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A business should invest in AI development when it has a specific, consequential problem, a measurable goal, and a credible way to put a solution into everyday work. Before committing to a full build, account for data and system readiness, implementation and ongoing costs, staff adoption, and risk controls. If important assumptions remain uncertain, run a bounded pilot with explicit success and stop criteria.
Start with a business problem, not the AI
Describe the problem in operational terms: which decision, workflow, or service needs to improve, who is affected, how often the issue occurs, and what it costs. Then state the outcome that would matter to the business—for example, fewer errors, shorter processing time, or better service quality. AI is worth considering only if it has a plausible role in achieving that outcome.
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This framing also helps distinguish AI development from general technology investment. If the problem is better solved by changing a process, improving ordinary software, or addressing a staffing constraint, an AI project may add cost without resolving the underlying issue.
Work out whether the value can be measured
Record a baseline before changing the workflow. Choose a metric that can be observed during a pilot and connect it to the business goal. Include both potential benefits and the full cost of reaching and sustaining them: development or purchase, integration, ongoing operation, oversight, and process change. Where possible, compare the pilot with a credible counterfactual, such as the existing workflow or a comparable group. Be clear about measurement limits.
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Do not assume that a plausible use case guarantees a predictable return. OECD, BCG, and INSEAD report that estimating ROI can remain difficult even for narrowly defined applications. In their 2022–23 survey, 62% of manufacturers and 56% of ICT enterprises said difficulty estimating ROI in advance was an obstacle to AI use; those figures describe respondents in those sectors, not businesses generally. The survey covered 840 enterprises in G7 countries and 167 in Brazil. OECD’s chapter on new evidence for policymaking and the report page describe the findings.
Check whether the business is ready to use AI
A model or tool cannot compensate for missing inputs, inaccessible systems, or a workflow that nobody can realistically change. Assess these conditions before choosing a development path:
- Data: Is relevant data available, usable, and representative of the cases the system will encounter?
- Systems: Can the solution access the information it needs and return useful outputs to the systems employees already use?
- People and process: Who will act on the output, what skills or training will they need, and how will the workflow change?
- Evaluation: Can the business check output quality and identify errors or failures in real operating conditions?
- Ownership: Is there a responsible business owner for the use case, performance, and ongoing oversight?
OECD, BCG, and INSEAD identify uncertainty about data and digital readiness, access to AI skills, integration with varied operating systems, and change management as adoption challenges. They also note that firms often need to tailor and experiment with use cases rather than plug in a generic tool. See the OECD report.
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There is no universally best route. Compare the options against the same business problem, expected benefit, and success measure—not just the initial price or technical appeal.
| Path | What to assess |
|---|---|
| Develop a custom solution | Whether the problem requires a tailored fit; total development and operating cost; access to data and systems; internal skills; time to operational use; and whether performance can be evaluated. |
| Adapt or buy an existing product | Fit with the actual workflow; integration effort; recurring costs; ability to inspect and evaluate performance; required process changes; and privacy, security, oversight, and accountability needs. |
| Use a service provider | Whether external expertise can address a skills or implementation gap; scope and cost; knowledge transfer; access to evaluation evidence; and who remains accountable for outputs and ongoing operation. |
| Wait and improve readiness | Whether weak data, disconnected systems, unclear ownership, or limited staff capacity makes a near-term project unlikely to succeed—and what specific gap must be addressed before reconsidering. |
A vendor demonstration is not proof that a product fits your data, systems, or workflow. For any path, ask how you will test the solution on representative cases and what evidence you can review before relying on it.
Use a bounded pilot to resolve important uncertainty
A pilot is useful when feasibility, value, or operational fit is not yet established. It should test a decision the business actually needs to make, rather than simply show that a model can produce an output.
- Limit the scope. Select a defined workflow and duration that are sufficient to test the main assumptions.
- Set the baseline and success threshold. Specify the existing result, the metric to observe, and the improvement that would justify further investment.
- Name an owner and review process. Identify who is accountable for the pilot and how people will check outputs before they affect consequential work.
- Agree on stop conditions. Define in advance what unacceptable errors, costs, risks, or workflow burdens would end the test.
- Evaluate real-workflow results. Check performance with actual users, data, system connections, and handoffs—not only a prepared demonstration.
At the end, compare results with the baseline and threshold. Consider whether benefits persist after including integration, operating, oversight, and process-change costs. A technically successful test is not a business success if it fails to improve a meaningful outcome or cannot be used reliably in the workflow.
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Identify risks relevant to the intended use, including reliability, safety, security, privacy, fairness, accountability, transparency, and explainability. Decide what human review, monitoring, documentation, or other controls are needed before deployment, and revisit them as the system or use changes. Check applicable sector- and jurisdiction-specific legal obligations separately; a general framework does not replace legal advice or compliance work.
NIST’s voluntary AI Risk Management Framework organizes risk work into four functions: Govern, Map, Measure, and Manage. NIST says trustworthiness considerations apply across pre-design, design and development, deployment, use, and testing and evaluation. The framework was released on January 26, 2023, and NIST’s framework page reports that it is under revision, with an April 7, 2026 concept note for a critical-infrastructure profile. Check NIST’s AI Risk Management Framework page for current materials. The framework is a voluntary management resource, not a substitute for obligations that apply to a particular business.
Scale only when operational evidence supports it
Expand beyond a pilot only when measured results meet the original success criteria and the solution remains affordable, usable, and acceptably controlled in the real workflow. Reassess costs, errors, staff experience, system integration, and risk before extending the use to more teams or decisions. If the evidence falls short, revise the use case, fix a readiness gap, choose a different path, or stop rather than treating prior spending as a reason to continue.
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