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Your business is ready to adopt AI for a particular use case only when you can define the problem and success measures, provide suitable data and systems, assign accountable people, and manage the risks and ongoing costs. Readiness is not a company-wide yes-or-no verdict: a business may be prepared to use AI to draft internal summaries while not being prepared to automate a consequential decision.
Start with a business problem, not an AI tool
Choose a specific workflow, decision, or task that might improve. State who would use the system, who could be affected by its output, and what the business hopes to change. Then compare AI with simpler alternatives: a clearer process, better forms, conventional software, or automation without AI may solve the problem with less risk and effort.
Define the current baseline before testing anything. For example, a team considering AI-assisted support replies might measure handling time, response accuracy, the rate of escalations, and customer complaints. Faster output alone is not enough if quality falls or staff spend more time correcting errors.
- Intended benefit: What should improve, and for whom?
- Outcome measures: Which small set of measures will show whether it improved?
- Quality and harm measures: What errors, unfair outcomes, privacy problems, or other negative effects must be tracked?
- Success and stop conditions: What results justify continuing, and what result or incident requires pausing or ending the use?
Assess the conditions for this use case
Use the following questions to find both strengths and gaps. No single theme establishes readiness on its own; the answers should be specific to the workflow and its risk.
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Opportunity and fit
Can you describe the task clearly enough to evaluate the system’s output? Is the expected benefit meaningful and measurable against the baseline? Consider whether AI is appropriate for the task, especially if outputs could influence customers, employees, finances, safety, or access to services.
People, skills, and ownership
Name the person accountable for the business result, the people who understand the current process, and the staff who will operate the system. Decide who can inspect outputs, correct them, override them, and respond to exceptions. Identify any training, workload, or change-management needs before launch.
Data
List the data the use case requires and check whether it is available, accurate enough, current, and representative of the cases the system will encounter. Confirm who owns it and whether the business may lawfully use it for this purpose. Define access, quality checks, retention, and security requirements; missing or unsuitable data is a readiness gap to resolve, not a reason to assume the tool will compensate for it.
Digital foundations
Check whether existing systems can connect to the proposed tool and whether identity and access controls, cybersecurity, storage, reliability, and staff support are adequate for the workflow. Match any infrastructure investment to the use case: more technology is not automatically the answer, and a small, bounded trial may require less than a production deployment.
Governance and risk
Identify who could be affected, what could go wrong, and how errors or harmful outcomes would be detected and handled. Set human review, escalation, monitoring, and a way to stop or roll back the system. Assign responsibility for these controls before use begins.
AI risk is sociotechnical: results depend not just on a model but on data, users, affected people, and the conditions in which the system is deployed. Data can change over time, which can affect performance and trustworthiness. NIST’s AI Risk Management Framework overview describes a voluntary approach to managing these considerations; it is not a mandatory certification.
Economics and operations
Estimate the full cost of operating the use case, not just the quoted tool price. Include implementation, integration, data preparation, human review, training, monitoring, support, and relevant vendor or contract terms. Compare that estimate with the expected benefit and the success threshold you set. There is no universal ROI, cost, or implementation timeline that establishes readiness for every business; calculate these for your specific workflow.
Use a structured assessment without turning it into a pass-or-fail score
The OECD’s AI readiness guide, published on December 3, 2024, groups readiness into five themes: opportunity identification, human capacity, data for AI, digital infrastructure, and responsible AI governance. Its guide is framed around AI for net zero and includes sector case studies, while noting that its checklists apply across sectors. Use the themes to organize questions and evidence, not as proof that all businesses or use cases have identical requirements.
For a practical assessment, write down what is in place, what evidence supports that judgment, what is missing, and who owns each gap. You can label each theme qualitatively—for example, “in place,” “needs work,” or “unknown”—but this is not a validated universal scoring instrument. An unknown answer is useful: it shows what must be investigated before a decision.
Rank #4
Run a bounded pilot only when it can be controlled
- Document the use case. Record the workflow, intended users, affected parties, proposed benefit, and alternatives considered.
- Record the baseline and measures. Choose a small number of outcome and quality measures, including an error or harm measure.
- Review readiness themes. Note evidence and gaps for opportunity, human capacity, data, digital infrastructure, and responsible AI governance.
- Map and manage risk. NIST organizes its voluntary AI Risk Management Framework around Govern, Map, Measure, and Manage. These functions support assigning responsibility, understanding context, evaluating risks and performance, and managing them over time.
- Decide whether to pilot. Proceed only if accountable people can inspect performance, handle exceptions, and stop the system. Make unresolved critical data, oversight, security, or legal questions prerequisites to production use.
- Review results before expanding. Compare results with the predefined measures and safeguards. Expand only when they are met; otherwise, revise the process, add controls, or stop.
NIST says AI Risk Management Framework 1.0 is being revised, so check the official framework page for current status and edition when consulting implementation details. The framework is designed to support trustworthiness considerations in AI design, development, use, and evaluation, and organizations can operationalize it in varying degrees and capacities.
Use the OECD SME tool only if its scope fits
The OECD’s SME AI Readiness Tool is a pilot for owners and managers of small and medium-sized enterprises based in G7 countries. It can be used whether a business already uses AI, is considering it, or has not started. It asks about firm profile, digital foundations, current or planned AI use, and obstacles. The OECD estimates completion at approximately five minutes; that is the publisher’s estimate, not an independently measured time. The page says responses are processed locally in the browser.
The OECD warns that the pilot may be incomplete, inaccurate, or not current. Treat it as an aid to reflection, not a certification or a substitute for assessing a specific use case. Businesses outside the G7 can still use the readiness themes as a general checklist, but the tool itself is targeted to G7-based SMEs.
Compare tools or vendors against the same requirements
If you are choosing between real options, use representative cases from the workflow and compare each option against the same criteria. A polished demonstration does not establish how a tool will perform on your data or in day-to-day operations.
Quick Recap
- Fit with the workflow and the outcome measures you defined.
- Output quality and failure modes on representative cases.
- Data use, privacy, security, retention, and access controls.
- Integration needs and ongoing operating requirements.
- Human review, override, and escalation controls.
- Total cost and contract terms.
- Ability to monitor performance, export data, change providers, or stop use.
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