The state of AI in 2026: there is a big gap between individual gains and enterprise impact
Organizations are deploying agentic coding tools and coming to grips with the costs of AI while still seeking to capture more of the benefits that their workers are deriving from their individual use of AI.
Key takeaways
- Use of agentic AI is increasing, mostly accounted for by large enterprises. Forty percent of respondents from large organizations (those with annual revenues of more than $1 billion) report scaling AI agents, up from 27 percent last year. The share of respondents from smaller organizations reporting scaling remained flat at 22 percent.
- Many organizations are already scaling software coding agents. About two in ten are scaling them (31 percent at larger enterprises).
- Organizations are using agentic coding tools to build software in-house in lieu of purchasing it. Nearly a third of respondents (32 percent) report that their organizations have decided against buying one or more software products or features because they could be built internally with agentic coding tools.
- The cost of AI is constraining usage in some organizations. About 20 percent of respondents report that AI-related operating costs (including token costs) constrained their AI use. But the majority plan to increase their AI investments.
- The share of respondents reporting enterprise-level financial impact from AI use has not changed since last year. Thirty-seven percent of respondents attribute at least some EBIT impact to AI use (about the same share as last year). And the proportion of AI high performers (those who attribute at least 5 percent of EBIT to their use of AI and describe the technology’s impact as “significant”) has remained flat at about 6 percent of all respondents.
- However, AI use is boosting the performance of individuals at work. Eighty percent of respondents report that AI has improved their individual productivity, and 50 percent report that AI helps them make better decisions.
- Respondents increasingly expect AI to spark workforce declines. Thirty-nine percent of respondents expect AI-related declines in their organizations’ total employment in the coming year, compared with 32 percent last year. (Forty-three percent expect no AI-related change.) But the share expecting workforce declines in last year’s survey was about double the share now reporting actual declines over the past year.
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).
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).
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).
Coding agents are emboldening companies to build their own software rather than purchase it. Nearly one-third of respondents (32 percent) report that their organizations have decided against purchasing at least one software product or feature because they were able to build the functionality in-house using agentic coding tools. These decisions are most commonly reported by respondents working in technology and healthcare, followed by those in professional services and energy and materials (Exhibit 4).
Despite broader use and individual results, AI impact remains concentrated
The survey results show that individual employees are seeing meaningful benefits from AI. Eight in ten respondents say AI has improved their productivity, and about half say it has helped them develop new skills and make better decisions. These impacts are remarkably consistent across organizational levels (Exhibit 5). However, the experience is not universally positive: Mid-level managers and individual contributors are more likely than executives to report AI-related strains. Among mid-level managers and individual contributors, 47 percent say they have experienced one of these negative effects, compared with 31 percent of executives and senior managers.
These individual gains have yet to translate into broad financial impact for organizations. About four in ten respondents (37 percent) report that AI has contributed positively to their organizations’ EBIT, essentially unchanged from 2025—despite growth in the share of organizations scaling AI technologies. But respondents do cite other organization-wide benefits: most notably, improvements in innovation, competitive differentiation, customer satisfaction, and employee satisfaction.1
Yet even respondents who don’t report enterprise-level EBIT impact do say their organizations are seeing financial impact from specific business functions’ use of AI. Respondents most frequently report cost reductions from AI use in supply chain management, service operations, and manufacturing (Exhibit 6).2
Revenue gains, meanwhile, are most often attributed to the use of AI in marketing and sales, followed by product and service development and software engineering (Exhibit 7).
While AI investments are increasing, operating costs are constraining AI use for some
As organizations expand deployment, AI operating costs (including for tokens) are becoming a meaningful consideration—but not yet a widespread constraint. One in five respondents says their organization is limiting AI use because of operating costs, a share that is broadly consistent across organizations of different sizes and across many industries (Exhibit 8).
Those cost constraints are being reported across the full range of AI tools. For each of three tools—AI chatbots, AI agents, and software coding agents—about one in ten respondents say their organizations’ use has been constrained by costs.
Despite emerging cost pressures, organizations are still investing meaningfully in AI. Twenty-eight percent of respondents say their organizations are spending more than 10 percent of their total enterprise-wide budget for information and communication technology on AI technologies. And looking ahead, 60 percent of respondents expect their organizations to increase their AI investments over the next year. Respondents in pharmaceuticals and medical products, insurance, and banking and other financial institutions are the most likely to expect increasing investment (Exhibit 9).
Quotes from McKinsey’s experts
Lieven Van der Veken – Senior partner:
“Not only are larger organizations scaling AI faster, but many are beginning to take greater ownership of their technology and change agendas. Until recently, many leaders assumed that AI was beyond the capabilities of their own technology teams, and that moving quickly meant securing the right external partnership. Now, the tone is changing. Leaders are asking what their organizations need to build AI tools themselves. The rise of software coding agents and in-house development is one clear sign of this broader shift.
This, of course, does not mean companies should build everything themselves or turn away from forging partnerships. It means that leading organizations are engaging partners from a position of agency. The organizations moving fastest are becoming more deliberate about where to buy, where to build, and where to develop enough internal capability to integrate and scale what works. They are also treating operating costs as a design constraint, not an afterthought, and making the deeper changes in workflows required for value realization.”
Dan Tinkoff – Senior partner
“A striking finding this year is the gap between individual gains and enterprise impact. At the individual level, AI is clearly a boon: 80 percent of survey respondents say it has improved their productivity and half say it helps them make better decisions. Yet, only 37 percent of organizations report any positive EBIT contribution, essentially flat compared with last year. What has changed, however, is the human foundation available to close that gap. Employees are building AI fluency—on their own or with assistance from company rollouts of general-purpose AI tools—and that shift matters. The opportunity for business leaders is to harness the energy created by growing employee literacy and to establish an operating model that enables them to lead the end-to-end reimagining of workflows in their areas of responsibility at speed and at scale.”
Michael Chui – Senior fellow:
“CFOs and chief AI officers are increasingly talking about “tokenomics” and the ROI of AI. They’ve discovered that AI isn’t “too cheap to meter” when it comes to agentic software development and complex reasoning tasks that benefit most from frontier models: Even as per-token costs have declined, the number of tokens consumed and generated has increased even faster. But these use cases have demonstrated value, so organizations are planning to invest more, even as they develop new disciplines for optimizing ROI from their AI expenditures. Spend on horizontal AI-enabled tools, such as chatbots, is increasingly managed as a necessary cost of doing business, similar to office productivity and communications tools. AI influences other parts of the IT budget, too: Agentic coding tools are creating a more capable option for moving some software development in-house, rather than buying packaged software.”
More details: The state of AI in 2026 – on the road to ROI
