights / Skip to: 00:34 Introducing the MIRRR UX Framework for designing trustworthy agentic AI Applications.  01:27 The importance of trust in AI systems and how it is linked to user adoption 03:06 Cultural shifts, AI hype, and growing AI skepticism 04:13  Human centered design practices for agentic AI   06:48 I discuss how understanding your users’ needs does not change with agentic AI, and that trust in agentic applications has direct ties to user adoption and value creation 11:32 Measuring success of agentic applications with UX outcomes 15:26 Introducing the first two of five MIRRR framework control points: 16:29 M is for Monitor; understanding the agent’s “performance,” and the right level of transparency end users need, from individual tasks to aggregate views  20:29 I is for Interrupt; when and why users may need to stop the agent—and what happens next 28:02 Conclusion and next steps

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.    Highlights/ Skip to: How Todd has addressed analytics apathy over the past decade at Pendo (1:17) Getting back to basics and not barraging people with more data and power (4:02) Pendo’s strategy for keeping the product experience simple without abandoning power users (6:44) Whether Todd is considering using an LLM (prompt-based) answer-driven experience with Pendo's UI (8:51) What Pendo looks for when hiring product managers right now, and why (14:58) How Pendo evaluates AI product managers, specifically (19:14) How Todd Olson views AI product management compared to traditional software product management (21:56) Todd’s concerns about the probabilistic nature of AI-generated answers in the product UX (27:51) What KPIs Todd uses to know whether Pendo is doing enough to reach its goals (32:49)   Why being able to tell what answers are best will become more important as choice increases (40:05)   Quotes from Today’s Episode “Let’s go back to classic Geoffrey Moore Crossing the Chasm, you’re selling to early adopters. And what you’re doing is you’re relying on the early adopters’ skill set and figuring out how to take this data and connect it to business problems. So, in the early days, we didn’t do anything because the market we were selling to was very, very savvy; they’re hungry people, they just like new things. They’re getting data, they’re feeling really, really smart, everything’s working great. As you get bigger and bigger and bigger, you start to try to sell to a bigger TAM, a bigger audience, you start trying to talk to the these early majorities, which are, they’re not early adopters, they’re more technology laggards in some degree, and they don’t understand how to use data to inform their job. They’ve never used data to inform their job. There, we’ve had to do a lot more work.” Todd (2:04 - 2:58) “I think AI is amazing, and I don’t want to say AI is overhyped because AI in general is—yeah, it’s the revolution that we all have to pay attention to. Do I think that the skills necessary to be an AI product manager are so distinct that you need to hire differently? No, I don’t. That’s not what I’m seeing. If you have a really curious product manager who’s going all in, I think you’re going to be okay. Some of the most AI-forward work happening at Pendo is not just product management. Our design team is going crazy. And I think one of the things that we’re seeing is a blend between design and product, that they’re always adjacent and connected; there’s more sort of overlappiness now.” Todd (22:41 - 23:28) “I think about things like stickiness, which may not be an aggregate time, but how often are people coming back and checking in? And if you had this companion or this agent that you just could not live without, and it caused you to come into the product almost every day just to check in, but it’s a fast check-in, like, a five-minute check-in, a ten-minute check-in, that’s pretty darn sticky. That’s a good metric. So, I like stickiness as a metric because it’s measuring [things like], “Are you thinking about this product a lot?” And if you’re thinking about it a lot, and like, you can’t kind of live without it, you’re going to go to it a lot, even if it’s only a few minutes a day. Social media is like that. Thankfully I’m not addicted to TikTok o

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.”  Highlights/ Skip to: Tony Zhang’s background (0:46) Context for the study (4:12) Zhang’s metrics for measuring over-reliance on AI (5:06) Understanding the differences between the two design options that study participants were given  (15:39) How AI-enabled hints appeared for pilots in each version of the UI (17:49) Using AI to help pilots make good decisions faster (20:15) We look at the ETF portfolio rebalancing use case in the study  (27:46) Strategic and tactical findings that Tony took away from his study (30:47) The possibility of commercially viable recommendations based on Tony’s findings (35:40)  Closing thoughts (39:04)   Quotes from Today’s Episode “I wanted to keep the difference between the [recommendation & forward reasoning versions] very minimal to isolate the effect of the recommendation coming in. So, if I showed you screenshots of those two versions, they would look very, very similar. The only difference that you would immediately see is that the recommendation version is showing numbers 1, 2, and 3 for the recommended airports. These [rankings] are not present in the forward-reasoning one [airports are default sorted nearest to furthest]. This actually is a pretty profound difference in terms of the interaction or the decision-making impact that the AI has. There is this normal flight mode and forward reasoning, so that pilots are already immersed in the system and thinking with the system during normal flight. It changes the process that they are going through while they are working with the AI.” Tony (18:50 - 19:42) “You would imagine that giving the recommendation makes your decision faster, but actually, the recommendations were not faster than the forward-reasoning one. In the forward-reasoning one, during normal flight, pilots could already prepare and have a good overview of their surroundings, giving them time to adjust to the new situation. Now, in normal flight, they don’t know what might be happening, and then suddenly, a passenger emergency happens. While for the recommendation version, the AI just comes into the situation once you have the emergency, and then you need to do this backward reasoning that we talked about initially.” Tony ( 21:12 - 21:58) “Imagine reviewing code written by other people. It’s always hard because you had no idea what was going on when it was written. That was the idea behind the forward reasoning. You need to look at how people are working and how you can insert AI in a way that it seamlessly fits and provides some benefit to you while keeping you in your usual thought process. So, the way that I see it is you need to identify where the key pain points actually are in your current decision-making process and try to address those instead of just trying to solve the task entirely for users.” Tony (25:40 - 26:19)   Links LinkedIn: https://www.linkedin.com/in/zelun-tony-zhang/  Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making: https://arxiv.org/html/2504.03207v1 

