Implementation checklist
AI implementation works better when the business treats it as workflow change, not tool installation. This checklist helps define the work, the role AI plays, the controls needed, and how success will be measured.
Start with a workflow that happens often, creates visible friction, has repeatable steps, and can be measured. Examples include enquiries, intake, quote preparation, follow-up, reminders, internal knowledge, reporting, and request routing.
Avoid starting with a broad promise like 'improve operations with AI'. The workflow needs a clear beginning, ending, owner, and success measure.
AI may answer, draft, summarise, classify, route, remind, extract, check, or prepare work for review. The business should choose the role intentionally rather than letting the tool decide.
Controls should be part of implementation, not added after something goes wrong. Define data rules, approval points, escalation paths, logging, exception handling, and the person responsible for reviewing the workflow.
Use real examples from the business before going live. Include routine examples, edge cases, sensitive requests, incomplete information, customer complaints, and situations where AI should hand off instead of continue.
The implementation should improve something the business already cares about: response time, missed requests, rework, consistency, follow-up, ownership, visibility, cost, or staff capacity.
Practical checklist
