AI Automation Implementation Checklist for a First Production System
A practical checklist for scoping, testing, launching, and measuring a first production AI automation system.
— Craig Major
AI implementation starts before the model or automation platform is selected. A production system needs a defined workflow, an owner, usable data, clear human decisions, tests, and a way to measure what changed.
1. Define the first boundary
Write the start event and the finished outcome in one sentence. For example, “a qualified enquiry arrives” to “the right person has reviewed the context and recorded the next action.” Avoid starting with “automate sales” or “add AI everywhere.”
Name what is explicitly out of scope. A small boundary makes testing and ownership possible.
2. Establish the baseline
Record the current volume, time per case, delay, error or rework rate, and the business outcome the process supports. If a measure is unavailable, mark it as a gap rather than estimating a success story.
3. Map the work and data
Document triggers, inputs, transformations, decisions, handoffs, and exceptions. Identify which system owns each field. Note permissions, retention, and the places where a person must see the original context.
4. Design the human boundary
Decide whether the system may act, prepare a recommendation, or wait for approval at every decision point. People retain judgment, relationships, sensitive decisions, and accountability. The system can prepare context and reduce repetitive coordination.
5. Build the smallest useful slice
Start with one workflow and one measurable outcome. Connect only the systems required for that slice. Add logging, retries, and a visible exception queue before adding more automation.
6. Test normal and difficult cases
Test complete records, missing fields, duplicates, conflicting values, permissions failures, timeouts, and an explicit human override. Record expected and actual results. A demo that works only with a perfect input is not a production system.
7. Launch with ownership
Name the operational owner, technical owner, escalation path, review cadence, and documentation location. Train the people who use the output. Make it easy to report a wrong recommendation or a missing handoff.
8. Measure after launch
Compare the same baseline measures after a defined period. Also track overrides, exceptions, missing context, and adoption. Do not claim revenue or savings until the attribution method is agreed.
Choose the next step
Use the AI Success Audit when the first workflow is not yet clear. If the workflow is defined, discuss an AI Automation System with testing, documentation, and human checkpoints.
What is the difference between a pilot and a production system?
A pilot proves that a technical path is possible. A production system also has an owner, exception handling, security boundaries, documentation, monitoring, and a measurement plan.