Today, I’m responding to a listener's question about what it takes to succeed as a data or AI product manager, especially if you’re coming from roles like design/BI/data visualization, data science/engineering, or traditional software product management. This reader correctly observed that most of my content “seems more targeted at senior leadership” — and had asked if I could address this more IC-oriented topic on the show. I’ll break down why technical chops alone aren’t enough, and how user-centered thinking, business impact, and outcome-focused mindsets are key to real success — and where each of these prior roles brings strengths and/or weaknesses. I’ll also get into the evolving nature of PM roles in the age of AI, and what I think the super-powered AI product manager will look like. Highlights/ Skip to: Who can transition into an AI and data product management role? What does it take? (5:29) Software product managers moving into  AI product management (10:05) Designers moving into data/AI product management (13:32) Moving into the AI PM role from the engineering side (21:47) Why the challenge of user adoption and trust is often the blocker to the business value (29:56) Designing change management into AI/data products as a skill (31:26) The challenge of value creation vs. delivery work — and how incentives are aligned for ICs  (35:17) Quantifying the financial value of data and AI product work(40:23) Quotes from Today’s Episode “Who can transition into this type of role, and what is this role? I’m combining these two things. AI product management often seems closely tied to software companies that are primarily leveraging AI, or trying to, and therefore, they tend to utilize this AI product management role. I’m seeing less of that in internal data teams, where you tend to see data product management more, which, for me, feels like an umbrella term that may include traditional analytics work, data platforms, and often AI and machine learning. I’m going to frame this more in the AI space, primarily because I think AI tends to capture the end-to-end product than data product management does more frequently.” — Brian (2:55)   “There are three disciplines I’m going to talk about moving into this role. Coming into AI and data PM from design and UX, coming into it from data engineering (or just broadly technical spaces), and then coming into it from software product management. I think software product management and moving into the AI product management - as long as you’re not someone that has two years of experience, and then 18 years of repeating the second year of experience over and over again - and you’ve had a robust product management background across some different types of products; you can show that the domain doesn’t necessarily stop you from producing value. I think you will have the easiest time moving into AI product management because you’ve shown that you can adapt across different industries.” - Brian (9:45)   “Let’s talk about designers next. I’m going to include data visualization, user experience research, user experience design, product design, all those types of broad design, category roles. Moving into data and/or AI product management, first of all, you don’t see too many—I don’t hear about too many designers wanting to move into DPM roles, because oftentimes I don’t think there’s a lot of heavy UI and UX all the time in that space. Or at least the teams that are doing that work feel that’s somebody else’s job because they’re not doing end-to-end product thinking the way I talk about it, so therefore, a lot of times they don’t see the application, the user experience, the human adoption, the change management, they’re just not looking at the world that way, even though I think they should be.” - Brian (13:32)   “Coming at this from the data and engineering side, this is the classic track for data product management. At least that is the way I tend to see it. I believe most compani

Today, I'm chatting with Shri Santhanam, the  EVP of Software Platforms and Chief AI Officer of Experian North America. Over the course of this promoted episode, you’re going to hear us talk about what it takes to build useful consumer and B2B AI products. Shri explains how Experian structures their AI product teams, the company’s approach prioritizing its initiatives, and what it takes to get their AI solutions out the door. We also get into the nuances of building trust with probabilistic AI tools and the absolutely critical role of UX in end user adoption.   Note: today’s episode is one of my rare Promoted Episodes. Please help support the show by visiting Experian’s links below:     Links Shri's LinkedIn Experian Assistant | Experian Experian Ascend Platform™ | Experian 

