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Build an AI strategy around business outcomes, not a list of tools. Identify a small set of promising workflows, compare their value and risks, assign accountable owners, and test each one against a measured baseline. Scale only when real users and meaningful results support it.
Where should your company start with AI?
Start by asking business leaders and frontline teams which outcomes matter most: faster service, better quality, less repetitive work, stronger forecasting, new revenue, or another concrete priority. For each goal, record the current baseline and a target measure before choosing a technology. That makes it possible to judge whether AI improved the work rather than simply whether people tried it.
AI use is widespread, but adoption figures do not show that every organization is ready or getting value. Stanford HAI’s 2026 AI Index, drawing on McKinsey & Company’s 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, compared with 71% in 2024. These are self-reported, directional survey results, not audited rates for every company. Stanford HAI, 2026 AI Index
How do you choose AI use cases?
Build a portfolio of candidate workflows with the teams who do the work. Describe who performs each task, where its information comes from, what output or decision is needed, what mistakes would cost, and how a person would check the result. Compare candidates across the following dimensions rather than ranking them on projected savings alone:
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- Business outcome and workflow: What should improve, and which people or process steps will be affected?
- Data readiness: Is the necessary information available, reliable, and appropriate to use?
- Feasibility: Can the system fit existing tools and processes, and does the company have the skills to operate it?
- Risk and controls: What could go wrong, how serious would the effects be, and what review or safeguards are needed?
- Measurement: Can you establish a baseline and gather feedback on quality and business impact?
- Operating cost: What ongoing costs and internal effort will be required?
Use these factors as a decision aid, not as a universal scoring formula. NIST’s AI Risk Management Framework offers voluntary guidance for incorporating trustworthiness across AI design, development, use, and evaluation; it does not prescribe one weighting for selecting company projects. NIST AI Risk Management Framework
Who should own AI governance?
Name an executive sponsor who can connect the work to company priorities, and operational owners who are accountable for each workflow’s results. Decide how business teams, technology, data governance, risk, and compliance will participate. The arrangement should fit the company’s size, sector, and risk profile.
McKinsey’s 2025 survey describes organizations using a mix of structures: respondents often reported centralized elements for risk and compliance and data governance, while technology talent and adoption more often used hybrid or partially centralized models. These are survey observations, not a prescription for every organization. McKinsey & Company, 2025
How should you assess AI risks before rollout?
Before a pilot, document the system’s purpose, users, data, suppliers, and potential effects. Set rules for what may be automated, which outputs need human review, how sensitive information must be handled, and how accuracy and failures will be monitored. Tailor safeguards to the use case; a tool that drafts internal summaries does not necessarily warrant the same controls as one influencing consequential decisions.
NIST describes AI RMF 1.0 as voluntary risk-management guidance, not a legal requirement or certification. Its Generative AI Profile can help organizations identify risks distinctive to generative AI and consider actions aligned with their goals. NIST says the framework is being revised, so consult its current materials when planning or updating controls. NIST AI Risk Management Framework · NIST Generative AI Profile
How do you run a useful AI pilot?
- Set measures in advance. Record the current workflow baseline and define what success means for quality, time, cost, or another relevant outcome.
- Test with intended users. Use the actual workflow and representative tasks, including exceptions—not only an ideal demonstration.
- Make review explicit. Specify who checks outputs, what errors they should look for, and when to escalate or stop using the system.
- Collect evidence. Track failures, review effort, user feedback, and the outcome measures chosen for the pilot.
- Decide against thresholds. Continue, revise, or stop based on pre-agreed criteria rather than enthusiasm or usage alone.
Count the work around the system, including exception handling, integration, review time, and user behavior. A tool that performs well in a demonstration may not improve the end-to-end process. McKinsey’s 2025 survey found that workflow redesign had the strongest association among 25 tested organizational attributes with respondents’ self-reported EBIT impact from generative AI use. Yet only 21% of respondents whose organizations used generative AI said their organizations had fundamentally redesigned at least some workflows. The association does not prove that redesign causes a financial result. McKinsey & Company, 2025
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you prepare employees and change the work?
Explain why the company is adopting AI, which tools are approved, what information employees may use, when human review is required, and how to report a problem. Provide training for the roles and tasks affected instead of assuming one general introduction is enough. Ask employees for feedback as the workflow changes; they are often best placed to identify exceptions, unnecessary steps, and new risks.
McKinsey identifies role-based capability training, senior leadership involvement, internal communications, feedback loops, trust practices, phased road maps, governance, and defined KPIs among practices organizations report using to scale generative AI. These practices are not a guarantee of results, but they point to the organizational work needed alongside technology. McKinsey & Company, 2025
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When should you scale an AI use case?
Scale only when a pilot meets its pre-agreed thresholds and the company can support the workflow, controls, and users at a broader level. Track measures that fit the use case: adoption, output quality, risk or safety incidents, process outcomes, and financial results where appropriate. Review system or vendor changes and revisit controls as the technology and its use evolve.
NIST describes its AI RMF as a living resource. Because the framework is under revision, check the official NIST page for current materials rather than assuming a particular version will remain unchanged. Obligations also depend on geography, sector, data, systems, and use case; this company-level approach is not sector-specific regulatory advice.
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