Experiencing Data w/ Brian T. O’Neill

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

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

Everyone is racing to the same place chasing a limited set of buyers—how will your “AI for BI” product stand out?
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.

Highlights / Skip to:

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)  

Links

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. 

Highlights / Skip to:

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)

Links

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. 

 

Highlights/ Skip to:

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)

Links

Podcast: Ep. 189 The Invisible Intelligence Gap