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The State of AI in the Enterprise

Based on a global survey of 3,500 senior leaders, our report reveals how AI is reshaping the way enterprises work, grow, and create value. Learn why leaders should consider building differently across operations, decisions, accountability, and cost.

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Seven key findings

1. Faster, not different

81% of surveyed leaders say AI has improved speed of decision-making and execution, yet only 25% are deeply transforming the business. Speed is becoming common. Deeper transformation is not.

2. The bill is visible. The unit cost isn't.

71% track total AI inference and API spend, but only 44% track per-unit inference cost. The total bill is visible. What sits underneath it is much less so.

3. The revenue gap is narrowing

28% now report revenue gains from AI, up from 20% in January. The report points to a value story extending beyond productivity to operational, customer, and innovation outcomes.

4. Work is changing, but full transformation remains rare

60% have redesigned jobs around AI to at least a moderate degree. Only 15% describe those changes as extensive of complete.

5. Accountability is becoming more important as autonomy expands

40% allow AI to execute decisions or actions without human approval to at least a moderate degree. For 23% accountability for a negative outcome remains unclear.

6. Physical AI adoption is rising faster than expertise

At least moderate physical AI use is expected to rise from 25% today to 48% within two years. Only 19% rate their expertise as high or very high.

7. Control is becoming part of the AI strategy

74% say data residency and in-region compute are at least moderately important to their AI strategy. Another 62% are at least moderately concerned about reliance on foreign-owned AI.

Actions for leaders

Six moves for turning AI momentum into business value

01.

Redesign the work, not just the workflow

Use AI-accelerated processes as an opportunity to rethink the work itself, including ownership, authority, and where human judgement matters most.

02.

Trace cost to the token, not just the total bill

Track AI-spend from the enterprise bill down to individual workflows, models, and token usage so leaders can understand what drives cost and value.

03.

Connect AI initiatives to the commercial decisions that produce revenue

Tie revenue-focused AI initiatives to specific commercial levers—such as pricing, demand, conversion, retention, or new offerings—and empower business owners to act.

04.

Match governance to the autonomy AI is actually given

Set approval, monitoring, escalation, and accountability based on what each AI system can recommend, execute, or decide.

05.

Build physical AI expertise ahead of the deployment curve

Develop multidisciplinary capabilities across robotics, sensors, systems integration, safety engineering, and operations ahead of wider deployment.

06.

Treat sovereignty as an ongoing strategic input, not a one-time fix

Reassess trade-offs among geography, control, capability, cost, and resilience as providers, regulations, and geopolitical conditions change.

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Contact us

Beena Ammanath

Executive Director
Global Deloitte AI Institute

Jim Rowan

US Head of AI
Deloitte Consulting LLP

Nitin Mittal

Global AI Leader
Deloitte Consulting LLP

Costi Perricos

Global GenAI Business Leader
Deloitte LLP

The State of AI in the Enterprise