McKinsey’s 2026 Global Survey on AI shows that organizations are deepening their use of artificial intelligence: they are scaling AI across enterprises, deploying it across more business functions, and adopting a broader set of tools—from chatbots and software-coding agents to agentic systems that can act autonomously across workflows. Individual employees report clear productivity gains, but reported enterprise-level financial impact (EBIT) has not risen in step: 37 percent of respondents say AI has contributed to their organization’s EBIT, essentially unchanged from last year.
The online survey ran from May 4 to June 8, 2026, and collected 1,719 responses from 97 countries. The sample covered a wide range of regions, industries, company sizes, functions, and tenures; responses were weighted by each respondent’s country share of global GDP.
Key figures and trends
- 87 percent of respondents report regular AI use in at least one business function. The share saying AI is scaling across the enterprise rose to 44 percent from 38 percent a year earlier.
- Use of AI across functions increased: 56 percent say their organizations use AI in three or more functions, up from 51 percent in 2025.
- Larger organizations lead adoption: among respondents from organizations with at least $1 billion in annual revenue, 54 percent report enterprise-scale AI, versus about one-third of respondents from smaller organizations.
- Adoption of agentic AI accelerated more among larger firms: the share scaling agents in one or more functions rose from 27 percent to 40 percent among larger organizations, while adoption among smaller organizations remained roughly flat at 22 percent.
Where AI is being scaled
- AI agents are most commonly scaled in IT, knowledge management, and software engineering. Industry patterns persist: technology firms report the most use of agents in software engineering; consumer goods and retail firms report agents most often in marketing and sales; advanced manufacturing firms report use in supply chain, inventory management, and manufacturing processes.
- Chatbots are the most widely scaled tool: 47 percent of respondents say their organizations are scaling chatbots across the enterprise. About two in ten respondents report enterprise-scale deployment of agentic AI and a similar share report the same for software-coding agents.
Costs, build vs. buy, and technology choices
- Coding agents are prompting firms to build rather than buy: 32 percent of respondents say their organizations decided against purchasing at least one software product or feature because they could build it in-house using agentic coding tools. This is reported most often by respondents in technology and healthcare, followed by professional services and energy and materials.
- AI operating costs (including token costs) are an emerging constraint: roughly one in five respondents says their organization is limiting AI use because of operating costs. For chatbots, AI agents, and coding agents, about one in ten respondents reports use constrained by cost.
- Despite cost pressures, investment continues: 28 percent of respondents say their organizations spend more than 10 percent of their enterprise-wide ICT budget on AI technologies. Sixty percent expect to increase AI investments over the next year, with respondents in pharmaceuticals and medical products, insurance, and banking and other financial institutions most likely to expect rising investment.
Employee experiences and workforce effects
- Individual benefits are clear: 80 percent of respondents say AI has improved their personal productivity; roughly half report gaining new skills and making better decisions as a result of AI. These effects are consistent across organizational levels.
- Negative experiences are more common among mid-level managers and individual contributors: 47 percent of those groups report experiencing at least one AI-related strain, compared with 31 percent of executives and senior managers.
- Workforce expectations are shifting upward: 39 percent of respondents expect AI to decrease their organization’s overall head count in the next year, while 43 percent expect little or no change. However, actual reductions over the past year were smaller than anticipated: only 14 percent of organizations using AI reported that AI contributed to an overall decline in workforce size over the past year, versus 32 percent who had expected such declines in last year’s survey.
- Only 13 percent of respondents say AI makes them feel anxious about their career prospects.
Financial impact in numbers
- 37 percent of respondents report that AI has positively contributed to their organization’s EBIT; this share is essentially unchanged from 2025.
- Function-level financial impacts are more visible: respondents most frequently report cost reductions from AI in supply chain management, service operations, and manufacturing. Revenue gains are most commonly linked to AI in marketing and sales, followed by product and service development and software engineering.
Characteristics of AI high performers
- AI high performers—those attributing 5 percent or more EBIT impact to AI and reporting “significant” value—make up 6 percent of respondents, unchanged from 2025.
- High performers differ in how they deploy AI: they pursue growth and innovation as well as efficiency; they fundamentally redesign workflows enabled by AI rather than merely inserting AI into existing processes; and they support deployments with leadership commitment and operational rigor.
- Investment and practices: high performers are much likelier to allocate larger shares of ICT budgets to AI (they are more than twice as likely as others to spend over 15 percent of ICT budgets on AI). More than half of high performers expect to increase AI investments by 10 percent or more in the next year, versus 36 percent of other respondents.
- High performers are also more likely to build rather than buy: nearly half report deciding not to purchase one or more software products or features because they could be built in-house using coding agents, compared with 31 percent of other respondents.
- Risk management: high performers are more active in mitigating AI-driven technical vulnerabilities and unauthorized or unintended actions.
Implications
Organizations are increasingly committed to AI—scaling deployments, adopting more capable technologies, and raising investments—while the share reporting meaningful EBIT impact has not yet grown in step. AI is reshaping decisions about build versus buy and creating new imperatives for managing operating costs. The survey suggests that firms that translate individual productivity gains into sustained enterprise-level financial performance are those that pair AI adoption with business transformation: redesigning workflows, securing leadership commitment, and making sizable, sustained investments.
Authors and acknowledgements
The article’s authors are Dan Tinkoff (global leader, QuantumBlack, AI by McKinsey; senior partner, McKinsey New Jersey), Lieven Van der Veken (global leader, QuantumBlack, AI by McKinsey; senior partner, McKinsey Lyon), Michael Chui (senior fellow, Bay Area), and Tara Balakrishnan (associate partner, Seattle). The authors thank colleagues including Anupama Agarwal, Bryce Blair, Cameron Camozzi, and the McKinsey Health Institute for their contributions. The article was edited by Heather Hanselman.
