Organizations locked out of AI’s most valuable markets don’t know it yet

Organizations face a growing challenge in AI governance, particularly in high-stakes industries like healthcare and finance, where decision-level accountability is becoming crucial to maintain market access and comply with emerging regulations such as the EU AI Act. While many companies focus on global explainability, which assesses model behavior in aggregate, there is an increasing demand for local explainability, which requires understanding specific decisions made by AI systems. This shift is necessary to satisfy regulatory, legal, and consumer demands for transparency and accountability. The ability to provide detailed explanations for individual AI decisions is becoming a competitive advantage, as organizations that can meet these requirements will gain access to markets closed to those that cannot. This challenge is not only technical but also architectural, requiring companies to build governance into their AI systems from the ground up. The organizations that recognize and address this governance gap by embedding local explainability and accountability into their systems are likely to dominate the AI landscape by 2030.

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