Today, I'm chatting with Stuart Winter-Tear about AI product management. We're getting into the nitty-gritty of what it takes to build and launch LLM-powered products for the commercial market that actually produce value. Among other things in this rich conversation, Stuart surprised me with the level of importance he believes UX has in making LLM-powered products successful, even for technical audiences.     After spending significant time on the forefront of AI’s breakthroughs, Stuart believes many of the products we’re seeing today are the result of FOMO above all else. He shares a belief that I’ve emphasized time and time again on the podcast–product is about the problem, not the solution. This design philosophy has informed Staurt’s 20-plus year-long career, and it is pivotal to understanding how to best use AI to build products that meet users’ needs.   Highlights/ Skip to  Why Stuart was asked to speak to the House of Lords about AI (2:04) The LLM-powered products has Stuart been building recently (4:20) Finding product-market fit with AI products (7:44) Lessons Stuart has learned over the past two years working with LLM-power products (10:54)  Figuring out how to build user trust in your AI products (14:40) The differences between being a digital product manager vs. AI product manager (18:13) Who is best suited for an AI product management role (25:42) Why Stuart thinks user experience matters greatly with AI products (32:18) The formula needed to create a business-viable AI product (38:22)  Stuart describes the skills and roles he thinks are essential in an AI product team and who he brings on first (50:53) Conversations that need to be had with academics and data scientists when building AI-powered products (54:04) Final thoughts from Stuart and where you can find more from him (58:07)   Quotes from Today’s Episode “I think that the core dream with GenAI is getting data out of IT hands and back to the business. Finding a way to overlay all this disparate, unstructured data and [translate it] to the human language is revolutionary. We’re finding industries that you would think were more conservative (i.e. medical, legal, etc.) are probably the most interested because of the large volumes of unstructured data they have to deal with. People wouldn’t expect large language models to be used for fact-checking… they’re actually very powerful, especially if you can have your own proprietary data or pipelines. Same with security–although large language models introduce a terrifying amount of security problems, they can also be used in reverse to augment security. There’s a lovely contradiction with this technology that I do enjoy.” - Stuart Winter-Tear (5:58) “[LLM-powered products] gave me the wow factor, and I think that’s part of what’s caused the problem. If we focus on technology, we build more technology, but if we focus on business and customers, we’re probably going to end up with more business and customers. This is why we end up with so many products that are effectively solutions in search of problems. We’re in this rush and [these products] are [based on] FOMO. We’re leaving behind what we understood about [building] products—as if [an LLM-powered product] is a special piece of technology. It’s not. It’s another piece of technology. [Designers] should look at this technology from the prism of the business and from the prism of the problem. We love to solutionize, but is the problem the problem? What’s the context of the problem? What’s the problem under the problem? Is this problem worth solving, and is GenAI a desirable way to solve it? We’re putting the cart before the horse.” - Stuart Winter-Tear (11:11) “[LLM-powered products] feel most amazing when you’re not a domain expert in whatever you’re using it for. I’ll give you an example: I’m terrible at coding. When I got my hands on Cursor, I felt like a superhero. It was unbelievable what I could build. Although [LLM products] look most amazing in

Today, I am going to share some of the biggest challenges internal enterprise data leaders may face when creating their first revenue-generating data product. If your team is thinking about directly monetizing a data product and bringing a piece of software to life as something customers actually pay for, this episode is for you. As a companion to this episode, you can read my original article on this topic here once you finish listening!

