6 Guidelines for Governing AI

For the first 10 years of my career, I worked in product management and data analytics by myself. I wrote database queries that pulled numbers out of corporate systems, built statistical models to predict what customers would buy, and shipped data pipelines that moved information between business systems. I built and scaled analytics teams at Best Buy and Target , studying how customers shop and what stores should stock. Today I lead enterprise AI transformation at Lowe’s , the Fortune 100 home improvement retailer. The goal is not to sell artificial intelligence ; it is to use it to deliver useful expertise at the moment a customer needs it. In retail and other customer-facing industries, virtual assistants can help people address everyday questions—such as how to repair a leaky faucet—while guiding them toward relevant products, services, or next steps. As these capabilities become more common, technology roles are changing. The work is no longer limited to building AI systems ; it also includes defining how they operate: which decisions they can make autonomously, when they must escalate to a person, and which actions must remain off-limits. That shift—from building AI systems to governing them —is coming for anyone who is accountable for what such systems produce. Not the casual user typing into a chatbot but the engineers, product managers, analysts, and business operators who sign off on work a machine drafted. It is the subject of the book I recently coauthored, The Enterprise Brain . I call the change the “governor shift,” from executing tasks yourself to setting th

AI governance is crucial

SOURCE     IEEE Spectrum: AI (tier 1)
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DOCUMENT   5d22295b-1855-4a57-a14c-ec66d498aeb1
           https://spectrum.ieee.org/6-guidelines-governing-ai
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EVIDENCE   2 extracted excerpts
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MODEL RUN  1 run, methodology 1.0.1
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SCORE      0  (Mixed or uncertain)
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CONFIDENCE 60%