Enterprise AI programs are rapidly expanding but struggling with governance and ownership issues. Most lack a clear, centralized owner accountable for AI oversight across multiple platforms, resulting in a “control gap” where ambition and spending outpace visibility and cost management. About 85% of organizations run multiple AI platforms competing for primacy, yet only 10% employ active monitoring and alerting for model failures—most rely on manual reviews. Shadow AI and unauthorized agentic usage are common, causing significant financial and operational risks. Despite growing AI portfolios, organizations often face fragmented accountability, limited automated detection, and disappointing returns on custom model investments. The core barrier to effective AI governance is the absence of a dedicated accountable owner, not technology or spending. A shift towards centralized ownership and better cross-platform controls is essential to close this widening control gap.
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