Today, I'm talking to Rana Gujral, CEO of Behavioral Signals, which provides AI that interprets human behavioral cues in speech to help route call center conversations more effectively, improve customer service performance, and detect voice-based fraud. Their moat is a decade of voice data tied to real business outcomes, not the model itself, as Rana explains.

During our conversation, Rana shares his practical framework for making the value of their AI obvious to the various humans in the loop that the product needs to “touch,” and he argues that a one size [UI] doesn’t fit all. In Rana’s product, they discovered that customer service reps need ambient assistance, supervisors need aggregate patterns, compliance teams need audit trails, and executives need outcome metrics tied to business results.

He also explains why having measurable ROI isn't enough. Early renewals for Behavioral Signals suffered because the people signing the checks couldn't actually see the product's impact. Rana's solution? “Ship the meter” alongside the intelligence. If your AI works quietly in the background, you still need reporting UIs that clearly communicate the product’s value.

For founders struggling with stalled POCs, Rana breaks down the three-stage evaluation journey his team developed after repeatedly seeing deals fail at predictable moments. By designing the customer experience around those milestones, his team transformed how buyers gained confidence throughout the evaluation process.

Finally, we explored why great B2B AI products don't succeed by becoming another dashboard. Rather, they succeed by closing the loop between decisions, outcomes, and learning. Rana also fills me in on his upcoming book, The AI Instinct, which focuses on how AI changes human judgment rather than simply advancing model capabilities. And his parting advice? Listen to find out!

Highlights / Skip to:

• Making “invisible AI” value clear (3:57)
• The four surfaces of visibility the product team dials into to ensure Behavioral Signals is indispensable to customers(6:26)
• Behavioral Signals’ intentionality behind their three-phase model to address deals not closing (15:35)
• How Rana’s team deals with AI moving downstream problems further upstream (19:56)
• Determining their product’s boundaries: when do you stop building? (22:55)
• Why proprietary data makes for such a good moat (24:57)
• What Rana would do the same and differently if he were starting over (28:45)
• Rana’s book: The AI Instinct: The Future of AI and Human Decision-Making (39:29)
• Rana Gujral’s closing advice (44:35)

Links

• Behavioral Signals
• The AI Instinct: The Future of AI and Human Decision-Making
• Rana Gujral’s website
• Rana Gujral’s LinkedIn

I've been seeing teams heavily invest in copilots, agents, semantic layers, governance frameworks, and increasingly sophisticated models, yet many still hear the same feedback from sales prospects: “We may just build this ourselves?" Or they don’t hear it, but suspect the customer is doing just that.

Whether they actually can DIY the solution is the wrong question. The bigger question is *why they believe they can.* Your product may have a genuine competitive advantage, but your real challenge is that this advantage isn't obvious to buyers. The moat exists, but it is invisible.

What makes this relevant is that many capabilities once considered differentiators are rapidly becoming normalized. AI copilots, agentic analytics, governed data, semantic layers, and broad integrations now appear across nearly every platform in the category. As AI accelerates development, sophisticated engineering alone becomes harder to defend as a lasting advantage.

So what actually creates a durable moat if the engineering and product seems easy to copy? I explore four areas: proprietary data, trusted relationships, and products that accumulate institutional knowledge remain difficult to replicate. And finally, user experience itself as a strategy. As users increasingly access your intelligence through AI agents rather than dashboards, their experience may become the moat that competitors can't copy.

• AI for BI and analytics products is facing a race to commoditization (2:09)
• Common moats that everyone is using right now and why they fail (3:28)
• Proprietary data as a moat (9:29)
• Being embedded in your community as a moat (11:14)
• Compounding institutional knowledge as a moat (15:22)
• UX design asa moat even when there is little/no UI to see (18:36)
• Find the baseline for customer experience to build into later strategies (25:11)
• Actionable questions to ask your team to move forward on finding your competitive differentiation as a B2B analytics product (28:02)

• CED: A UX Framework for Designing Analytics Tools That Drive Decision Making

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.

