ABBYY Study Finds Business Leaders Disagree on Who Has Ultimate Responsibility When AI Goes Wrong

• 2026 ABBYY Who Answers for AI: The Governance Gap reveals that governance is struggling to keep pace with innovation
• AI ROI is falling short of expectations and data quality is the top reason
• 70% say data governance requirements slow AI deployment

New global research commissioned by ABBYY reveals that AI is delivering value, but business leaders are divided over who should ultimately be responsible when AI systems produce harmful or incorrect outputs.

The ABBYY Who Answers for AI: The Governance Gap report surveyed 1,200 senior managers in businesses of 100+ employees located in the US, UK, France, Germany, Australia and Singapore and conducted by Opinium. It found that nearly a third (31%) of business leaders at businesses using AI believe responsibility should be shared between the organization and the vendor. Meanwhile a quarter (26%) believe the organization using the AI should bear primary responsibility, and a fifth (20%) believe it lies fully with the AI vendor. A further 12% believe the end user should be responsible, and 10% believe regulators should take the lead.

Lack of clarity around AI governance and responsibility reflects wider AI concerns

Just over a quarter (26%) of business leaders using AI in their organization say they completely trust AI systems to produce accurate outputs, and the same proportion (26%) completely trust AI to explain how important decisions were reached or to protect confidential information. Confidence falls further when it comes to risk, with just 22% saying they completely trust AI systems to operate without introducing unacceptable risks.

In addition, more than half (54%) of business leaders say AI is being adopted faster than their organization can effectively govern it, highlighting concerns that governance is struggling to keep pace with the speed of innovation.

These concerns are having a tangible impact on the pace of AI adoption. Over a third (34%) say ethical concerns in their organization considerably slow down deployment of AI initiatives, highlighting the challenge businesses face in balancing innovation with responsible adoption.

Data governance needed to scale AI adoption

Data governance — the controls that ensure data is accurate, secure and compliant — is a major barrier to scaling AI, with 70% of business leaders using AI in their organization saying data governance requirements slow AI deployment.

This slowing could be impacting ROI, with less than half (47%) of business leaders at organizations using AI saying their AI initiatives have exceeded their expected targets, and 15% saying AI performance is below expectations. This raises questions about whether businesses are putting the right structures in place to realize the full value of AI.

Challenges impacting AI ROI

Concerns around data quality are the biggest barrier to achieving greater ROI from AI, with a fifth (20%) of business leaders citing it as their top challenge. This is followed by integration with existing systems (15%) and implementation costs (13%).

Part of the challenge may be a lack of shared understanding within organizations. Knowledge of who is responsible for AI decreases the more junior employees are. Business leaders at organizations using AI say 89% of senior leadership knows who is responsible for developing, implementing, and managing AI across their organizations, 84% say middle management knows, and 65% say the same for junior employees.

AI governance emerges as a critical factor in successful adoption

Yet the findings suggest that effective governance can play a key role in helping organizations realize greater value from AI. More than three quarters (78%) of business leaders at organizations using AI say governance has made AI initiatives at their organization more successful, while 75% say their organization’s governance approach effectively balances risk mitigation with the need to deliver AI quickly.

The majority of business leaders (82%) whose organizations currently use AI say their organization has a formal AI governance framework in place. However, governance maturity varies, with only half (50%) have an AI incident reporting process, 46% have an AI incident response plan, 35% have an AI kill switch, and 35% have AI rollback capabilities. Just 4% say their organization has no AI incident management capabilities.

More organizations are also planning to strengthen their governance approach, with 15% of business leaders at organizations using AI saying their organization does not currently have official AI governance in place but plans to develop one within the next 12 months.

Human oversight important to AI governance

Those with AI governance frameworks in place are already seeing practical benefits. More than half say governance makes it easier to develop AI systems (57%), scale AI initiatives beyond the pilot stage (57%), and encourage employee adoption of AI systems (59%).

Human oversight remains important, with 56% of business leaders saying humans review all important AI decisions before action is taken. This rises to 61% in Australia, while falling to 49% in Singapore, showing continued variation in how organizations approach human involvement in AI decision-making.