2026 Report
State of Enterprise AI:
The AI Value Gap
AI is creating visible operational gains for many enterprises. However, it's not yet consistently producing business and financial value.
2026 AI Adoption & Performance
Most organizations say AI works well and employees are using it. User adoption, agent utilization, governance and model accuracy all rate about expectations.
Operational impact gets mixed reviews, with employee productivity gains coming in slightly below what was expected, and most business and financial outcomes have fallen far short.
Today, nearly a quarter of AI work is reviewed by humans and almost 14% involves humans only for exception handling. The role of the human employee is moving away from completing every task and toward managing the conditions in which AI performs the work.
of AI work is human-led today
This is expected to shrink below 40% by the end of 2027.
will keep increasing AI budgets
If they continue gaining value at the current rate.
average increase in employee decision quality
AI’s strongest workforce impact, level with the creation of new AI talent roles.
Of AI work autonomous by the end of 2027
Nearly double today’s share of less than 7%.
Leading AI Use Cases
More than 40% of enterprises report that AI has generated the most value over the last 12 months through workflow-centered uses.
- Task automation and workflow execution
- Data analysis and insights generation
- Process optimization and operational improvement
Requiring people to validate these functions can create bottlenecks when AI produces results faster than employees can review them. Enterprises can avoid that by applying AI to higher-value roles within workflows, such as monitoring information and gathering context to identify issues for humans to assess.
AI Value Realization is Not a Linear Journey From Adoption to Success
Organizations can use AI extensively and experience very different outcomes.
The differentiator is an organization’s ability to convert AI into value while managing the technology, workforce and economic challenges that come with it.
Let's dive in...
To generate value from AI, enterprises need to modernize their data, simplify the technology stack, redesign the way employees work, clarify the economics of AI and set clear governance guardrails. These changes will help turn today’s AI activity into tomorrow’s measurable business and financial results.

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