Enterprise AI deployment is expanding, but organizations are struggling to translate adoption into measurable business value, with governance and data readiness emerging as major barriers, according to Accenture’s inaugural CIO Pulse survey, published November 3. 

The survey gathered responses from 300 chief information officers (CIOs) and senior technology leaders across 19 industries and three regions.

Accenture conducted the survey during August and September 2026 among senior technology leaders at organizations with more than $1 billion in global revenue across the Americas, EMEA and Asia-Pacific.

The survey found that 64% of respondents said their organizations were scaling enterprise AI deployment, while only 7% had reached the stage of optimizing AI at scale. 

AI use also remains concentrated in individual productivity: 70% said their organizations primarily use AI for productivity and copilots within workflows, compared with 37% using it for end-to-end orchestration through agents.

AI Governance continues to be a significant obstacle. Fifty-four percent of respondents identified governance, risk and responsible AI concerns as a leading barrier to scaling AI, yet only 32% said their organizations had reached advanced or leading AI-governance maturity.

Data-related challenges were almost as prominent. Fifty-three percent cited poor data readiness and quality, data sovereignty or regulatory constraints as barriers to scaling AI, while 51% pointed to the absence of a trusted and governed data foundation.

Enterprise Architecture Is Not Keeping Pace With Agentic AI

The survey also highlighted a gap in how organizations are preparing their technology architecture for agentic AI. While 61% of CIOs expect significant transformation or a fundamental redesign of enterprise architecture within three years, only 27% said agentic architecture was currently included in those plans.

The findings suggest that many organizations are modernizing their data, cloud and security infrastructure without yet accounting for the orchestration, integration and access-control capabilities required to support AI agents across business functions.

Cybersecurity and operational controls are also developing unevenly. Seventy percent of respondents said their organizations were building agent-performance monitoring capabilities, 66% were developing human-escalation mechanisms and 61% were implementing agent governance and access controls.

Data Governance Becomes Central to Enterprise AI Readiness

For chief data officers (CDOs), the findings reinforce the need to treat data readiness as a core part of enterprise AI strategy. 

Reliable data, consistent definitions, clear ownership and appropriate access controls are essential when AI systems begin retrieving information and coordinating actions across different business functions.

The survey also points to a need for closer collaboration between data, technology, security and risk teams. As organizations move toward agent-based workflows, data governance must account for how agents discover, access and use information, alongside the quality and provenance of that information.

Accenture’s findings also suggest that governance investment is not keeping pace with deployment. CDOs can help close this gap by working with business and technology leaders to establish trusted data foundations and integrate governance into AI architecture from the outset.