• The challenges of selling API-based analytics and AI products (0:56)
• Why this topic matters right now (2:48)
• The Invisible Intelligence Gap that may be slowing your sales (3:34)
• Strategies for bridging the Invisible Intelligence Gap with a UI (user interface) layer (7:01)
• Client case study: the impact and results you may see adding a UI on top of your technical product (14:05)
• Signs that you should consider adding UI to your technical product (18:23)
• Leveraging humans’ highly developed visual system to help potential customers see the full value of your product (26:24)
• Conclusion (27:32)

• Invisible Intelligence Gap
• Azeem Azhar’s Exponential View (6/4/26 episode)

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.

• First principles thinking - add sales effort or fix the product? (0:43)
• How the POC phase supports sales efforts (3:28)
• The role of the Invisible Intelligence Gap (5:38)
• What is “buyer’s block” and how to avoid it (6:26)
• Avoiding the “Two-Costs Model” and what that model is! (11:34)
• Overcoming a stalled sales process (13:42)
• Understanding the problem, users, outcomes, and boundaries (14:41)
• Three product strategy moves you can make (17:50)
• Always ask how customers are experiencing the product and if it sells itself (24:04)

• Podcast: Ep. 189 The Invisible Intelligence Gap

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.

• Juan Sequeda’s background (2:14)
• Is AI for BI the way to go for proprietary analytics products? (4:30)
• Bolted-on AI versus transformational AI, and what customers are doing with current reporting (8:26)
• Knowing your product’s boundaries and when extending into adjacent customer workflows stops making strategic sense (14:46)
• Setting proper expectations for non-technical founders around what AI can “answer” with analytics (18:43)
• The role of customer problems in informing the prerequisite technology and data decisions (24:37)
• What's the actual lift to add chat-with-your-data capabilities to a SaaS product: data foundation, semantic layer, and the build-vs-buy call (33:38)
• Why Juan thinks every company should become “AI-native” (41:20)
• AI might theoretically make for a better analytics UX, but are users ready to change their behavior or abandon the analytics tools they use now? (46:00)
• How to follow Juan Sequeda (49:03)

• Catalogs & Cocktails Podcast
• Juan Sequeda’s LinkedIn
• Juan Sequeda’s Substack

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.

• Where the idea for this episode came from (00:46)
• Why faster building of artifacts with AI doesn’t necessarily mean faster market validation (2:09)
• How understanding the problem space results in fewer prototypes being created (5:40)
• Using blue ocean strategy to arrive at new products worth paying for (08:23)
• Finding missed market opportunities blocked by cost, tech limits, or risk (12:39)
• How AI-assisted user research fits into blue ocean thinking (14:33)
• The big picture: winners will figure out what’s worth building before they build it (20:42)

• Contact Designing for Analytics

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.

• Why I think product adoption (i.e. product usage analytics) are misleading as a means to define whether your solution is valuable to users (1:34)
• Getting a better baseline reading of user experience so you can improve their life and your sales/retention KPIs (4:56)
• How to measure, hypothesize, and observe if your product is working “well” (7:35)
• Discovering where your product is being appreciated (20:28)
• What about when AI is in the loop? (23:05)
• The risk of creating bigger messes with AI capabilities (28:20)
• How to gain useful insights from your customer exposure time (31:28)
• The quantitative metrics you can use to help measure UX outcomes (36:17)
• Why "ship it and see if it gets used" isn't a product strategy (40:52)

• More Resources
• Get 1x1 Help from me if you know your product’s value is opaque, or the user experience is hindering your sales or adoption goals

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.

• What is Zig.ai? (00:48)
• When managers see the value of a product but end-users don’t—and how product leaders need to react (5:20)
• What Zig’s UX is like and how it was designed (9:45)
• The sales process and risks salespeople face when demoing Zig (16:12)
• How Zig addressed their time-to-value challenge during the product experience (20:14)
• How Zig found a problem people were willing to pay to solve (24:16)
• We discuss whether an AI product company might be a services company with technology or a traditional software company (24:16)
• The Invisible Intelligence Gap Steve has observed within Zig’s business space (AI and analytics-powered sales tooling) (27:57)
• Why Steve isn’t worried about the major CRMs from building internal solutions to circumvent third-party tools like Zig (35:37)
• Steve Ancheta’s advice for trying to bring sophisticated data products to market (39:26)