Map the current process
Write down the inputs, decisions, outputs, tools and hand-offs in the workflow. Identify where people spend time copying information, searching documents, classifying requests or drafting a first response.
Measure the current process where possible: volume, handling time, error rate and the cost of a mistake. Without a baseline, it is difficult to know whether automation helped.
Choose the right level of automation
Some steps are better handled by deterministic rules, validation and ordinary integrations. AI is more useful when inputs are unstructured, language-heavy or require flexible extraction and summarisation.
A hybrid workflow is often safer: software handles validation and routing, AI proposes or extracts information, and a person reviews decisions with meaningful consequences.
Design for uncertainty
Model output can be incomplete or wrong. Add confidence checks where appropriate, source references for retrieval, clear error states and a way to route uncertain cases to a human.
Do not give an AI workflow access to more data or actions than it needs. Treat user-provided content and external documents as untrusted input.
Pilot and measure
Run a limited pilot on a real workflow. Compare quality, handling time, review effort and failure cases against the original process.
Expand only when the measured outcome justifies the cost and operational responsibility of maintaining the workflow.