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
  • 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
  • 182 - Designing with the Flow of Work: Accelerating Sales in B2B Analytics and AI Products by Minimizing Behavior Change

    182 - Designing with the Flow of Work: Accelerating Sales in B2B Analytics and AI Products by Minimizing Behavior Change
    Nov 10 2025
    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 are meaningless if they do not fit into the context of a user’s work. The key is to translate user needs into actionable workflows and design every element to support those flows. This approach reduces cognitive load, improves adoption, and ensures the product's ROI is realized. It also allows you to anticipate challenges and design solutions that make workflows smoother, faster, and more efficient. By centering design on actual tasks rather than arbitrary metrics, your product becomes a tool users can’t imagine living without. Workflow-focused design directly ties to measurable outcomes for both end users and buyers. It shifts the conversation from features to value, making adoption, satisfaction, and revenue more predictable.” — “Just because a product is built with AI or powerful data capabilities doesn’t mean anyone will adopt it. Long-term value comes from designing solutions that users cannot live without. It...
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    23 mins
  • 181- Lessons Learned Designing Orion, Gravity’s AI, AI Analyst Product with CEO Lucas Thelosen (former Head of Product @ Google Data & AI Cloud)

    181- Lessons Learned Designing Orion, Gravity’s AI, AI Analyst Product with CEO Lucas Thelosen (former Head of Product @ Google Data & AI Cloud)
    Oct 28 2025

    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 human experience. Experience is a collection of feelings and perceptions about how something went.” — Brian

    Links
    • Gravity: https://www.bygravity.com
    • LinkedIn: https://www.linkedin.com/in/thelosen/
    • Email Lucas and team: hello@bygravity.com
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    50 mins
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