AI Readiness Checklist

We have identified specific business problems that AI could address (not just "we need AI").

Vague goals like "improve efficiency" don't count. You need concrete problems with measurable outcomes.

Leadership can articulate why AI is being considered and what success looks like.

If leadership can't explain the "why" without buzzwords, the organization isn't ready.

We have realistic expectations about AI capabilities and limitations.

AI is not magic. If the expectation is "it will just figure it out," recalibrate first.

We have a timeline that includes education and pilot phases, not just deployment.

Rushing to deployment is the #1 predictor of failure. Plan for learning time.

We have budget allocated beyond just licensing fees (data prep, integration, training, maintenance).

Total cost typically runs 3x to 7x the licensing fee. Budget accordingly.

We know where our relevant data lives and who owns it.

Data scattered across shared drives, inboxes, and spreadsheets needs consolidation first.

Our data is reasonably clean, structured, and accessible.

If your data has significant duplicates, gaps, or formatting issues, budget for data cleanup.

We have enough historical data to support the AI use case we are considering.

Some AI needs thousands of examples. Some needs only your existing documents. Know which you need.

We understand our data privacy obligations and have classification policies.

Know what data is public, internal, confidential, and regulated before any AI touches it.

We have (or can create) a data pipeline to feed AI systems on an ongoing basis.

AI isn't a one-time data load. It needs fresh, current data to remain useful.

Our current systems have APIs or integration points that AI tools can connect to.

If your core systems are isolated with no API access, integration costs will be significant.

We have someone (internal or external) who can manage AI tool integration.

AI deployment is not plug-and-play. You need technical capability to connect, test, and maintain.

Our network and cloud infrastructure can handle additional AI workloads.

AI can be bandwidth and compute intensive. Make sure your infrastructure can handle the load.

We have a testing or staging environment to pilot AI before production.

Never deploy AI directly to production. Test with real scenarios in a controlled environment first.

We have monitoring and logging capabilities to track AI system performance.

You can't manage what you can't measure. Plan for observability from day one.

Our team has baseline AI literacy (or we have a plan to build it).

People don't need to be data scientists. They need to understand what AI can and can't do.

We have identified AI champions in different departments (not just IT).

AI adoption succeeds when driven by the people closest to the problems, not just technologists.

Employee concerns about AI (job security, reliability, workload) have been addressed.

Unaddressed fear becomes resistance. Have honest conversations early.

We have a change management plan for introducing AI into existing workflows.

Dropping a new tool on people without process changes guarantees low adoption.

Leadership is visibly committed to AI adoption (not just delegating it).

If leadership treats AI as "IT's project," the rest of the organization will too.

We have (or are developing) an AI acceptable use policy.

Without clear rules, employees will use AI in ways that create risk. Define boundaries.

We understand where AI-processed data goes and how it is stored.

If you can't answer "where does our data go?", don't sign anything yet.

We have defined who is accountable for AI-generated outputs.

AI is a tool. The person who uses the output is responsible for it.

We have a plan for vendor evaluation that includes security and compliance.

Evaluate vendors on data handling and security, not just features and price.

We have a process for regularly reviewing and updating AI policies.

AI moves fast. Annual reviews aren't enough. Plan for quarterly at minimum.