Experiencing Data w/ Brian T. O’Neill

I’ve seen this pattern repeatedly with teams building analytics and AI products: the issue usually isn’t the quality of the models or the sophistication of the data. The technology often works just fine. The real breakdown happens earlier—when teams begin with the data they already have and try to figure out what to build, instead of starting with the decisions their customers need to make.

 

 

That approach often produces polished dashboards and compelling features that generate interest, but fail to drive real action. The missing piece is context. Decisions in the real world depend on incentives, habits, risk tolerance, and uncertainty—not just clean data. If your product doesn’t reflect that reality, it won’t meaningfully change behavior.

 

 

Another common trap is assuming all available data is *evidence* worth surfacing. This “more is better” mindset leads to cluttered analytics tools that offload interpretation onto users. Even conversational AI interfaces can fall into this, encouraging open-ended exploration without helping users reach decisions.

 

 

The analytics and AI products that succeed take a different approach. They’re designed around decision-making to reduce uncertainty, fit into real workflows, and guide users toward clear actions. In doing so, they bridge the gap between analytical capability and real-world value, making the product’s intelligence tangible, usable, and worth paying for.

 

 

Highlights/ Skip to:
 

The core mistake I see people making during the discovery process of building an insights product (2:07)

Improve your product strategy by working ‘backwards” and understanding what decisions customers are trying to make  (6:06)

Insights don’t equal decisions in the real world (7:39)

Designing with a goal of improving the lives of users in mind  (11:17)

Prototypes as a means of discovery (vs. product/solution validation) (13:48)

The bias of data availability (20:39)

Using AI and LLMs for discovery and product UX (24:17)

Why AI-assisted analytics products should shape UX around making structured decisions (31:03)

Overcoming the Invisible Intelligence Gap  (34:57)

Final thoughts (37:21)

 

Links

CED: My UX Framework for Designing Analytics Tools That Drive Decision Making https://designingforanalytics.com/ced  

Need my help finding the right use cases for your analytics or AI product?  Book a complimentary 1x1 discovery call with me: https://designingforanalytics.com/contact/

I’ve worked with a lot of teams building analytics and insights products and decision-support systems. The pattern I keep seeing isn’t that the math is wrong or the ML / AI models are weak. Much of the time, the technology is fine.

 

 

The challenge is that all that [not always artificial!] intelligence is not surfacing as value to your customer. Dashboards look impressive. AI features demo well. Pilots get strong reactions. And then… usage stalls. Sales cycles drag. Teams quietly revert to spreadsheets. Buyers, or rather, prospective buyers, say they “like the vision,” but deals don’t move into the “closed” stage.

 

 

If your gut tells you the primary blocker is not your sales process, pricing/packaging, procurement, data quality, or risk/compliance, then you may be suffering from what I call the Invisible Intelligence Gap. 

 

 

Your product’s intelligence simply isn’t visible to them. Three forces tend to amplify this gap. First, the value translation gap, which is when buyers and users can’t easily connect insights to their own goals. Second is the workflow alignment gap resulting from the product not fitting how work actually gets done. Third, the trust and control gap involves users lacking confidence in how the system reaches conclusions. My frameworks like CED, FOWA, and MIRRR are designed to close these gaps by making value obvious, workflows smoother, and AI more trustworthy.

 

 

Highlights/ Skip to:

The challenge of insights not providing value to buyers, end-users, and stakeholders (3:20)

How the invisible intelligence gap manifests itself (6:42)

Common symptoms of the invisible intelligence gap (8:10) 

Examples of how changes in human behavior cause the gap (10:00)
The (3) amplifiers of the invisible intelligence gap (11:47)

The CED framework for addressing the intelligence gap problem (18:28)

Addressing the invisible intelligence gap with FOWA (20:14)

Using MIRRR to solve the invisible intelligence gap (21:25)

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)

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)