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What Enterprises Still Don’t Understand About AI Risk
Wednesday, 18 March, 2026
AI adoption is accelerating, but many organizations are discovering the same problem. The technology is moving faster than the data foundation required to support it.On this episode of Ctrl + Alt + AI, host Dimitri Sirota speaks with Scott Wimberly, Senior Manager for Data & AI at Accenture, about why enterprise AI success still depends on disciplined data management.Scott explains how the shift from traditional machine learning to generative AI has exposed weaknesses in how companies manage their data. Fragmented systems, poor governance, and inconsistent data models make it difficult for organizations to trust AI outputs.The conversation explores how enterprises can address these challenges through clearer data ownership, better governance, and practical approaches that focus on solving smaller problems first. For security leaders, data teams, and AI practitioners, the discussion offers a grounded view of what it takes to turn AI investments into real business results.In this episode, you’ll learn:How early excitement about generative AI outpaced enterprise data readinessHow legacy systems and fragmented data environments create major barriers for AI programsWhy enterprise leaders should focus on measurable outcomes and ROI when investing in AIThings to listen for: (00:00) Meet Scott Wimberly(01:32) Why AI and data strategy must go together(02:53) How AI evolved from ML to generative models(05:10) Moving beyond chatbots to real AI decision systems(06:05) Why data ownership matters more than traditional stewardship(07:44) The growing importance of unstructured data for AI(13:42) LLMs, SLMs, and the rise of enterprise AI agents(15:11) How MCP connects enterprise data with external models(17:06) Why legacy systems make AI adoption difficult(20:15) Why ROI still determines whether AI projects succeed(22:16) Solving AI challenges one problem at a time
