Practical guides for AI projects.

Use conventional automation when the steps are known, a copilot when a person should stay in the loop on every output, and an agent only when the task needs judgement across tools and you can define what it is allowed to do.

Your data is ready enough for an AI project when the specific data that one use case needs is accessible, understood, representative of real work, of known quality and permitted for that use. You do not need a perfect data estate first; you need to assess readiness one use case at a time.

Retrieval-augmented generation lets a language model answer from your own documents by finding the relevant passages first and citing them. It is usually the right choice when knowledge changes often, answers must be traceable, and access differs by user; fine-tuning suits changing how a model behaves rather than what it knows.

Measure an AI system against a baseline taken before launch, using a small set of outcome metrics, quality guardrails that must not get worse, the full cost per unit of work, and adoption. Be explicit about what the numbers can and cannot attribute to the AI.

A pilot is ready for production when it has a named owner, an evaluation set that reflects real work, integrations that run under their own identity, documented controls, and a plan for monitoring, cost and support. If any of those are missing, fix them before you scale.