Deloitte is one of the world’s largest consulting firms, advising governments, global corporations, and industry leaders on strategy, technology, and transformation. Because of this position, its research often reflects early signals of where industries are heading. When Deloitte publishes findings about artificial intelligence, operating models, or governance, it usually indicates changes that many organizations will soon face.

The State of AI in the Enterprise 2026 report highlights a key moment in AI adoption. Organizations around the world are expanding access to AI tools and experimenting with new capabilities. At the same time, many companies still struggle to turn experimentation into measurable business results

AI Access Is Growing Across the Workforce

One of the clearest trends is the rapid expansion of AI access.

Within a single year, the share of employees with access to sanctioned AI tools increased from less than 40 percent to roughly 60 percent. Some leading organizations now provide AI access to more than 80 percent of their workforce.

However, access alone does not guarantee value. Among employees who already have AI tools available, fewer than 60 percent actually use them regularly in their daily work. This shows that many companies are still in an early stage of adoption where tools exist but workflows have not yet been redesigned to use them effectively.

The Main Barrier Is Scaling AI Beyond Pilots

Many organizations are experimenting with AI, but scaling it across the enterprise remains difficult.

Only about 25 percent of companies have moved at least 40 percent of their AI experiments into production so far. However, more than half expect to reach that level in the near future.

The gap exists because pilot projects and production deployments have very different requirements. A pilot can run with a small team and limited data. Production systems require:

• integration with existing systems

• security and compliance checks

• monitoring and maintenance

• reliable data pipelines

Because of this complexity, many AI projects stall between experimentation and full operational deployment.

Agentic AI Is the Next Major Wave

A growing trend highlighted in the report is the rise of agentic AI systems. These systems can plan tasks, interact with tools, and execute multi-step workflows. Their adoption will require stronger data foundations, better system integration, and clear governance.

Governance Is Lagging Behind Adoption

AI adoption is accelerating, but governance is not keeping pace.

While many organizations expect to deploy autonomous AI systems in the coming years, only around 21 percent report having mature governance frameworks for these systems.

Because autonomous AI can act directly inside business processes, companies need clear rules for decision autonomy, human oversight, and monitoring of AI actions. Without these controls, scaling AI safely becomes difficult.

Physical AI Is Expanding

Another trend is the growth of physical AI, which connects artificial intelligence with robotics, sensors, and machines.

Today about 58 percent of organizations already use physical AI in some form, and adoption is expected to reach around 80 percent within the next two years.

Examples include robotics in manufacturing, autonomous logistics systems, and intelligent infrastructure monitoring.

Strategy Is Ahead of Execution

Many companies believe they are strategically ready for AI, but operational readiness often lags behind. Data infrastructure, talent, and technology architecture still need significant improvement.

As a result, the main challenge is no longer defining an AI strategy but executing it effectively.

Companies Are Transforming at Different Speeds

Organizations are adopting AI at very different levels.

About 34 percent of companies are using AI to fundamentally transform their business, while around 30 percent are redesigning key processes around AI. At the same time, roughly 37 percent of organizations still use AI only in limited ways without major operational change.

This gap is creating a growing competitive divide between companies that redesign workflows around AI and those that simply add AI tools on top of existing processes.

Conclusion

AI adoption is accelerating, but the biggest challenge is turning experimentation into real operational change.

Organizations that succeed will focus on scaling AI into production, redesigning workflows, strengthening governance, and building the infrastructure needed to support AI at scale.