AI Automation Readiness Checklist

Dimension 1: Data Readiness

• The data the AI will need is stored in a system accessible via API or database query (not only in email, spreadsheets, or people's heads).
• The data is in a consistent format — the same type of information is stored the same way across records, without significant variation or inconsistency.
• We have at least 3 months of historical data for the process we want to automate, and it is representative of current operations.
• We know the data quality issues that exist in our data (missing fields, duplicates, incorrect values) and have a plan to address them.
• Data privacy and compliance requirements for the data involved are documented and a legal review of AI processing has been completed or is planned.

Dimension 2: Process Maturity

• The process we want to automate is documented — the steps, the decision rules, and the exceptions are written down, not only in people's heads.
• The process is executed consistently — different people doing the same task produce outcomes that meet the same quality standard.
• We know the exception rate — how often an input to the process requires non-standard handling — and it is below 20%.
• The inputs to the process are well-defined — we know every type of input the process handles and can describe what makes each type different.
• We have defined what a correct output looks like — we can specify the criteria by which we would evaluate whether the AI has done the task correctly.

Dimension 3: Team Capability

• We have at least one technical person on the team or engaged as a consultant who has built and deployed a production AI system (not just built prototypes).
• We have identified who will own the AI system after deployment — who is accountable for its performance, maintenance, and improvement over time.
• The business-domain experts who know the process best are available and willing to invest time in the project (estimated 2-4 hours per week during the build phase).
• The team has budget and authority to engage external AI engineering support if internal capability gaps are identified during the project.
• The people whose roles will change as a result of automation are aware of the project and have been consulted about its design.

Dimension 4: Strategic Clarity

• We have defined a specific business metric that this automation project should move (e.g., processing time, cost per unit, error rate) and measured its current baseline.
• We have defined what success looks like at 3 months, 6 months, and 12 months — with specific metric targets, not general aspirations.
• Senior leadership has approved the project with an understanding of the timeline, cost, and what the project will and will not deliver.
• We have allocated budget for the full project lifecycle: build, deployment, year-1 operation, and ongoing maintenance — not just the build phase.
• We have a defined plan for what happens if the project does not meet its success criteria — a no-go threshold and a decision process for scope adjustment or discontinuation.

How to Score Your Responses

Score each dimension: 1 point for each "Yes" answer. Score each dimension out of 5, then total across all 4 dimensions (maximum 20).