Brian talks with Logan Havern, CEO of Datalogz, about enterprise analytics sprawl as AI makes “hundreds of new dashboards” and “junk, hallucinations, slop” faster than anyone can clean up. They dig into a 30-minute connection that delivers instant cost-savings ROI, plus “semantic intelligence” to stop execs “talking to their data” and getting wrong answers.
What happens when your product team suddenly moves six times faster at delivering features, but your sales and business traction is unchanged? Brian T. O’Neill explains why “speed does not equate to value,” why AI evals and acceptance criteria measure the product—not its impact—and how buyers judge your POC on success metrics “your team never wrote down.”
What makes an analytics or intelligence product indispensable when technical sophistication alone isn’t enough? When you believe the underlying technical IP should be enough to speak for themselves, but you’re seeing stalled POCs, flat user adoption, quiet non-renewal, accept that the underlying technology isn’t itself valuable; it’s the perceived value, as experienced by the humans in the loop.
At CDOIQ, I met David Krauza (Comcast) to unpack B2B buying in the age of AI. Sure, you can “vibe code” anything—but “what it comes down to” is the long-term roadmap, integrations, and whether you want to “be in that business.” His first question: “what’s the data” the system “throws off” and can it feed your ecosystem and AI.
I’m talking to Nishkam Prabodh of Venture Guides. He explains why “even if you have the best technology in the world,” startups stall at the founder-led sales handoff—when customer understanding doesn’t translate into repeatable GTM. Communicate outcomes and ROI—“revenue, cost, or risk”—for every stakeholder, and “pick one customer, one industry, one revenue band” to build repeatability.
I’m talking to Bhaskar Sunkara, CEO of bicycle.AI—“an AI analyst that’s assigned to a revenue-critical KPI.” We unpack why analytics fails when it becomes a “metrics toilet,” and why the key is “the right metric, right granularity.” Bhaskar shares DEAL—Detect, Explain, Act, and Learn—plus push/pull surfaces: dashboards, chat, alerts, and data stories.
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. Rana’s solution? “Ship the meter” alongside the intelligence.
Everyone is racing to the same place, chasing a limited set of buyers—so how is your “AI-for-BI” product going to stand out? Prospects saying “we may just build this ourselves” is a diagnostic: you have “no visible moat.” When everyone has the same AI, “the moat isn’t the product’s intelligence, it’s the experience of the intelligence.”
I’ve been seeing a recurring pattern with companies selling APIs, MCPs, platforms, data feeds, and other developer-focused analytical intelligence products: “sales momentum sometimes slows” when prospects have to imagine how it creates value. Flexibility can increase the “Invisible Intelligence Gap.” One way to close it: “build a visual UI that showcases what’s possible”—show, don’t tell.
Your B2B analytics or AI POC “goes really well” — then “the silence kicks in.” Brian calls this the “Invisible Intelligence Gap”: buyers see the promise in a guided demo, but once you leave, the product can’t “continue to do this work” and “buyer’s block” sets in. Fix it as a product strategy problem: “make the value visible” so it sells itself.