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

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

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

By: Brian T. O’Neill from Designing for Analytics
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 Is the value of your enterprise analytics SAAS or AI product not obvious through it’s UI/UX? Got the data and ML models right...but user adoption of your dashboards and UI isn’t what you hoped it would be?

While it is easier than ever to create AI and analytics solutions from a technology perspective, do you find as a founder or product leader that getting users to use and buyers to buy seems harder than it should be?

If you lead an internal enterprise data team, have you heard that a ”data product” approach can help—but you’re concerned it’s all hype?

My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I share the stories of leaders who are leveraging product and UX design to make SAAS analytics, AI applications, and internal data products indispensable to their customers. After all, you can’t create business value with data if the humans in the loop can’t or won’t use your solutions.

Every 2 weeks, I release interviews with experts and impressive people I’ve met who are doing interesting work at the intersection of enterprise software product management, UX design, AI and analytics—work that you need to hear about and from whom I hope you can borrow strategies.

I also occasionally record solo episodes on applying UI/UX design strategies to data products—so you and your team can unlock financial value by making your users’ and customers’ lives better.

Hashtag: #ExperiencingData.

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https://designingforanalytics.com/ed

ABOUT THE HOST, BRIAN T. O’NEILL:
https://designingforanalytics.com/bio/© 2019 Designing for Analytics, LLC Art Economics Management Management & Leadership
Episodes
  • 185 - Driving Healthcare Impact by Aligning Teams Around Outcomes with Bill Saltmarsh

    185 - Driving Healthcare Impact by Aligning Teams Around Outcomes with Bill Saltmarsh
    Dec 23 2025

    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
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    41 mins
  • 184 - Part III: Designing with the Flow of Work: Accelerating Sales in B2B Analytics and AI Products by Minimizing Behavior Change

    184 - Part III: Designing with the Flow of Work: Accelerating Sales in B2B Analytics and AI Products by Minimizing Behavior Change
    Dec 9 2025

    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.
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    14 mins
  • 183 - Part II: Designing with the Flow of Work: Accelerating Sales in B2B Analytics and AI Products by Minimizing Behavior Change

    183 - Part II: Designing with the Flow of Work: Accelerating Sales in B2B Analytics and AI Products by Minimizing Behavior Change
    Nov 27 2025
    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 converge and say, ‘Yes, that’s the thing, that is the friction we agree must change,’ and that shift from interpretation to observation becomes the foundation for better design, better decision-making, and far less wasted effort. And once you anchor the conversation in visible behavior, you eliminate so many circular debates and give everyone, from engineering to leadership, a shared starting point that’s grounded in reality instead of theory." __ One of the reasons that measuring the usability/utility/satisfaction of your product’s UX might seem hard is that you don’t have a baseline definition of how satisfactory (or not) the product is right now. As such, it’s very hard to tell if you’re just making product *changes*—or you’re making *improvements* that might make the product worth paying for at all, worth paying more for, or easier to buy. "It’s surprisingly common for teams to claim they’re improving ...
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    35 mins
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