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.

• 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)

• 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):

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)