AI Readiness Assessment Checklist

A practical AI readiness checklist covering workflow, data, integrations, security, governance and whether an AI project is ready for investment.

1. Is there a defined business problem?

Describe the workflow, current pain point, volume, cost, delay or capacity constraint without referring to a specific AI product. A project is more investment-ready when leadership can state the operating outcome it wants to change and establish a measurable baseline.

2. Is the workflow stable enough to improve?

Document who performs the work, what starts the process, which decisions are made, which systems are used and what completes the task. AI rarely fixes a process that nobody understands. If the workflow varies because ownership is unclear, process design may need to come first.

3. Is the required data available and usable?

Identify the information the AI capability needs, where it resides, who owns it and whether it is sufficiently accurate and current. Determine whether the project needs documents, CRM records, ticket history, transactional data, knowledge bases or other sources.

4. Can the necessary systems be integrated?

List every application the solution must read from or write to. Check APIs, authentication, permissions, rate limits and vendor restrictions. Integration feasibility often determines whether a promising AI use case can become a production workflow.

5. Are security and privacy boundaries defined?

Decide which information AI systems may access, where data can be processed, what must be logged and which actions require additional controls. Sensitive customer, employee, financial or regulated data may change the architecture and vendor requirements.

6. Is there a clear human approval boundary?

Define what the system can draft, recommend or execute automatically and which actions require confirmation. The consequence of an error should determine the level of autonomy, not enthusiasm for the technology.

7. Does the project have accountable owners?

Assign a business owner responsible for the outcome and a technical owner responsible for the system. Identify the users who will test the workflow and the person who will review quality after launch. Production AI needs ongoing ownership.

8. Can success be measured?

Choose baseline and target measures before implementation. Useful metrics include cycle time, handling time, task completion, throughput, error rate, conversion, response time, capacity created and operating cost. Avoid relying only on subjective impressions of output quality.

9. Is there a credible economic case?

Estimate implementation cost, integration effort, recurring model or platform cost, support and change management. Compare those costs with the expected value. A readiness assessment should identify whether there is enough potential benefit to justify a pilot, not simply whether AI is technically possible.

10. Is the organization ready to adopt the new workflow?

Identify who will use the system, how their work changes, what training is needed and why they would choose the new process over the old one. Adoption is a business-case assumption and should be treated as part of implementation design.

Readiness outcome: proceed, prepare or stop

A strong assessment should end with a decision. Proceed when value, feasibility, ownership and controls are strong enough for a measured pilot. Prepare when specific gaps such as data, integration or governance must be resolved first. Stop when the use case lacks measurable value or the risk and complexity outweigh the expected benefit.

What an AI readiness assessment should deliver

The useful output is not a generic maturity score. It should produce a prioritized use-case shortlist, current-state workflow findings, data and integration dependencies, risk and governance requirements, preliminary economics, pilot success measures and a practical recommendation for the next investment step.