Data Governance is Sexy Again: Why Your AI Projects Fail Without It

In this episode of the Disambiguation podcast, host Michael Fauscette talks with Zoher Karu, Head of AI at Taelor, about why data governance has suddenly become a boardroom priority, how dirty data and missing business context cause AI projects to fail, and what practical steps leaders can take to build a data foundation that actually supports AI at scale. Zoher has spent his career at the intersection of data, analytics, and business strategy. He holds a PhD in electrical engineering from MIT, started at McKinsey, then founded startups in retail analytics and call center intelligence before leading enterprise-wide data and analytics organizations at Sears Holdings, eBay, Citibank (across 17 markets in Asia and Europe), and Blue Shield of California. The conversation covers why data governance is "sexy again" (AI amplifies data quality problems, so bad data now means bad decisions at machine speed), the blood-in-the-body analogy for enterprise data (every organ needs it, it should not be dirty or leaking), why a two-year data cleanup project is the wrong approach (clean as you go with a use-case-driven mindset), three reasons AI projects fail to deliver results (data quality and trust, missing business context that lives in people's heads not databases, and change management resistance), the "Susie knows how to do that" problem (business rules and institutional knowledge that AI agents cannot access), why pilot success does not predict production success (manually cleaned spreadsheets do not reflect real-world data), change management and the value exchange (people need to know what is in it for them), why productivity gains are not the same as business transformation (the real power of AI is reimagining processes entirely), cross-industry patterns in data challenges (siloed customer views, fractured definitions, departmental selfishness), how data teams are evolving toward full-stack roles with AI-assisted tools, governance as brakes that help you go faster (knowing the boundaries lets you push all the way to them), and practical first steps for business leaders (start with cost savings, ask employees what would make their job easier, build momentum through small wins). Timestamps: 00:00 - Introduction 00:32 - Episode title and guest intro 00:45 - Zoher's background: MIT, McKinsey, startups, Sears, eBay, Citibank, Blue Shield 03:18 - Business-first mindset for data leadership 05:03 - Why data governance is sexy again 06:18 - Data is like blood: the enterprise body analogy 07:33 - When "revenue" means different things to different teams 08:02 - Clean as you go, not a two-year cleanup project 09:30 - The shift from AI productivity to AI governance 10:00 - Three reasons AI projects fail 10:55 - Missing business context: the "Susie knows" problem 13:10 - Pilot versus production: the cleaned spreadsheet trap 13:38 - Change management and the value exchange 15:07 - Making existing work easier: the call center notes example 15:28 - The real power of AI: reimagining processes entirely 17:34 - Board-driven AI mandates and the fear of being left behind 18:44 - AI is a tool, not a solution looking for a problem 19:32 - Cross-industry patterns in siloed customer data 21:49 - Citibank example: loan data missing time of day 23:40 - Full-stack data teams and AI-assisted tools 26:17 - Building governance that protects without becoming a bottleneck 27:07 - Traffic laws analogy: rules of the road for AI 29:32 - Brakes help you go faster 30:14 - Practical first steps: start with cost savings and productivity 32:06 - Ask your employees what would make their job easier 33:04 - Success builds on success 33:31 - Recommendation: Factfulness by Hans Rosling Guest: Zoher Karu, Head of AI, Taelor Host: Michael Fauscette, CEO & Chief Analyst, Arion Research Subscribe and turn on notifications so you never miss an episode.