DatAInnovators & Builders

What happens when a company built on 16 acquisitions tries to run on one trusted data model? At Precisely, Dave Shuman has spent six years building the data infrastructure needed to take the business from a $300M mid-market firm to a billion-dollar global software company, and the lessons from that journey cut straight to the heart of what makes or breaks an enterprise AI strategy. Dave outlines f...

What happens when half the room at a Gartner executive workshop raises their hand to say they've shipped AI to production and tracked ROI? Conor Jensen, Global Field CDO at Dataiku, uses that moment to reframe the entire "AI is failing" narrative and get specific about what separates the companies making it work from the ones still stuck in prototype mode. Conor walks Saket through t...

Most enterprises are racing to build AI labs while the real problem sits in their data layer. Santiago Guillotti, Chief Data and Analytics Officer at Chubb LATAM, leads a 130-person data organization and his conclusion is direct: no vendor can fix your data problems for you. AI platforms are replaceable. The data you generate is not. Santiago outlines for Saket how GenBI is creating outsized effic...

What happens when data breaks in production and no one sees it coming? At Recce, Dori Wilson is building the systems to make data reviews as systematic as software code reviews, and the gap between those two worlds is bigger than most teams realize. Dori Wilson, Head of Data at Recce, walks Saket through how data validation has historically lagged behind software engineering, why dashboards are be...

Jo Hjersman has spent over five years at a healthcare XR startup wearing every hat on the data stack, from research data scientist to Head of Data. In this episode, he walks Saket through how he built and scaled a behavioral analytics platform for mental health care, navigated a full cloud migration from Azure to GCP with critical systems kept online throughout, and keeps the whole system running...

Christine Pierce spent over 20 years at Nielsen building and running the data systems behind a $600 billion advertising ecosystem. She now consults on media measurement, AI deployment, and data monetization. Her take on enterprise AI cuts through the noise: most deployments fail not because of the technology, but because of change management and misaligned incentives. Christine outlines how the sh...

Most organizations have thousands of dashboards and still can't get a simple answer from their data. Francois Lopitaux, SVP of Product Management at ThoughtSpot, argues that the problem was never the data, it was a fundamental misunderstanding of who analytics tools were actually built for. In this episode, Saket and Francois trace the full arc from dashboard factories to agentic BI, and why t...

What happens when a company runs 15 AI agents across its processes but still cannot measure their impact on the top or bottom line? According to Yorck F. Einhaus, former Global CDO at Liberty Mutual and CDO at Farmers Insurance, that is not an AI problem. It is a data problem, and it is the most common reason enterprise AI programs fail to scale. Yorck shares how he led Farmers Insurance through a...

Most companies think turning on ChatGPT Enterprise and running a few lunch-and-learns counts as AI transformation. Michael Domanic, VP and Head of Generative AI Business Strategy at UserTesting and OpenAI's System Builder of the Year, has spent two years proving otherwise inside an 800-person org. His starting point is a reframe that changes how the whole program runs: there are no more indivi...

Most agents fail in production not because the model is bad, but because they forget everything and can't access the right data at the right time. Rowan Trollope, CEO of Redis, has built his entire product strategy around solving exactly those two problems, and in this episode he gets specific about how. From architecting a semantic layer that sits between your enterprise data and the agent&#3...

Fred Gertz completed his PhD in electrical engineering under the inventor of the modern magnetic hard drive, then left academic research to solve a problem that's stumped manufacturers for decades: how to optimize complex operations when you have almost no data. At Collide Technologies , he's applying swarm intelligence to tackle NP-hard scheduling and routing problems that LLMs fail at sp...

Marcel Santilli built GrowthX to $13 million ARR without hiring an AE until three months ago. His approach: 170 paid workshops at $500+ each validated exactly what the market needed before writing a line of platform code. The methodology behind it came from his time as Deepgram's CMO, where AI workflows plus human judgment generated 3,000 continuously improving pages and helped 4x revenue in t...

Companies turn on Microsoft Copilot or Glean, then shut them off a month later after discovering sensitive data exposure across their environment. Stephen Gatchell , Partner and Head of AI Strategy at Ortecha , explains why this pattern keeps repeating and what it takes to actually get enterprise AI into production safely.  Stephen breaks down the real blockers: unstructured data at petabyte scale...

Most AI agent demos still look great but fall apart in production. At BigPanda, Alexander Page's team solved this by building systems that internalize user corrections and improve without requiring source data fixes. The Engineering Director of Applied AI shares with Saket how his team designs production-grade AI agents for IT operations. When a user flags that step seven of a retrieved runbook is...

Most IT teams burn months integrating business systems. Ashish Thusoo's agents at CurieTech AI deliver 70-80% productivity improvements by changing one thing: the loop shifts from human build, human verify, human refine to AI build, human verify, AI refine. That compression happens because machines can now build and refine while humans focus verification energy where it matters. From co-creating A...

Robin Sutara is Field Chief Data Strategy Officer at Databricks, where she works with organizations facing a common problem: employees sit through AI training, check the box, then nothing changes. The issue isn't awareness. It's that a store manager, plant floor worker, and data scientist need completely different capabilities, but most organizations treat them identically. Robin breaks down why g...