Agentic AI is moving into production faster than most companies can govern it. Agents are already making decisions, touching sensitive data, and acting on behalf of the business. Few organizations have the policy, infrastructure, or measurement in place to keep up. If you’re not sure what AI governance actually covers, start with our explainer on what AI governance is. Below are five specific gaps showing up across enterprises right now, backed by recent industry data.
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The Governance GapAdobe’s 2026 AI and Digital Trends report found that only 37% of organizations have responsible use guidelines in place for agentic AI. That compares to 65% for generative AI. Most companies wrote policy for the AI tools they understood first. Agents outran that policy before it could catch up. How to close it: Treat AI governance improvement as a recurring review, not a document written once and filed away. Revisit it every time a new agent goes into production. |
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The Infrastructure GapOnly 51% of organizations have cloud infrastructure built to support agentic AI, compared to 89% for generative AI, according to Adobe’s 2026 AI and Digital Trends report. Many companies are running AI agents on infrastructure designed for a simpler, less autonomous kind of AI tool. How to close it: Evaluate whether your current AI governance platform can support autonomous agents. Don’t settle for a platform that only monitors generative AI outputs. |
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The Data Gap75% of organizations cite data integration and quality as their top challenge for agentic AI, per Adobe’s 2026 AI and Digital Trends report. That ranks ahead of talent shortages and unclear ROI. Agents act on whatever data they can reach. Fragmented or outdated data leads to fragmented, outdated decisions at machine speed. How to close it: Fix data quality and access before scaling agent autonomy. No governance framework can compensate for bad inputs. |
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The Measurement GapOnly 31% of organizations have a measurement framework for agentic AI, and 47% have no framework at all, according to Adobe’s 2026 AI and Digital Trends report. Without a way to track outcomes, IT leaders can’t tell if an agent is working as intended. It could be quietly drifting off course. How to close it: Build an AI governance framework with defined success metrics before an agent goes live. Don’t wait until something goes wrong to measure it. |
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The Trust GapOrganizations consistently overestimate how comfortable customers are with agentic AI, per Adobe’s 2026 AI and Digital Trends report. Customers rank clear disclosure and the ability to reach a human as their top priorities. Governance that ignores this gap risks building agents customers will actively avoid. How to close it: Build disclosure and human escalation into every customer-facing agent. Test customer comfort directly instead of assuming it. |
Frequently Asked Questions
What is the biggest AI governance gap right now?
Data integration and quality is the most commonly cited challenge for agentic AI, ahead of talent shortages or unclear ROI. Most other gaps, including measurement and infrastructure, trace back to this one.
What AI governance tools do companies actually need?
At minimum, companies need a full inventory of active AI tools and agents. They also need a way to monitor what each one can access, and a measurement framework to track outcomes over time.
How is agentic AI different from generative AI for governance purposes?
Generative AI produces content for a person to review. Agentic AI takes independent action, which means governance has to cover decision boundaries and escalation paths, not just output quality.
Why do customers distrust AI agents even when they work correctly?
Customers place a high value on knowing when they are interacting with AI and being able to reach a human. When disclosure is missing, trust drops even if the agent performed well.
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