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’m talking to Bhaskar Sunkara, CEO of bicycle.AI, which provides an AI analyst product designed to monitor revenue-critical KPIs, investigate the business and technical drivers behind KPI changes, and take a “governed next step.” Bhaskar explains why analytics products often fail when they overwhelm users with telemetry instead of focusing on the signals that matter. Drawing from his experience as founding CTO of AppDynamics, he shares how his team moved from low-level technical monitoring to business transactions like logins, checkouts, and bookings. The key lesson? Start with the right metric at the right level of granularity, then use deeper technical analysis to explain why something changed.

Bhaskar also breaks down how bicycle.AI serves multiple audiences inside an enterprise. Business leaders want measurable outcomes, KPI owners need answers about what changed and what to do next, and data teams require trust, governance, and traceability. He explains how, in order to support these different users, Bicycle separates product experience into four core surfaces: pull features like dashboards and chat, and push features like alerts and data stories. Alerts further help operational users respond quickly to KPI changes and data stories provide executives with strategic narratives around trends, causes, and business impact. During our chat, Bhaskar also draws a line most AI products blur: be explicit about which findings are deterministic and which are only a theory. He connects this directly to my CED framework, separating the conclusion from the evidence from the underlying data, and argues that how much you automate should be governed by one question: how costly is being wrong?

I also probed Bhaskar about their moat. He’s learned that enterprise adoption requires winning over both executives who care about revenue impact and analytics teams that need confidence in the system’s recommendations. Bhaskar also explains why their long-term advantage comes from the DEAL framework: Detect, Explain, Act, and Learn. By continuously incorporating validated decisions, business context, and customer-specific knowledge, the platform becomes more useful over time. We finish up with his advice for fellow analytical AI product founders, including why AI makes user experience more important, not less: it is the connection between agents, decisions, humans, and accountability.

Highlights / Skip to:

• Making the invisible feel urgent enough for customers to buy products (2:41)
• How to avoid creating the ‘metrics toilet’ when the system can do so much (6:56)
• Designing for the end-user versus the buyer, especially during the POC phase (12:20)
• Thinking about the product’s design in a way that ensures Bicycle’s business value is obvious (15:38)
• How bicycle.AI’s “push” and “pull” features help stakeholders see value (20:54)
• Getting their first 20 customers (25:19)
• What Bhaskar got wrong: over-rotating on the business buyer vs. the analytics team (32:23)
• The homework a build-anything horizontal platform imposes on customers (and Bicycle’s vertical antidote) (34:30)
• Bicycle.AI’s moat: compounding institutional knowledge (36:08)
• DEAL: Detect, Explain, Act, and Learn (40:46)
• How they designed the UX to reduce time-to-value during onboarding/setup (44:51)
• Bhaskar Sunkara’s advice for other analytical AI founders (and why AI makes UX even more important to address) (47:47)

Links

• bicycle.ai
• Bhaskar Sunkara’s LinkedIn
• My CED framework for advanced analytics products that Bhaskar references in this episode

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