Experiencing Data w/ Brian T. O’Neill

I've been seeing a recurring pattern with companies selling APIs, MCPs, data feeds, and other developer-focused AI products. While the technology is often sound if not impressive, sales momentum sometimes slows when prospects have to imagine how the product will create value in their own environment. My perspective on this is that the flexibility that makes these tools powerful can also make them harder to evaluate.

Flexibility can adversely increase the Invisible Intelligence Gap, and I think certain types of AI-based solutions (LLM) may actually increase this because the boundaries of the product are often so much wider than ever before (if not invisible to the buyer). So, how to close this gap? Well, one way is to build a visual UI that showcases what’s possible with your API/feed/data solution. You take the buyer out of the conceptual space and make things concrete. So today, that’s what we dig into: when to consider adding a UI, how far you need to go with it, how you can use Copilot/AI agents to help customize these example implementations, and the benefits you might see. 

It’s a common pattern for teams building B2B analytics and AI products: the proof-of-concept goes well, the buyers sound excited, and everyone assumes the deal is about to close—until it quietly stalls out. The assumption is usually that sales needs to follow up harder or marketing needs more enablement material. But often, the real issue is that the product itself cannot communicate its value without humans in the room explaining it.

I call this the Invisible Intelligence Gap. Buyers may understand the promise during a guided demo, but once the sales engineers leave, customers are left trying to figure out workflows, use cases, trust concerns, integrations, and organizational fit on their own. This gets even harder with broad, general-purpose AI tools and chat-based interfaces that sometimes assume users already know what to ask.

The solution isn’t simply shipping more features or training content. It’s designing products that clearly reveal their value, reduce customer effort, and continue selling themselves after the POC ends, and getting that design right starts with the right product strategy. 

If you’re hoping that adding AI to your analytics product or capabilities is going to unlock new revenue, sales, and greater user adoption, but you’re not sure what’s involved in this transformation, this episode is for you!

Today, I’m talking with Juan Sequeda today, an expert in knowledge graphs and ontologies who most recently was Head of the AI lab at data.world, which was recently acquired by ServiceNow. Juan and I met while speaking at CDOIQ a few years ago, and after being on his former podcast “Catalogs and Cocktails.” (With a name like that, I naturally had him out to my local tiki bar while visiting Cambridge!)

Talk-to-your-data products – effectively next-gen business intelligence applications – are a hot topic right now, and this has made much of Juan’s PhD work in semantics highly relevant right now as companies try to make analytics more user-friendly via natural language. 

Juan is clear that the starting point for this transformation isn’t the model or the UI, but actually the customer’s workflow—and that was like music to my ears! Analytics only matters when it drives action, so the real challenge is not answering more questions, but enabling better decisions and outcomes.

A key theme is semantics, which, in product design language, I think of as making users’ mental models of their business or domain map logically to system and data models so that AI produces the right answers in the right context. Juan outlines a practical path to getting started with this: strong data modeling, a well-defined semantic layer, buy-vs-build considerations, and throughout, a constant focus on what the customer’s workflow and problem is.

Speed is often confused with good product thinking. The idea is that if teams can ship prototypes, dashboards, and models faster, they will automatically learn faster. But execution speed alone doesn’t ensure a clearer understanding of what’s actually worth building.

Instead, teams often fall into a loop driven by demo feedback. They present working prototypes, and users respond to what they can see in the form of interface design, visualizations, or surface-level data behavior. While this feedback feels positive, it’s often misleading. Teams can end up reacting to presentation (UI) feedback only to find it does not change propensity to buy or increase user adoption.

The key idea today is that prototypes can either be used to clarify the problem space and user needs or to validate the solution presented. Where I see most teams fail is that every artifact or prototype is seen as a solution to validate, and they can miss the forest for the trees.

Another approach borrows from blue ocean thinking, which focuses on creating value by looking for overlooked opportunities in the empty space—beyond the known “problem space” your customer knowingly lives in now. 

Because AI lets us move so fast with prototyping, I think there is an exciting possibility to explore the blue-ocean spaces where your product could evolve to produce value. 

As always, we seek to go beyond building “technically right, effectively wrong”—which doesn’t make people buy, use, or refer your product.  Today, we look at what AI can help us to do to see even farther beyond the immediate problem space. 

I’ve seen this challenge again and again with teams building analytics and AI products: nobody can define what quality to the end user means or how to measure. The answer? “Adoption.”  The problem is that “amount of usage” tells you nothing useful about your customer’s experience with your product beyond “it’s not zero.” So what should you be measuring instead so your buyers don’t quickly abandon once the end users get their hands on the keyboard (or agent!)?

The answer is to understand through qualitative measures what users’ experiences are like now, so you have an objective baseline from which to compare future product investment. When you can define their current experience’s quality, it’s much easier to imagine their better future, and you also now have a change you can measure. Measurable outcomes are the foundation of high-value, sticky B2B analytics and intelligence products—and when your end users’ lives are improved, the sales close, and the renewals aren’t questioned. So today, I jump into “how do you measure UX?” so you aren’t surprised when the sale doesn’t close or that renewal doesn’t come through unexpectedly. 

I'm talking with Steve Ancheta, CEO of Zig, a platform designed to free sales teams from repetitive, non-revenue-generating tasks. CRM and logistical tasks can consume up to 72% of the week of a sales team, but Zig’s AI agents handle them so reps can focus on closing deals. Unlike tools built for managers, Zig follows a rep-first design—simple, intuitive, and aligned with the motivation to sell more—while also creating an intelligence layer that preserves institutional knowledge and accelerates onboarding for new hires. 

I wanted to chat with Steve about how he built a product that is both used—and worth paying for—with AI under the hood. Rather than relying on chat prompts, Zig surfaces prioritized tasks in panels and cards, integrates with CRMs and Slack, and builds confidence scores from user interactions. 

Because Steve comes from the world of sales—and that’s the domain his product sits in—I wanted to explore his “problem clarity” and share that with you, since I often find data and technical founders to be more solution-oriented and lacking in this area.  Steve was an open book with me, and I’m hoping other founders trying to turn analytical complexity into commercial clarity can see how Steve is using AI and agents to make data work for end users—and worth paying for.

Finally, I also challenge Steve to answer whether Zig.ai is a software company or a services company with a product behind the scenes—a question you might also ask yourself depending on your GTM model.

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.

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:

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:

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: