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):

1. Tailor your demo or POC to map to the prospects' world and their workflow (1:53)
2. 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)
3. Reducing perceived behavior change (6:39)
4. Realize that your product’s visual design are likely impacting your product’s clarity and its desirability (12:29)
5. Aligning your sales and product teams around customer outcomes and not feature gaps (18:03)

I’m digging into a frustrating reality many teams face: even technically superior analytics and AI products routinely lose deals—not because the KPIs or models aren’t good enough, but because buyers and users can’t clearly see how the product fits into their day-to-day work. Your demos and POCs may prove what’s possible, but long time-to-understanding, heavy thinking burden on the user, and required behavior or process changes introduce risk—and risk kills momentum. When value feels complicated, sales don’t move forward.

Adding to the challenge is that many sales efforts focus almost entirely on the fiscal buyer while overlooking the end users who actually have to adopt the product to create outcomes. This buyer–user mismatch, combined with status quo bias, often leads to indecision rather than change.

To address this, I explore the idea of thinking about the sales challenge as a product problem—and I introduce the idea of achieving Flow of Work Alignment (FOWA). The goal isn’t better persuasion—it’s clearer value. Strong FOWA means transitioning from demonstrating capabilities to helping customers see themselves—and their workflows—represented in your demos and POCs. The result? Prospects understand your value quickly, ask deeper, contextual questions, and deals move forward.

Highlights/ Skip to:

• Data products must work harder to expose value clearly to avoid the dreaded “closed-lost” deal stage in your CRM (1:38)
• Making your data product’s value instantly obvious (5:18)
• How the “old model” of selling based on capabilities and feature demos can lead to lost sales (7:22)
• What Flow-of-Work Alignment is and how it can help you unlock deals (13:02)
• How to know if you have achieved FOWA or not in your product and sales process (13:58)

I’m back! After about 7 years (or more) of bi-weekly publishing, I gave myself a break (to have the flu, in part), but now it’s back to business! In 2026, I’ll be focusing the podcast more on the commercial side of data products. This means more founders, CEOs, and product leader guests at small and mid-sized B2B software companies who are building technically impressive B2B analytics and AI products. With all the focus on AI, I want to focus on things that don’t change: what do value and outcomes look like to buyers and users, and how do we recreate it with analytics and AI? What learnings and changes have leaders had to make on the product and UI/UX side to get buyers to buy and users to use?

So, that brings us to today’s episode. Today, I’ll explain why I think model quality, analytics data, and raw AI capability are quickly becoming commodities, shifting the real challenge to how effectively companies can translate their data and intelligence into value that buyers and users can clearly understand and defend.

I dig into a core tension in B2B products: fiscal buyers and end users want different things. Buyers need confidence, risk reduction, and defensible ROI, while users care about making their daily work easier and safer. When products try to appeal broadly or force customers to figure out how AI fits into their workflows, adoption breaks down. Instead, I make the case for tightly scoped, workflow-aware solutions that make value obvious, deliver fast time-to-value, and support real decisions and actions.

Highlights/ Skip to:

• Refocusing the trajectory of the show for 2026 (00:31)
• Turning your product’s intelligence into clear, actionable solutions so users can see the value without having to figure it out themselves (4:32)
• You’re selling capability, but buyers are buying relief from a specific pain point (7:33)
• Asking customers where AI fits into their workflow is poor design (16:57)
• Buyers and users both require proof of value, but in different ways (20:05)
• Why incomplete workflows kill trust (24:18)
• The importance of translating technical capability into something a human is willing to own (30:09)

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.

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)

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:

“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

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