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A business is ready for AI only when a particular use case has a clear purpose, suitable data, capable people, workable infrastructure, and proportionate safeguards. Use this checklist to decide whether to run a bounded pilot, prepare first, or pause—not to assign your whole organization a universal “AI-ready” label.
1. Define the business problem before choosing AI
Start with a task or outcome, not a product. A useful candidate might be reducing a defined repetitive workload, improving response times, or supporting a specific analysis. Write down what changes if the initiative succeeds and who will be affected.
- Describe the workflow: identify the process, its users, and the people affected by its outputs.
- Set a baseline: record how the process performs now and choose a measurable result that would justify continuing, changing, or stopping the initiative.
- Set human decision points: decide when a person must check, approve, or override an output, particularly if it could materially affect customers, employees, or other stakeholders.
- Compare options: weigh fit to the problem, expected benefit, implementation effort, ongoing cost, and risk. These are practical comparison criteria, not a published OECD scorecard.
Readiness depends on the use case, sector, firm size, and digital maturity. The OECD’s SME adoption framework describes differentiated pathways rather than a single route for every organization. OECD analysis of AI adoption by SMEs
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2. Check whether the data is usable and appropriately governed
Having data is not the same as having data suitable for a particular AI task. Establish what information the workflow relies on, whether it is fit for purpose, and whether the business can use it in the proposed way.
- Inventory relevant records, applications, and data stores.
- Identify the data owner, authorized users, and the business’s permission to use the information for this purpose.
- Check whether important records are digitized, findable, sufficiently complete, consistent in format, and current enough for the task.
- Look for duplicates, missing fields, inconsistent entries, manual errors, and silos that prevent a coherent view.
- Decide who will correct source-data errors and how that work will be tracked.
- Before sending sensitive information to an AI service, set appropriate rules for access, retention, security, privacy, and quality review.
For SMEs, OECD recommendations include digitizing core records, standardizing and labeling data, clarifying ownership and quality checks, and using governance for access, retention, and security that is proportionate to the context. OECD SME adoption recommendations
3. Match skills and capacity to the roles involved
Training needs differ depending on whether someone uses an AI tool, chooses an initiative, or operates the underlying systems. Assess capability by role rather than assuming a subscription or a single training session will prepare everyone.
Employees who use the system
Users should know how to operate the tool appropriately, protect information, question outputs, and apply independent judgment. They also need a clear way to raise errors or concerns.
Leaders who sponsor or approve the work
Decision makers need to connect the use case to business priorities, assess potential and risks, assign responsibility, support changes to the workflow, and budget for implementation and maintenance.
Digital, data, and technical staff
Someone must be able to integrate, monitor, maintain, and risk-manage the system. That expertise may be internal or provided by an outside service provider, but responsibilities and oversight should still be clear.
Identify training needs and give people time to learn and redesign processes. An OECD workforce paper distinguishes skill needs among these roles, but it concerns public institutions; use it as a planning reference, not as evidence of a private-sector legal duty. OECD paper on AI skills in the workplace
4. Verify infrastructure, integration, and ongoing cost
Infrastructure readiness is about whether the workflow can reliably reach the data and services it needs—not whether the business owns a particular kind of hardware. Check the current environment against the specific use case.
- Confirm reliable connectivity for the people and locations involved.
- Map where the relevant data lives and whether existing systems can exchange information with the AI application.
- Review identity and access controls, cybersecurity, backups, recovery, and how a vendor handles business data.
- Determine whether an existing managed service is adequate or whether the use case needs more cloud capacity, compute, or storage.
- Estimate integration work, lifecycle costs, maintenance, and how data can be exported or moved if the provider or system changes.
The OECD identifies connectivity and access to data, algorithms, and compute as adoption enablers; it does not prescribe a universal hardware specification. OECD SME AI adoption framework
5. Put governance and risk controls around the use case
Governance is an operating responsibility, not a one-time approval. Scale the review to the sensitivity of the data and the consequences of an error. Record how the system is intended to work and what people should do when it does not.
Rank #4
- Assign accountability: name a person or function responsible for the use case and its continued operation.
- Document the system: record its purpose, users, affected groups, vendor, data inputs, expected outputs, and known limitations.
- Assess possible impacts: identify plausible harms and failure modes before use.
- Set operating controls: define human review, escalation, output checks, incident handling, and conditions for suspending use if performance or circumstances change.
- Review obligations: get appropriate expertise on privacy, security, intellectual property, contracts, and jurisdiction-specific requirements.
- Monitor and communicate: track outcomes and risks after launch, record material changes, and communicate relevant practices to affected stakeholders.
OECD enterprise guidance describes responsible-business-conduct due diligence as embedding commitments in policies and management systems; identifying and assessing actual or potential adverse impacts; ceasing, preventing, or mitigating them; tracking implementation and results; communicating actions; and providing for or cooperating in remediation when appropriate. Its examples apply across the AI value chain and are not exhaustive or suitable in every situation. OECD due diligence guidance for responsible AI
NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance for incorporating trustworthiness considerations into AI design, development, use, and evaluation. The framework page says AI RMF 1.0 is being revised. Its Playbook organizes suggested actions under Govern, Map, Measure, and Manage, while cautioning: “The Playbook is neither a checklist nor set of steps to be followed in its entirety.” The suggestions are voluntary and can be selected to fit an organization and use case; neither NIST resource replaces checking applicable law or obtaining qualified advice. NIST AI Risk Management Framework · NIST AI RMF Playbook
6. Decide whether to proceed, prepare, or pause
Use the review to make a decision about this use case—not to score the organization as a whole. For each readiness area, record the evidence you have, an owner, the gap, the next action, and a date to review progress.
Best Value
- Proceed with a bounded pilot when the purpose is specific, the exposure is manageable, the relevant data and infrastructure are adequate, and people know their responsibilities.
- Prepare first when a remediable gap—such as incomplete records, unclear access, missing training, or integration work—would undermine the result or create avoidable risk.
- Pause when the intended outcome is unclear, material risks lack workable controls, or the business cannot responsibly operate and monitor the system.
A company may be able to test a low-risk task while needing more preparation before automating a sensitive or consequential process. If comparing approaches, consider problem fit, data sensitivity and quality, integration effort, reliability, human oversight, lifecycle cost and maintenance, vendor data terms and portability, and governance burden. These comparison axes are a practical synthesis, not a standardized published scale.
Using an SME readiness assessment
The OECD SME AI Readiness Tool asks, “Is your business AI-ready?” It is designed for SME owners and managers in G7 countries, so it should not be treated as a universal benchmark. The OECD page describes it as a pilot under active development; as of May 2026, its content had only preliminary validation by G7 governments. Check the tool’s current notice and status before relying on it. OECD SME AI Readiness Tool
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