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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsAI is becoming part of everyday business solutions because organizations are using it in familiar work such as IT, marketing, knowledge management and customer service. But reported use is not the same as company-wide adoption or proven financial impact: a 2025 survey found widespread use alongside much more limited scaling. For businesses, the practical question is where AI can help a specific workflow—and how to evaluate it without surrendering oversight.
How are businesses using AI in everyday work?
AI is appearing in routine tasks that involve finding, drafting, organizing or responding to information. McKinsey’s 2025 global survey identified IT, marketing and sales, and knowledge management among the business functions with the most reported AI use. Examples included conversational interfaces for handling or retrieving information, support for marketing content and strategy, and automation in some contact-center or customer-service activity.
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These are reported patterns, not a prescription for every company. A customer-service workflow with clear escalation rules is different from one involving sensitive decisions; a marketing team’s drafting needs differ from an IT team’s troubleshooting needs. The relevant question is whether a particular system fits the task, the data involved and the level of human review required.
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Common places AI shows up
- Information work: Conversational tools can help staff retrieve or work with information, subject to access controls and verification.
- Marketing and sales: Generative AI is used to support content and strategy work; employees still need to check accuracy, brand fit and claims.
- Service operations: Some organizations report using AI to automate parts of contact-center or customer-service activity. Businesses need a way to route unclear, sensitive or unresolved cases to a person.
- IT and software work: These functions are among the areas with significant reported AI use, but the exact task and acceptable risk vary by organization.
Function and industry affect what is useful. McKinsey’s 2024 survey found variation across sectors including technology, professional services, financial services, healthcare and retail. Marketing and sales was the most commonly reported generative-AI function overall, but the pattern was not identical across industries. That is a reason to assess a business’s own workflows rather than assume every organization should start in the same department.
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How widespread is business AI adoption?
In McKinsey’s 2025 Global Survey on the state of AI, 88% of respondents said their organizations regularly used AI in at least one business function, compared with 78% in the preceding year. The 2025 survey was fielded June 25–July 29, 2025, and had 1,993 respondents. These are survey responses, not a census of all businesses.
For generative AI specifically, 79% of respondents said their organizations regularly used it in at least one function in the 2025 survey. In the earlier 2024 survey, 71% reported regular generative-AI use in at least one function; that survey was fielded July 16–31, 2024, among 1,491 participants. The figures describe different survey years and should not be read as a guarantee that every business uses these tools.
High reported use does not mean AI is fully embedded across companies. In the 2025 survey, about one-third of respondents said their organizations had begun scaling AI programs across the organization. Use in one or more functions and enterprise-wide scaling are different stages of adoption.
Why use does not automatically mean business value
Experimenting with or regularly using AI can improve a task without changing company-wide results. In findings from the 2024 McKinsey survey, published in 2025, more than 80% of respondents said their organizations had not seen tangible enterprise-level EBIT impact from generative AI. That result does not rule out gains in individual teams or workflows; it cautions against treating broad use as proof of material financial impact.
McKinsey’s 2025 report describes organizations redesigning workflows, strengthening governance and mitigating risks as they seek value from generative AI. It also identifies practices including senior-leadership involvement, role-based training, workflow integration, feedback loops, roadmaps and defined key performance indicators. Together, these point to a more useful evaluation than counting how many employees have access: identify the process being changed and measure whether the change helps.
Evaluate a workflow, not a vague AI initiative
- Choose a bounded task. Define the work, who performs it, what information it uses and where a human decision is required.
- Set a baseline and a goal. Select a measurable outcome tied to the task, such as handling time, error rates or completion quality. Choose measures that reflect the workflow’s real purpose rather than tool activity alone.
- Test with appropriate oversight. Have staff review and correct outputs before consequential actions. Record errors and cases where the system fails or needs escalation.
- Check the effect in context. Compare results with the baseline and account for training, process changes and the time needed for staff to adapt. Do not infer a guaranteed return from survey adoption rates.
- Decide whether to adjust, expand or stop. Scale only when the result, controls and operating responsibilities are clear enough for the wider use.
How can a business adopt AI responsibly?
The National Institute of Standards and Technology (NIST) offers a voluntary AI Risk Management Framework (AI RMF) for organizations incorporating trustworthiness considerations into 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 check its framework page for updates rather than assuming version 1.0 is the latest state.
NIST also published its Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile on July 26, 2024. The cross-sectoral profile accompanies AI RMF 1.0 and suggests actions for managing generative-AI risks. It is voluntary guidance, not a guarantee that a system is safe, legally compliant or suitable for a particular organization. Applicable legal obligations depend on the business’s jurisdiction, industry and use case.
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- Govern: Assign an accountable owner, define acceptable uses and set rules for approval, access and escalation.
- Map: Document the task, affected people, information involved, intended benefits and ways the system could fail or cause harm.
- Measure: Check outputs for accuracy and consistency in the actual workflow, and monitor errors, user feedback and relevant performance indicators.
- Manage: Set controls for review, correction, incident reporting and changes to the system or process.
These checks should be proportionate to the task. A tool that drafts internal text does not necessarily need the same controls as one whose output could affect a customer, employee or consequential decision. NIST’s framework is a starting point for organizing risk management, not a substitute for context-specific legal or security advice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to check when choosing an AI system
Compare options against the workflow rather than relying on a generic “best AI” label. Establish these requirements before a broader rollout:
- Task fit: Can the system address the specific job and business context, including the language, formats and edge cases staff encounter?
- Human review: Can employees inspect, correct or reject an output before it leads to a consequential action?
- Data protection: Are privacy, security and access controls appropriate for the information the workflow uses? Confirm the provider’s applicable terms rather than assuming all systems handle data alike.
- Process fit: Can staff use the system within existing work, and can the business measure a defined outcome?
- Ownership and monitoring: Who is responsible for the system in operation, reviewing performance and receiving reports of failures or harms?
For a framework to structure these questions, consult NIST’s AI Risk Management Framework and its Generative AI Profile. For adoption patterns and survey context, see McKinsey’s 2025 state of AI report and its 2024 survey findings on how organizations are rewiring to capture value.
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