The AI governance gap: Why growing confidence is outpacing real accountability

Ask 10 business leaders who should be held responsible when an AI system produces a harmful or incorrect output, and you will get at least five different answers. That is not a hypothetical. It is exactly what happened when ABBYY surveyed 1,200 senior managers across six countries for the 2026 ABBYY State of Intelligent Automation report, Who Answers for AI: The Governance Gap.

This finding is worth sitting with for a moment. AI is now embedded in business-critical processes across enterprises worldwide. AI is influencing hiring decisions, credit approvals, customer interactions, and supply chain forecasts. And yet, the people deploying it cannot agree on a fundamental question: when the technology fails, who is accountable?

This is the governance gap. It is not a gap in ambition or investment. It is a gap in clarity, oversight, and trust, and it is widening faster than most organizations are prepared to admit.

AI is outpacing the ability to govern it

The core tension in the data is simple. Adoption is accelerating. Governance is not.

More than half of business leaders, 54%, say AI is being adopted faster than their organization can effectively govern it. That single statistic reframes much of the current conversation about AI. The urgent risk is not that businesses are too cautious. It is that they are moving quickly while the guardrails lag behind.

This creates a compliance exposure that compounds over time. Every new model, workflow, or automated decision added without corresponding oversight increases the surface area for something to go wrong, and decreases the odds that anyone will know who is meant to fix it.

Nobody agrees on who is accountable for AI

Here is where the governance gap becomes most visible. When asked who should bear responsibility when AI produces harmful or incorrect outputs, business leaders splintered:

• 31% believe responsibility should be shared between the organization and the vendor.

   

• 26% say the organization using the AI should bear primary responsibility.

   

• 20% place it fully with the AI vendor.

   

• 12% point to the end user.

   

• 10% believe regulators should take the lead.

   

There is no majority view. No dominant consensus. Just fragmentation.

Fragmented accountability is dangerous for a straightforward reason: when responsibility is distributed across five possible parties, it effectively belongs to no one. In the event of a serious AI failure, that ambiguity turns into finger-pointing, delayed response, and regulatory exposure precisely when speed and clarity matter most.

The problem deepens inside organizations, too. Knowledge of who is responsible for AI drops sharply the further down the org chart you look. Leaders report that 89% of senior leadership knows who owns AI development, implementation, and management. That falls to 84% at middle management, and just 65% among junior employees, the very people often closest to how these systems operate day to day.

A junior employee encounters a critical issue in an AI system, but confusion arises because only 65% of their peers are aware of who is responsible for managing it. This lack of clarity delays resolution and increases operational risks.

Meanwhile, senior leadership, with 89% familiarity regarding AI ownership, remains far removed from the day-to-day challenges, creating a disconnect between strategic decisions and practical implementation.

The trust deficit behind the numbers

The accountability confusion sits on top of a broader trust problem. Business leaders are not yet confident in the systems they are deploying:

• Only 26% completely trust AI systems to produce accurate outputs.
• Only 26% completely trust AI to explain how important decisions were reached, or to protect confidential information.
• Just 22% completely trust AI to operate without introducing unacceptable risks.

These are strikingly low numbers for a technology already operating inside core business processes. They reveal an uncomfortable reality: organizations are scaling tools they do not fully trust, without a clear agreement on who answers when that trust proves misplaced.

Governance is being treated as a brake, not an engine

One reason the gap persists is a widespread misconception that governance and innovation are opposing forces.

70% of business leaders say data governance requirements, the controls that keep data accurate, secure, and compliant, are slowing AI deployment. A further 34% say ethical concerns considerably slow their AI initiatives.

Governance, in other words, is frequently experienced as friction.

That friction has consequences.

Less than half of leaders, 47%, say their AI initiatives have exceeded expected targets, and 15% say performance is falling below expectations. When we asked what stands in the way of stronger returns, the answers were revealing:

• Data quality was the top barrier, cited by 20% of leaders.
• Integration with existing systems followed at 15% .
• Implementation costs came in third at 13% .

Notice what tops that list. The biggest obstacle to AI ROI is not the sophistication of the model. It is the data and trust underneath it.

AI requires organizations to know where data comes from, whether it can be trusted, how it can be used, and where it is stored and processed. As data sovereignty grows in importance, a shift toward machine-readable controls that govern data throughout its lifecycle is required to ensure consistent compliance.