Businesses can apply AI to internal knowledge, marketing, sales, customer service, product development, software engineering, IT, risk research, and operations. These are practical application areas, not a ranking of products: the right fit depends on the workflow, data, integration, human oversight, cost, and risk tolerance. McKinsey’s 2025 survey found that 88% of respondents said their organizations regularly used AI in at least one business function, but only about one-third said their organizations had begun scaling AI programs enterprise-wide.1
Top 10 AI applications for businesses
The applications below describe where AI may support work; they do not imply that every organization needs a separate AI system for each function. Start with a defined task and a way to assess whether assistance improves it.
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1. Internal knowledge retrieval and research
Conversational tools can help employees search internal documents, summarize information, and assemble findings. This is most useful when relevant knowledge is spread across approved repositories and employees need a faster route to it. Access controls and source references matter: a fluent answer is not proof that it is complete or current. McKinsey’s 2025 survey reports AI use to capture, process, and deliver information, and identifies knowledge-management research among agentic AI examples.1
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AI can help brainstorm campaign ideas, draft or adapt content, and surface information relevant to marketing strategy. Teams still need to check facts, brand voice, rights, and audience fit before publishing. McKinsey includes marketing strategy and content-related work among reported AI use cases; this describes adoption, not proof that generated material improves campaign performance.1
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3. Sales personalization and follow-up
Sales teams can explore AI assistance for tailoring outreach, identifying leads, and preparing follow-up based on relevant customer information. The system’s suggestions should be checked against accurate, permitted data and the relationship context. McKinsey describes generative AI’s potential to support sales and marketing, but that potential does not guarantee higher conversion or revenue.2
4. Customer self-service
Conversational systems can handle routine questions or direct customers to the right channel. Define what the system may answer, which information it can access, and when it must transfer the conversation to a person. Escalation is important for ambiguous, sensitive, or consequential problems. Customer operations and customer interactions are central to McKinsey’s analysis of generative AI use cases.2
5. Contact-center agent assistance
AI can support contact-center staff by retrieving information, drafting responses, or helping organize case details. This differs from replacing an agent: the human remains responsible for understanding the customer’s issue and checking the proposed response. McKinsey’s 2025 survey reports customer-service automation and use-case-level benefits, but does not establish a universal performance gain for every contact center.1
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Teams may use generative AI to support ideation, development, and testing workflows—for example, exploring concepts or helping prepare and review materials. Domain experts must validate whether an output is feasible, safe, and appropriate for the intended users. McKinsey’s 2025 survey identifies product and service development among functions associated with AI-related revenue increases; that survey finding does not establish a result for a specific project or company.1
7. Software engineering
AI coding assistants can draft code or help developers work through development tasks. Engineering review, testing, security checks, and compliance with project standards remain necessary before code is merged or deployed. McKinsey includes code drafting among generative AI examples and reports software engineering among functions with use-case cost benefits; the evidence does not promise the same benefit in every team.12
8. IT service-desk support
Conversational or agentic systems can assist with service-desk workflows, such as answering routine questions or helping route requests. A useful deployment needs clear boundaries for account access, sensitive actions, and escalation to an IT employee. McKinsey’s 2025 survey says reported AI-agent use is most common in IT and knowledge management, including service-desk management.1
9. Risk, legal, and compliance research
AI may help search or summarize documents and organize research for review by qualified staff. It should not be treated as an authority on what the law requires or as a substitute for professional judgment on consequential decisions. Check citations against the underlying materials, protect confidential data, and apply appropriate governance. The cited sources discuss trust, explainability, and governance challenges; they do not validate particular legal or compliance products.13
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AI can be evaluated for information processing, monitoring, and support for existing analytical workflows in supply chains or manufacturing. Separate generative tasks—such as summarizing operational information—from numerical forecasting or optimization, which may use different analytical methods. McKinsey’s 2025 survey reports cost benefits in manufacturing, but that does not show that every manufacturing or supply-chain task is a generative-AI use case or will produce savings.12
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the adoption figures do—and do not—show
Survey findings indicate broad experimentation and use, but they should not be confused with mature deployment or confirmed company-wide returns.
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- In McKinsey’s November 5, 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, while nearly two-thirds had not begun enterprise-wide scaling.1
- In the same survey, 39% attributed any enterprise-level EBIT impact to AI; most of that subset reported that less than 5% of EBIT was attributable to AI. These are respondent reports, not a universal measured return.1
- A 2023 McKinsey analysis estimated that about 75% of modeled generative-AI use-case value fell across customer operations, marketing and sales, software engineering, and research and development. This is modeled potential across analyzed use cases, not realized returns or a forecast for an individual company.2
- McKinsey’s January 2025 workplace report said 92% of surveyed companies planned to increase AI investment over the following three years, while 1% of surveyed leaders described their companies as mature in AI deployment. Its survey included 3,613 employees and 238 C-level executives in October and November 2024; the main findings pertain to US workplaces.3
How to choose an AI application for your business
Choose a bounded workflow, not a tool because it is prominent or because another company uses it. Compare candidates using the same criteria so a pilot can answer a business question rather than merely demonstrate a feature.
- Define the problem and baseline. Identify the task, who performs it, how often it occurs, and the current time, quality, or service measure.
- Check workflow fit and integration. Determine whether AI can work with the systems and approved information employees already use, and what process changes or staff training it requires.
- Set data and governance boundaries. Decide what information may be used, who can access it, how outputs are stored, and what security, privacy, or explainability controls apply.
- Specify review and escalation. Name the person responsible for checking outputs and define when the task must move to a human, especially for sensitive or high-impact decisions.
- Measure outcomes over a defined period. Track an outcome tied to the original problem, alongside error rates, user adoption, and customer or employee impact where relevant.
- Include total operating cost. Account for implementation, integration, oversight, training, and ongoing operation—not just access to the AI system.
- Scale only when evidence supports it. A successful trial on a narrow task does not by itself show that the approach will work across teams, data sources, or locations.
McKinsey identifies workflow redesign, leadership, trust, training, KPI tracking, explainability, and uncertainty about costs at scale as material adoption issues.13
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