Introducing AI into a business: choosing the first workflow
Business AI implementation begins with a repeatable workflow that has an owner, lawful data, an error cost and a measurable result—not with a model name.
Choose a bounded first workflow
Prefer a frequent task with clear input, reviewable output and accessible historical examples. Classification, grounded knowledge search, drafting and document field extraction are safer starts than irreversible autonomous decisions.
Choose work with enough repeated examples, a reviewable output and a clear owner; novelty alone is not a business case.
Measure the baseline
Record volume, waiting time, labour, error rate and rework before automation. Define quality as an observable rate, such as correctly routed enquiries or documents accepted without correction.
Build the baseline from current time, error and rework before introducing the model, otherwise improvement cannot be attributed credibly.
Separate pilot from production
Test on held-out real examples, run beside the current process and compare before and after. Production additionally requires access control, action logs, spend limits, retries, monitoring and a reliable stop mechanism.
Keep deterministic rules outside the model and document when a person must approve, correct or reject its proposed result.
Know when to postpone
If rules change weekly, data sits in personal chats and nobody can define a good result, stabilise the process first. Automating disorder accelerates errors as well as work.
Promotion to production needs monitoring, fallback, support ownership and a budget for evaluation after models or source data change.
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