The AI Implementation Checklist: Before You Build Anything
The most expensive AI implementation mistakes happen before a single line of code is written — in the planning and scoping phase. This checklist ensures every AI implementation starts with the right foundation. Run through it before any build begins.
Run This Before Every AI Implementation
Problem definition
☐ The problem is stated in specific, measurable terms (not improve efficiency but reduce X from Y hours to Z hours per week). ☐ The current cost of the problem is quantified (hours per week times hourly cost, or revenue at risk, or error rate times cost per error). ☐ Success is defined with a specific metric and a measurement method. ☐ The 60-day success check is scheduled in the calendar before the build begins.
Data quality assessment
☐ The data the AI will operate on has been reviewed for completeness (what percentage of records are missing required fields?). ☐ The data has been reviewed for consistency (is the same information formatted consistently across records?). ☐ Any data quality issues that would produce unreliable AI outputs have been addressed or explicitly accepted as a known limitation. ☐ The data source can be accessed via API or export at the frequency the automation requires.
Process documentation
☐ The current process (what a human does today) is documented in enough detail to design the automation (trigger, inputs, steps, outputs, exceptions). ☐ Any judgment calls within the current process are explicitly identified and mapped to rules or escalation paths. ☐ Edge cases (the unusual inputs that occur 5 to 10% of the time) are documented and handled in the design. ☐ The quality criteria for a correct output are documented and testable.
Platform selection
☐ The right platform for each component of the implementation has been selected based on the requirement, not the default preference. ☐ Make.com for automation, Bubble.io for custom applications, GoHighLevel for CRM workflows, Claude or OpenAI for AI processing. ☐ The selected platforms have been verified to support the required connections and data flows. ☐ The ongoing cost of the platform stack has been calculated and budgeted.
Human review and error handling
☐ A human review stage has been designed for the first 2 weeks of operation. ☐ The threshold for AI confidence below which outputs route to human review has been defined. ☐ The error handling for each module that could fail has been designed (what happens if the AI API is down, if the data source is unavailable, if the output does not parse correctly). ☐ An alert mechanism is configured to notify the owner if the automation encounters errors.
Ownership and maintenance
☐ A named owner has been assigned who is accountable for the implementation’s success. ☐ The documentation plan is in place — how will the system be documented so it is maintainable by someone other than the builder. ☐ A monitoring schedule has been defined — how often will the execution logs be reviewed in the first month. ☐ The team who will use the automation has been involved in the design and will receive training before launch.
Launch and measurement
☐ A controlled launch plan is in place — a pilot with real data before full deployment. ☐ The before measurement (current state) has been documented with actual numbers. ☐ The measurement method for the after state is defined and will be applied at 30 and 60 days. ☐ A communication plan is in place for informing relevant team members of the change and their role in the new workflow.
📌 This checklist should take 2 to 4 hours to complete for a typical automation project. Any box that cannot be checked represents a gap that will create problems during or after the build. The time invested in completing the checklist before building is reliably recovered from the problems it prevents. The most expensive projects are those that skipped the checklist and discovered the gaps during build — when addressing them requires rework rather than planning.
What if I cannot answer all the checklist questions before starting?
The questions you cannot answer are the most important ones to resolve before building. If you cannot define success in specific, measurable terms — the implementation will have no clear definition of done and no evidence of ROI. If the data quality issues are unresolved — the AI will produce unreliable outputs that undermine adoption. If the error handling is not designed — the automation will fail silently at some point and nobody will know. Treat unanswered checklist questions as blockers, not acceptable gaps. Build them before building anything else.
Is this checklist the same for large and small implementations?
The checklist is designed for any implementation — a simple Make.com scenario and a complex Bubble.io application. The depth of each item scales with the complexity: for a simple automation, the process documentation might be a one-page description; for a complex application, it might be a 10-page requirements document. The items are the same; the depth of treatment is proportional to the complexity and risk of the implementation. Never skip items for a simple implementation — the simplicity of the implementation does not reduce the importance of clear problem definition or data quality assessment.
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