Experiencing Data w/ Brian T. O’Neill

They unpack why starting with available data leads to polished but ineffective analytics. The conversation pushes teams to work backwards from the decisions customers actually face. It critiques data-availability bias and showy dashboards that shift interpretation onto users. They explore how conversational AI can help or hinder discovery and why prototypes should reveal problems, not validate assumptions.

A deep look at why technically strong analytics and AI often fail to deliver perceived value. Discussion of how insights can be invisible to buyers and users, causing stalled deals and reversion to spreadsheets. Exploration of three amplifiers: value translation, workflow alignment, and trust and control. Introduces practical frameworks to make intelligence visible, fit into real work, and build user confidence.

I’m continuing my exploration of a hard truth many leaders of analytics software companies run into: deals don’t stall because the tech is weak. Instead, they stall because prospects can’t see the value soon enough or the risk of changing the status quo is too high. This is often a product problem, not a sales one, and obtaining Flow-of-Work Alignment (FOWA) may help you start closing more evals and deals. So what is FOWA? The idea is simple, but demanding: stop showcasing features and start designing experiences that fit into how customers already do their work, create value, and add delight when your product is added into the loop.  Getting to FOWA means tailoring demos with realistic, industry-specific data, reducing mental translation, and minimizing behavior change. In this scenario, improvements become small, testable bets tied to outcomes, not feature checklists. UX and usability are not cosmetic; they should shape trust, adoption, and buyability.  When prospects can clearly see themselves succeeding with your product, value feels obvious, evals progress, and deals close.  Highlights/ Skip to: Steps to implementing Flow-of-Work Alignment (FOWA):  Tailor your demo or POC to map to the prospects' world and their workflow (1:53) Treat product improvements as bets that have to be tested so that observable outcomes are what you’re holding your product team accountable for (3:57) Reducing perceived behavior change (6:39) Realize that your product’s visual design are likely impacting your product’s clarity and its desirability (12:29)  Aligning your sales and product teams around customer outcomes and not feature gaps (18:03) Why you might think FOWA won’t work for your product—and how to reframe those objections (24:22)

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.