Today, I’m talking with Natalia Andreyeva from Infor about AI / ML product management and its application to supply chain software. Natalia is a Senior Director of Product Management for the Nexus AI / ML Solution Portfolio, and she walks us through what is new, and what is not, about designing AI capabilities in B2B software. We also got into why user experience is so critical in data-driven products, and the role of design in ensuring AI produces value. During our chat, Natalia hit on the importance of really nailing down customer needs through solid discovery and the role of product leaders in this non-technical work. We also tackled some of the trickier aspects of designing for GenAI, digital assistants, the need to keep efforts strongly grounded in value creation for customers, and how even the best ML-based predictive analytics need to consider UX and the amount of evidence that customers need to believe the recommendations. During this episode, Natalia emphasizes a huge key to her work’s success: keeping customers and users in the loop throughout the product development lifecycle.   Highlights/ Skip to What Natalia does as a Senior Director of Product Management for Infor Nexus (1:13) Who are the people using Infor Nexus Products and what do they accomplish when using them (2:51) Breaking down who makes up Natalia's team (4:05) What role does AI play in Natalia's work? (5:32) How do designers work with Natalia's team? (7:17) The problem that had Natalia rethink the discovery process when working with AI and machine learning applications (10:28) Why Natalia isn’t worried about competitors catching up to her team's design work (14:24) How Natalia works with Infor Nexus customers to help them understand the solutions her team is building (23:07) The biggest challenges Natalia faces with building GenAI and machine learning products (27:25) Natalia’s four steps to success in building AI products and capabilities (34:53) Where you can find more from Natalia (36:49)   Quotes from Today’s Episode “I always launch discovery with customers, in the presence of the UX specialist [our designer]. We do the interviews together, and [regardless of who is facilitating] the goal is to understand the pain points of our customers by listening to how they do their jobs today. We do a series of these interviews and we distill them into the customer needs; the problems we need to really address for the customers. And then we start thinking about how to [address these needs]. Data products are a particular challenge because it’s not always that you can easily create a UX that would allow users to realize the value they’re searching for from the solution. And even if we can deliver it, consuming that is typically a challenge, too. So, this is where [design becomes really important]. [...] What I found through the years of experience is that it’s very difficult to explain to people around you what it is that you’re building when you’re dealing with a data-driven product. Is it a dashboard? Is it a workboard? They understand the word data, but that’s not what we are creating. We are creating the actual experience for the outcome that data will deliver to them indirectly, right? So, that’s typically how we work.” - Natalia Andreyeva (7:47) “[When doing discovery for products without AI], we already have ideas for what we want to get out. We know that there is a space in the market for those solutions to come to life. We just have to understand where. For AI-driven products, it’s not only about [the user’s] understanding of the problem or the design, it is also about understanding if the data exists and if it’s feasible to build the solution to address [the user’s] problem. [Data] feasibility is an extremely important piece because it will drive the UX as well.” - Natalia Andreyeva (10:50) “When [the team] discussed the problem, it sounded like a simple calculation that needed to be created [for users]. In reality, it was an entire p

Today I am going to try to answer a fundamental question: how should you actually measure user experience, especially with data products—and tie this to business value? It's easy to get lost in analytics and think we're seeing the whole picture, but I argue that this is far from the truth. Product leaders need to understand the subjective experience of our users—and unfortunately, analytics does not tell us this. The map is not the territory.   In this episode, I discuss why qualitative data and subjective experience is the data that will most help you make product decisions that will lead you to increased business value. If users aren't getting value from your product(s), and their lives aren’t improving, business value will be extremely difficult to create. So today, I share my thoughts on how to move beyond thinking that analytics is the only way to track UX, and how this helps product leaders uncover opportunities to produce better organizational value.  Ultimately, it’s about creating indispensable solutions and building trust, which is key for any product team looking to make a real impact. Hat tip to UX guru Jared Spool who inspired several of the concepts I share with you today.   Highlights/ Skip to  Don't target adoption for adoption's sake, because product usage can be a tax or benefit (3:00) Why your analytical mind may bias you—and what changes you might have to do this type of product and user research work (7:31) How "making the user's life better" translates to organizational value (10:17) Using Jared Spool's roller coaster chart to measure your product’s user experience and find your opportunities and successes (13:05) How do you measure that you have done a good job with your UX? (17:28)  Conclusions and final thoughts (21:06)   Quotes from Today’s Episode Usage doesn't automatically equal value. Analytics on your analytics is not telling you useful things about user experience or satisfaction. Why? "The map is not the territory." Analytics measure computer metrics, not feelings, and let's face it, users aren't always rational. To truly gauge user value, we need qualitative research - to talk to users - and to hear what their subjective experience is. Want *meaningful* adoption? Talk to and observe your users. That's how you know you are actually making things better. When it’s better for them, the business value will follow. (3:12) Make better things—where better is a measurement based on the subjective experience of the user—not analytics. Usable doesn’t mean they will necessarily want it. Sessions and page views don’t tell you how people *feel* about it. (7:39) Think about the dreadful tools you and so many have been forced to use: the things that waste your time and don’t let you focus on what’s really important. Ever talked to a data scientist who is sick of doing data prep instead of building models, and wondering, “why am I here? This isn’t what I went to school for.” Ignoring these personal frustrations and feelings and focusing only on your customers’ feature requests, JIRA tickets, stakeholder orders, requirements docs, and backlog items is why many teams end up building technically right, effectively wrong solutions. These end user frustrations are where we find our opportunities to delight—and create products and UXs that matter. To improve their lives, we need to dig into their workflows, identify frustrations, and understand the context around our data product solutions. Product leaders need to fall in love with the problems and the frustrations—these are the magic keys to the value kingdom. However, to do this well, you probably need to be doing less delivery and more discovery. (10:27) Imagine a line chart with a Y-axis that is "frustration" at the bottom to "delight" at the top. The X-axis is their user experience, taking place over time. As somebody uses your data product to do their job/task, you can plot their emotional journey. “Get the data, format the data, include the da