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AI is becoming part of everyday business work, but widespread use is not the same as company-wide transformation. In McKinsey’s 2025 survey, 88% of respondents said their organizations regularly used AI in at least one business function; about one-third said their organizations had begun scaling AI programs across the enterprise. The figures describe survey responses, not every business, and they point to a practical distinction: AI is showing up in workflows faster than many organizations are scaling it.
How are businesses using AI in everyday work?
AI is appearing in functions that handle information, customers, products, and internal operations. McKinsey’s 2025 survey identified IT, marketing and sales, and knowledge management among the functions with the most reported AI use. Reported generative-AI applications included conversational tools for handling or retrieving information, support for marketing content and strategy, and some contact-center or customer-service activity. These are examples of reported use, not evidence that a particular application suits every organization or will produce a guaranteed result. McKinsey’s 2025 global survey provides the survey context.
Earlier findings show how use can vary by function and industry. In McKinsey’s 2024 survey, marketing and sales was the most commonly reported generative-AI function overall; patterns differed across surveyed sectors such as technology, professional services, financial services, healthcare, and retail. Product and service development, service operations, software engineering, and IT were also among functions where respondents reported generative-AI use. This variation is one reason a business should assess its own work rather than assume every company should begin in the same department. McKinsey’s analysis of the 2024 survey describes these patterns.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWhat do adoption figures say—and what do they not say?
McKinsey’s 2025 Global Survey on the state of AI, fielded June 25–July 29, 2025, with 1,993 respondents, found that 88% said their organizations regularly used AI in at least one business function, compared with 78% in the preceding year. For generative AI specifically, 79% reported regular use in at least one function. These are respondent-reported findings, not a census of all organizations.
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The distinction between use and scale matters. About one-third of respondents said their organizations had begun scaling AI programs, meaning most had not reported scaling across the organization. A team using AI in one workflow does not establish that the company has integrated it broadly, governed it consistently, or realized measurable enterprise-wide value.
The prior survey offers a useful point of comparison, but it should not be mistaken for the latest rate: McKinsey’s 2024 survey, fielded July 16–31, 2024, among 1,491 participants, found 71% reporting regular generative-AI use in at least one business function. Survey scope and timing matter when interpreting changes between annual results.
Why reported use does not automatically create business value
Adoption statistics measure reported activity, not a causal financial result. In findings from the 2024 survey published in 2025, more than 80% of respondents said their organizations had not seen tangible enterprise-level EBIT impact from generative AI. That does not mean no team or workflow saw a benefit; it does mean broad use should not be presented as proof of material company-wide financial gains.
McKinsey’s 2025 reporting describes organizations redesigning workflows, elevating governance, and mitigating risks as they pursue value from generative AI. It also points to practices such as senior-leadership involvement, role-based training, workflow integration, feedback loops, roadmaps, and defined key performance indicators. For a business evaluating AI, the practical implication is to set a specific workflow and outcome to measure rather than assume that adding a tool will itself improve performance.
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How can a business adopt AI responsibly?
NIST’s voluntary AI Risk Management Framework (AI RMF) offers a structured way to consider trustworthiness across AI design, development, use, and evaluation. Its four functions are Govern, Map, Measure, and Manage. NIST says AI RMF 1.0 is being revised, so organizations should check the framework page for updates rather than assume that version 1.0 is the latest state. The framework is guidance, not a certification or a guarantee of safety or legal compliance. NIST’s AI Risk Management Framework page explains its scope and status.
For generative AI, NIST published a cross-sector companion to AI RMF 1.0 on July 26, 2024: the Generative Artificial Intelligence Profile. It suggests actions for managing generative-AI risks, but it remains voluntary guidance rather than proof that a system is safe or compliant. Applicable legal requirements depend on the business, its use case, and jurisdiction; these sources do not determine which laws apply to a particular organization.
For an initial evaluation, connect the framework’s risk-management approach to the actual workflow:
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- Decide where human review is necessary. Staff should be able to review and correct outputs before consequential actions when the task or risk warrants it.
- Match safeguards to the information involved. Consider privacy, security, and access controls appropriate to the data and process.
- Assign ownership. Name who governs the use, monitors performance, and receives reports of failures or harms.
- Fit the system into the workflow. Assess whether it works with existing processes and whether people can provide feedback and escalate problems.
- Monitor against defined measures. Track the chosen outcome and relevant failures over time; adjust or stop the use if the evidence does not support continuing.
This is a decision structure, not a promise of return on investment. The survey findings do not establish that any particular implementation will deliver a specific result.
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