Start from the workflow, not the model
The strongest AI projects begin with a painful, repetitive process — triaging tickets, extracting data from documents, drafting routine responses — and ask where judgement is actually required. Choosing a model comes later.
Integration patterns that respect legacy systems
- API facade: expose legacy capabilities through a thin, well-documented API layer that AI services can call safely.
- Retrieval-augmented generation (RAG): index existing documents and records so language models answer from your data, with citations.
- Event-driven augmentation: listen for events (a new order, a support email) and enrich them asynchronously without changing the core system.
- Human-in-the-loop agents: let AI draft or propose actions while people approve anything with consequences.
Guardrails matter more than prompts
Production AI needs the same discipline as any other system: access controls scoped to the minimum data required, audit logs of inputs and outputs, evaluation suites that catch regressions, and clear fallbacks when the model is uncertain.
Prove value in weeks, not quarters
Pick one workflow, define a measurable outcome — time saved, accuracy, throughput — and ship a narrow pilot. A working pilot earns the trust and budget that a slide deck never will.
