AI Implementation Best Practices
AI fails when it is adopted as a vague ambition instead of a defined problem. These practices keep an AI effort scoped to work that repays the effort and the risk.
Pick a defined problem, not a vague ambition
Start with a specific task: extracting fields from these documents, answering these questions, triaging these requests. A concrete problem has a concrete success measure.
Start with high-frequency, low-stakes tasks
Automate the repetitive work with a low cost of being wrong first. That builds confidence and a baseline before you touch anything high-stakes.
Keep a human in the loop for high-stakes decisions
Where an error is expensive or irreversible, AI should draft and a person should confirm. The loop is not a sign of weakness; it is the control that makes AI safe to deploy.
Measure accuracy and cost per task
Track how often the output is correct and what each task costs to run. AI earns its place when it is accurate enough and cheaper than the manual alternative — not when it is merely impressive.