Does the value of your insights, analytics, or automated intelligence product sometimes feel invisible to buyers and users? Does your product have impressive analytics and AI technology, but user adoption and sales still are not where you want them to be? While it has never been easier to build data-driven products, why does it still seem so hard to build indispensable data products that users can't live without—and will gladly pay for? I’m Brian T. O’Neill, and on Experiencing Data — a Listen…
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Recently, I met David Krauza, VP of Enterprise Data Strategy and Products & Governance at Comcast, at the 2026 CDOIQ symposium, and after chatting for a bit, he agreed to come on the show to talk about how he, as an enterprise buyer, thinks about B2B software purchases in the age of AI. As vibe coding makes internal development more accessible, David explains why the buy-versus-build decision…
I'm talking to Nishkam Prabodh of Venture Guides, an early-stage venture capital firm focused on infrastructure software, cybersecurity, and data. Nishkam explains why strong technology alone rarely determines whether a startup succeeds. Many technical founders struggle when transitioning from founder-led sales to a repeatable go-to-market motion because the founder's deep customer understanding…
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…
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…
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…
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…
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…
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…
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…
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…
