Experiencing Data w/ Brian T. O’Neill (AI & data product management leadership—powered by UX design)

They explore why technically superior analytics and AI products still lose deals when buyers cannot see workflow fit. The conversation highlights hidden adoption costs, cognitive load from capability-led demos, and the danger of ignoring end users. They introduce Flow of Work Alignment as a way to make value instantly visible and show signs that demos are truly representing customer workflows.

The discussion tackles why advanced AI products can feel irrelevant to buyers. A key point is the divergence in needs between fiscal buyers seeking ROI and end users looking for ease in their tasks. The host emphasizes that while model quality may become a commodity, the challenge lies in effectively translating data into clear benefits. He advocates for narrowly focused solutions that don't force customers to find their own use cases, warning that incomplete workflows can damage trust and undermine long-term retention.

Bill Saltmarsh, the Vice President of Enterprise Data and Transformation at Children's Mercy Kansas City, shares his journey from analyst to data leader. He emphasizes the importance of a product mindset in healthcare data, advocating for meaningful outcomes over simply producing reports. Bill discusses the need for better stakeholder communication and how balancing product-focused behaviors with leadership expectations can foster trust. The conversation also covers generative AI's role in clinical workflows and the criticality of data literacy in empowering organizations.

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.

The discussion dives into the critical role of user experience in driving sales for B2B analytics and AI products. Emphasizing the need to design for actual user workflows, the speaker highlights how addressing user frustrations can accelerate value creation. Practical steps are shared for improving UX outcomes, including establishing baselines and mapping workflows. The importance of making small, strategic bets in product development is stressed, allowing teams to learn quickly and adapt. Overall, the focus is on aligning product design with user needs to enhance satisfaction.

Discover the importance of user adoption in maximizing ROI for B2B analytics and AI products. Learn how designing solutions that align with user workflows can drastically simplify the sales process. Explore the notion of focusing on outcomes rather than just data outputs, and how reframing user requests into solvable problems can lead to better product development. Hear a compelling analogy about products flowing like rivers alongside user activities, minimizing behavioral change and risk.

In this captivating discussion, Lucas Thelosen, CEO of Gravity and creator of the innovative AI analyst product Orion, shares his impressive background, including his tenure at Google Data & AI Cloud. He dives into how Orion transforms data team dynamics by augmenting analysts rather than replacing them, tackling challenges of accuracy and reliability in AI. Lucas highlights the importance of understanding customer needs, the breakthrough moment that changed his approach to data products, and the impact of effective UX design on user engagement.

Data professionals are urged to shift their mindset from simply delivering answers to becoming proactive problem seekers. The importance of meaningful adoption versus mere usage metrics is discussed, along with the necessity of integrating change management during the design phase. By solving unarticulated needs and asking better questions, teams can create impactful AI and data products. Additionally, the value of tiered problem discovery and rapid learning in deployment are highlighted as crucial steps for success in data product management.

Discover foundational UX principles that empower data and AI product managers. Learn why non-designers must grasp UX to amplify their impact. Dive into the importance of routine user research and defining measurable outcomes. Explore the concept of refining problems over solutions and fostering trust as a pivotal feature in AI. Embrace a prototyping mindset to experiment with AI and design for observable behavior changes. Finally, understand that successful design is a collaborative effort focused on serving users and stakeholders.

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 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.” –Brian T. O’Neill   Links KO Insights: https://www.koinsights.com/ LinkedIn for Kate O’Neill: https://www.linkedin.com/in/kateoneill/ Kate O’Neill Book: What Matters Next: A Leader's Guide to Making Human-Friendly Tech Decisions in a World That's Moving Too Fast