AI becomes useful when it removes friction from a task people already need to do. The starting point should be the workflow, not the model.
Good candidates include summarising long documents, classifying enquiries, extracting structured data, comparing a document against requirements, drafting a first response or searching a large knowledge base.
Not every task needs AI. Calculations, validation rules, database lookups and fixed business logic are often better handled with normal code. AI can sit around those steps where interpretation or language is genuinely useful.
Important workflows should make it clear when a person needs to review, correct or approve the output. That is especially important where an incorrect answer could affect a customer, a financial decision or a regulated process.
Useful metrics might include time saved per case, response time, percentage of drafts accepted with minor edits, support volume reduced or completion rate improved.
Projects such as CVBoostr illustrate this approach: the value is not “AI” in isolation. It is turning a document-heavy comparison task into a more structured workflow.
Have a repetitive workflow you think AI could improve? Describe it to us.