State of AI in the Enterprise 2026: Deloitte Maps the “Untapped Edge” of Enterprise AI

The State of AI in the Enterprise 2026: The Untapped Edge report from Deloitte captures a defining moment in how organizations around the world are engaging with artificial intelligence. Drawing on insights from 3,235 director- to C-suite-level business and IT leaders across 24 countries and six industries, the report shows that while AI adoption is accelerating rapidly, most enterprises remain caught between experimentation and true transformation.

At the heart of Deloitte’s findings is a widening divide: access to AI tools is expanding quickly, but the ability to turn that access into sustained, organization-wide impact is lagging. How companies close that gap will increasingly determine whether AI delivers incremental efficiency—or becomes a foundation for long-term competitive advantage.

AI Access Is Expanding—But Utilization Still Lags

One of the clearest signs of momentum is how quickly organizations have broadened workforce access to AI. Over the past year, enterprise-approved AI access expanded by roughly 50%, rising from under 40% of workers to nearly 60%. In this context, sanctioned access refers to AI tools that are formally approved, governed, and supported by the organization, rather than informal or off-policy use by employees.

Among more advanced organizations, 11% now provide AI tools to more than 80% of their workforce, signaling a shift toward AI as a standard part of everyday work rather than a specialist capability. Yet access alone is not enough. Even among employees who have approved AI tools available, fewer than 60% use them regularly in their daily workflows, a figure that has remained largely unchanged year over year.

This disconnect highlights one of the report’s central conclusions: AI’s productivity and innovation potential remains significantly underutilized, not because of technology constraints, but because organizations struggle to embed AI into how work actually gets done.

From Pilot to Production: The Scaling Bottleneck

Moving AI from pilots into production remains the most critical—and most difficult—step in capturing value. Today, only 25% of organizations report that 40% or more of their AI experiments have been deployed into production. Encouragingly, 54% expect to reach that level within the next three to six months, suggesting that many see a clear path forward.

The report identifies a recurring “proof-of-concept trap.” Pilots are typically built with small teams, clean data, and limited risk. Production deployments, by contrast, require infrastructure investment, integration with existing systems, security and compliance reviews, monitoring, and long-term maintenance. Use cases initially scoped for three months can stretch to 18 months or more once real-world complexity emerges.

Without a coherent strategy for scale, organizations risk pilot fatigue—continuing to experiment while never realizing enterprise-level returns.

Productivity Gains Are Common—Business Reinvention Is Not

AI’s near-term impact is most visible in efficiency and productivity. 66% of organizations report current gains in productivity, 53% cite improved decision-making, and 38% are already seeing cost reductions. These benefits explain why confidence and investment in AI continue to rise.

However, more ambitious outcomes remain largely aspirational. While 74% of organizations hope AI will drive revenue growth, only 20% say it is doing so today. This gap reflects a deeper issue: most companies are still using AI to optimize existing processes rather than to rethink their businesses.

Only 34% of organizations report using AI to deeply transform products, processes, or business models. Another 30% are redesigning key processes around AI, while 37% are using AI at a surface level with little or no structural change. The organizations in the first group are pulling ahead by reimagining how value is created—not just how efficiently existing work is done.

Jobs, Skills, and the Limits of AI Fluency

Despite widespread expectations of automation, 84% of companies have not redesigned jobs around AI capabilities. Within one year, 36% expect at least 10% of jobs to be fully automated, and over a three-year horizon that figure rises to 82%. Yet most organizations have not adjusted career paths, workflows, or operating models to reflect this shift.

Talent strategy remains a weak point. While 53% of companies focus on educating employees to raise AI fluency, far fewer are rethinking roles, restructuring teams, or redesigning career mobility. Worker sentiment mirrors this imbalance: 13% of non-technical workers are highly enthusiastic, 55% are open to exploring AI, but 21% prefer not to use it unless required, and 4% actively distrust it.

The report makes clear that AI does not eliminate the need for people. In many cases, it increases demand for uniquely human strengths such as judgment, oversight, and adaptability—particularly as systems become more autonomous.

Agentic AI Is Accelerating Faster Than Governance

One of the most consequential shifts highlighted in the report is the rise of agentic AI—systems that can set goals, reason through multi-step tasks, use tools and APIs, and take autonomous action.

Today, 23% of organizations use agentic AI at least moderately. Within two years, that figure is expected to climb to 74%, with 23% using agentic AI extensively and 5% fully integrating it as a core operational component. At the same time, 85% of organizations expect to customize AI agents to fit their specific business needs.

Governance, however, is not keeping pace. Only 21% of organizations report having a mature governance model for autonomous agents, even as 73% cite data privacy and security as their top AI risk, followed by legal and regulatory compliance (50%) and governance oversight (46%). The report frames governance not as a constraint, but as the mechanism that enables AI to scale responsibly and with confidence.

Physical AI Moves From Edge Case to Core Operations

AI is no longer confined to software. Physical AI—systems that perceive the real world and drive physical actions through machines—is already embedded in enterprise operations. 58% of organizations report using physical AI today, and adoption is projected to reach 80% within two years.

Regional differences are striking. In Asia-Pacific, 71% of organizations already use physical AI, compared with 56% in the Americas and EMEA. Within two years, adoption in APAC is expected to reach 90%, outpacing other regions. Manufacturing, logistics, and defense lead adoption, but applications now span warehouses, retail, restaurants, and industrial facilities.

Cost remains the primary barrier. Physical AI deployments often require millions of dollars in infrastructure, robotics, facility modifications, and maintenance—far exceeding the cost of AI software alone.

Sovereign AI Becomes a Strategic Priority

Sovereign AI—where AI is designed, trained, and deployed under local laws using controlled infrastructure and data—has moved firmly into the boardroom. 83% of organizations view sovereign AI as important to strategic planning, and 43% rate it as very or extremely important. Meanwhile, 66% express concern about reliance on foreign-owned AI technologies, with 22% highly concerned.

In practice, 77% of organizations now factor an AI solution’s country of origin into vendor selection, and nearly 60% build their AI stacks primarily with local vendors. Sovereign AI is increasingly seen not just as a compliance requirement, but as a source of resilience, trust, and competitive positioning.

From Ambition to Activation

The central message of State of AI in the Enterprise 2026 is clear: AI’s transformational potential is real, but it will not be unlocked by tools alone. Organizations that succeed will be those that move beyond access and experimentation to activation—redesigning work, building governance before scale, modernizing infrastructure, and aligning AI strategy with human capability.

Enterprises today stand at the untapped edge of AI’s potential. The next phase will be defined not by who adopts AI fastest, but by who integrates it most thoughtfully—turning AI from a promising technology into a foundational capability that reshapes how organizations operate, compete, and grow.