AI adoption is accelerating -- 82% of organizations use it -- creating urgent governance challenges. This AuditBoard and Panterra report, based on survey data from 400+ GRC professionals, reveals the reasons why actual implementation lags and offers five strategic actions to build a scalable, effective AI governance program.
With 82% of organizations reporting moderate to extensive deployment of AI tools across functions, AI adoption is no longer speculative. It’s operational. Organizations across sectors are racing to integrate generative and machine learning tools into their core business processes, seeking productivity gains and competitive advantages. But this momentum has triggered a parallel challenge: managing the associated risks.
Business leaders are now tasked with building robust AI governance programs at a time when the technology, threats, and regulatory expectations are all evolving rapidly, and without a consistent benchmark for what “robust” governance really looks like. The result is a growing disconnect between policy creation and policy execution.
Nearly all organizations surveyed report awareness of regulatory developments and express deep concern about AI-related risks. Yet implementation lags behind. Many have drafted policies, but few have embedded AI governance into their organizations' operational fabric. This "policy-practice gap" is emerging as a new risk frontier, one rooted not in ignorance but in executional uncertainty, cultural fragmentation, and misaligned ownership.
This report draws on survey data from 412 GRC and audit professionals to examine:
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Why this gap exists despite high awareness and urgency
- How cultural and structural factors are greater barriers than technology
- Where organizations are overconfident and underprepared
- What practical steps can help embed governance into day-to-day operations
Download your copy today and turn your AI policies into effective, real-world control.
