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Build your company’s AI strategy around business outcomes, not a list of tools. Choose a small set of workflows where AI could make a measurable difference, assess data readiness and risk, assign accountable owners, then pilot in the real work before deciding what to scale.
Where should your company start with AI?
Start by asking business leaders and the people doing the work what needs to improve. Possible outcomes include faster customer service, fewer repetitive tasks, better quality, more reliable forecasting, or new revenue. Make each outcome specific enough to measure: record how the process performs now and set a target before selecting a system.
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AI use is widespread in survey responses, but adoption figures do not show that every company is prepared or getting value. Stanford HAI’s 2026 AI Index, drawing on McKinsey & Company’s 2025 survey, reports that 88% of respondents said their organizations used AI in at least one business function in 2025, compared with 78% in 2024. It also reports regular generative AI use in at least one function at 79% in 2025, up from 71% in 2024. These are self-reported, directional survey results—not audited adoption rates for all companies. Stanford HAI, 2026 AI Index
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How do you choose AI use cases?
Ask operating teams to describe candidate workflows before discussing products. For each task, document who does it, what information they use, what output or decision is needed, what a mistake could cost, and how a person can verify the result. Compare candidate uses against the same criteria:
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| Decision factor | Questions to answer |
|---|---|
| Business outcome | Which target will improve, and who benefits from the change? |
| Data | Is the necessary information accessible, sufficiently reliable, and permitted for this use? |
| Feasibility | What integration, technical work, supplier support, and internal capability are required? |
| Workflow fit | Where will the system enter the process, and how will exceptions and human review work? |
| Risk | What could go wrong, who might be affected, and what controls are needed? |
| Measurement | Can you establish a baseline and track output quality and business impact? |
| Operating cost | What will it take to run, monitor, support, and update the system? |
You can score these factors to compare options, but treat the result as an internal decision aid. NIST and McKinsey do not prescribe a universal scoring formula or weighting. Prefer a short portfolio of promising, assessable workflows over a broad wish list of speculative projects. NIST AI Risk Management Framework; McKinsey, 2025
Who should own the AI strategy?
Name an executive sponsor who can connect AI work to company priorities and resolve conflicts over resources or risk. Give each selected use case an operational owner responsible for the workflow, its target measures, and the day-to-day review of results. Assign clear participation to business, technology, data governance, risk, and compliance teams; the right structure depends on company size and sector.
McKinsey’s 2025 survey describes organizations using different arrangements: risk and compliance and data governance often had centralized elements, while technology talent and adoption more often used hybrid or partly centralized models. Those are reported practices, not a universal blueprint. Establish at least a clear route for approving use cases, escalating incidents, and deciding whether a pilot may proceed or scale. McKinsey, 2025
How should you manage AI risks?
Before rollout, document the system’s purpose, users, data, suppliers, and possible effects. Decide what may be automated, which outputs require human review, how sensitive information will be handled, and how accuracy and failure will be monitored. Controls should reflect the specific use and its potential consequences rather than treating every AI feature as equally risky.
NIST describes AI RMF 1.0 as a voluntary resource for incorporating trustworthiness into AI design, development, use, and evaluation; it is not a certification or, by itself, a legal requirement. NIST also provides a Generative AI Profile to help organizations identify risks specific to generative AI and consider actions aligned with their goals. NIST says the framework is being revised, so check its official pages for current materials when using it. NIST AI Risk Management Framework; NIST Generative AI Profile
How do you run a useful AI pilot?
Test the selected use case in the workflow where people will actually use it, not only in a demonstration. A pilot should establish whether the system improves the chosen outcome after accounting for review time, exceptions, integration work, and how people use the tool.
- Set the baseline and success thresholds. Record current process performance and agree in advance what improvement, quality, and risk results would justify continuing.
- Define the review path. Specify who checks outputs, what needs escalation, and how users handle errors or uncertain results.
- Test with intended users. Include normal tasks and realistic exceptions; collect feedback and examples of failure, not just successful outputs.
- Evaluate the whole workflow. Compare results with the baseline, including the time spent checking, correcting, or routing work.
- Decide from evidence. Refine, stop, or expand the pilot according to the pre-agreed thresholds rather than enthusiasm for the technology.
Workflow redesign may matter as much as the AI feature itself. In McKinsey’s 2025 survey analysis, workflow redesign had the strongest association among 25 tested organizational attributes with respondents’ self-reported EBIT impact from generative AI. Yet only 21% of respondents whose organizations used generative AI said their organizations had fundamentally redesigned at least some workflows. This is a survey association, not proof that redesign causes a financial result. McKinsey, 2025
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How do you prepare employees and change the work?
Tell affected employees why the company is adopting AI and what will change in their work. Explain which tools are approved, what information may be entered, when human review is required, and how to report a problem. Train people for their roles: someone reviewing generated content, for example, needs different guidance from a team configuring a workflow or handling customer data.
McKinsey identifies role-based capability training, internal communications, and feedback as practices reported by organizations scaling generative AI. Make employee feedback part of the pilot and ongoing operation so that practical problems reach the people who can change the workflow or controls. McKinsey, 2025
When should you scale an AI use case?
Expand only when the pilot meets its agreed thresholds and the organization can support the workflow beyond the test. Track measures suited to the use case, such as adoption, output quality, process outcomes, risk events, and financial impact. Assign owners to review results and respond when performance changes.
Before expansion, confirm that the review process, training, support, data permissions, and controls work at the expected scale. Revisit the assessment when a supplier, model, data source, or workflow changes. NIST describes its framework as a living resource and says revision is underway; its official pages provide the current status. NIST AI Risk Management Framework
What should an AI strategy document contain?
- Business outcomes: priorities, baseline measures, and targets the work is intended to improve.
- Use-case portfolio: selected workflows, owners, expected value, feasibility, data needs, risks, and measurement plans.
- Accountability: executive sponsor, operational owners, decision rights, and escalation paths.
- Risk and controls: approved uses, data handling, human review, monitoring, and incident response appropriate to each use.
- People and workflow: process changes, role-based training, communications, and feedback routes.
- Delivery plan: pilot milestones, scale criteria, ongoing measures, and review points when systems or uses change.
This is a company-level planning guide, not sector-specific regulatory advice. Legal and regulatory obligations depend on geography, industry, data, systems, and use case; the cited sources do not resolve those case-specific requirements.
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