You Can't Govern What You Can't See: The Shadow AI Crisis with Daniel Ikem

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Responsibility breaks where AI moves fastest, and that’s exactly where we go today. Grant sits down with Daniel Ikem—strategic operator at the intersection of emerging technology, intellectual property, and public policy—to unpack how shadow AI, data limits, and legal gray zones collide inside modern organizations. From boardrooms pushing Copilot to teams quietly pasting prompts into other models, we trace how governance cracks form and why documentation, auditability, and accountability must evolve as quickly as the tools.

Daniel shares firsthand insights from big-tech partnerships and from founding the Diverse IP Alliance, where he’s helping HBCU and underrepresented students build fluency in AI and IP. We examine the core challenges leaders face: capturing tacit knowledge that models can’t see, preventing biased historical data from influencing outcomes, and defining ownership of outputs when proprietary data mixes with external systems. We also tackle the jagged frontier of agentic AI—who’s liable when autonomy kicks in—and the geopolitical reality that makes “slow down” easier to say than to implement.

You’ll walk away with pragmatic steps to act now: set clear policies on approved models and data access, capture critical processes that were never written down, design human-in-the-loop review for high-impact decisions, and build a living risk register that survives model updates. We compare U.S. uncertainty with GDPR and the EU AI Act to show where global benchmarks can guide you before domestic rules arrive. Above all, we make the case that governance is not just compliance—it’s strategy, trust, and long-term resilience.

If you care about AI governance, IP risk, bias, and building a talent pipeline that reflects the communities your systems will serve, this one’s for you. Subscribe, share with a colleague who’s wrestling with AI policy, and leave a review with your top governance question so we can tackle it next.

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