The Control Gap: Enterprise AI organizations have an ownership problem, not a technology problem — and most are governing it by hand
Enterprise AI organizations are facing a control gap, with AI portfolios expanding faster than their ability to govern them. Most organizations lack a single accountable owner for AI, leading to challenges in detecting model failures in production. The majority of enterprises run multiple platforms each claiming to be the primary AI layer, complicating governance. Only a small percentage have active monitoring and alerting in place, with confidence in detecting failures relying heavily on manual review. The control gap is further exacerbated by financial and operational failures from autonomous agents, such as shadow AI pipelines. Custom fine-tuning has shown limited ROI, pushing enterprises towards a hybrid vendor posture.
Enterprise AI organizations are facing a control gap, with AI portfolios expanding faster than their ability to govern them. Most organizations lack a single accountable owner for AI, leading to challenges in detecting model failures in production. The majority of enterprises run multiple platforms each claiming to be the primary AI layer, complicating governance. Only a small percentage have active monitoring and alerting in place, with confidence in detecting failures relying heavily on manual review. The control gap is further exacerbated by financial and operational failures from autonomous agents, such as shadow AI pipelines. Custom fine-tuning has shown limited ROI, pushing enterprises towards a hybrid vendor posture.
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