Before investing in an AI automation project, it helps to know where your organization stands on data quality, technical integration, and internal readiness. This 15-question checklist gives you a score and specific next steps based on your answers.
Answer 15 questions about your data, technical setup, and organization to get a readiness score and specific recommendations. Your answers stay in your browser. Nothing is submitted to a server.
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A low score doesn't mean you shouldn't automate. It means there is groundwork to do first, usually around data quality, process documentation, or internal ownership.
Most people complete it in 5, 10 minutes. Be honest with your answers, the recommendations are only useful if they reflect your actual situation.
No. All scoring runs in your browser. Nothing is submitted to a server.
Good automation removes repetitive data entry and routing, not judgment calls. Teams typically redeploy saved hours into higher-value work. If a workflow requires relationship nuance or legal sign-off, keep a human in the loop.
Simple automations with clean data sources often go live in three to six weeks. Workflows touching multiple systems, approval chains, or legacy exports usually need eight to twelve weeks including testing.
Unclear ownership of edge cases. Before development starts, document what happens when data is missing, when confidence is low, and when someone overrides the automation. Undefined edge cases become scope creep.
Always. Run the automation on one team, location, or ticket type for two to four weeks. Measure false positives, time saved, and override rate before expanding.
Ask for a reference in your industry, a clear list of what is included in maintenance, and who owns prompt or rule changes after launch. Fixed-price scoping beats open-ended hourly billing for first projects.
We build custom AI systems. Projects start at $5,000.
