The state of AI in 2026: On the road to ROI

Nearly a decade into McKinsey’s survey research on companies’ use of AI, organizations are deepening their use of these technologies. The latest McKinsey Global Survey on the state of AI finds that organizations are scaling AI across the enterprise, deploying it in more business functions, and using a range of tools—from chatbots and software coding agents to agentic systems capable of acting autonomously across workflows. And individual employees are reporting real impact: Eight in ten respondents say AI has improved their own productivity.

However, enterprise-level financial impact hasn’t followed the same trajectory. The share of respondents reporting that AI has contributed to their organizations’ EBIT is essentially unchanged from a year ago, at 37 percent.

The survey also finds that AI-related operating costs are beginning to constrain AI use for about one in five organizations, even as most respondents expect their organizations to increase AI investments in the year ahead. Nearly a third of organizations report deciding against purchasing at least one software product or feature because they can now build them in-house using agentic coding tools, which could be a sign that AI is beginning to reshape how technology budgets are allocated. Meanwhile, workforce expectations continue to shift: A larger share of respondents than in 2025 anticipates AI-related declines in their organizations’ total head count over the coming year. Reported reductions over the past year, however, fell well short of what respondents in last year’s survey had anticipated.

The experience of a small group of high performers points to a path forward. These organizations are distinguished by how and to what extent they deploy AI. High performers pursue growth and/or innovation alongside efficiency; they fundamentally redesign workflows that are enabled by AI rather than insert AI into existing ones; and they back their deployments with the leadership commitment and operational rigor needed to achieve real gains. In doing so, they offer a lesson for the majority of organizations still looking to move from individual productivity to financial impact.

Use of AI is deepening as organizations move beyond experimentation, with large enterprises leading the way

Organizations continue to move beyond AI experimentation. Nearly nine in ten respondents report regular use of AI in at least one business function, but more organizations are moving from isolated deployments to enterprise-scale adoption: 44 percent now report that AI is scaling across their enterprise, up from 38 percent a year ago (Exhibit 1).

Exhibit 1

Organizations using AI are increasingly reaching the scaling phase.

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AI is also reaching more parts of the enterprise, with the share of respondents saying their organizations use AI in three or more functions increasing from 51 percent to 56 percent.

As we have seen in previous years, AI deployment by larger organizations continues to outpace deployment by smaller organizations. Fifty-four percent of respondents from organizations with at least $1 billion in annual revenue report scaling AI across the enterprise, compared with one-third of those from smaller organizations.

These larger organizations have moved more quickly than smaller ones over the past year in their use of agentic AI. The share scaling agents in one or more functions increased from 27 percent to 40 percent, while adoption among smaller organizations remained essentially flat, at 22 percent (Exhibit 2).

Exhibit 2

Larger companies are moving from experimentation to scaling AI agents much faster than smaller ones.

Sidebar

Use of agentic AI varies by industry

Among AI tools, chatbots are the most widely scaled, with 47 percent of respondents saying their organizations are scaling them across the enterprise. About two in ten respondents report reaching the scaling phase across their organization with AI agents and a similar share report the same with software coding agents (see sidebar, “Use of agentic AI varies by industry”). The results show that larger organizations are ahead in scaling across different types of AI tools (Exhibit 3).

Exhibit 3