AI for Corporate Workflows

Good candidates are frequent tasks with enough reliable data and a clear cost of delay or error. For example, a Kenyan retailer might forecast stock-outs for fast-moving goods, while a service team might summarise approved call notes. “Build a chatbot” is not a use case until it names users and a result such as shorter response time.

A pilot should test the riskiest assumption first: perhaps whether staff will use the tool, data is sufficient, or the answer quality is safe. Compare pilot results with the current process, not with an imagined perfect future. Keep the pilot small enough that people can learn without disrupting customers.

A customer-service lead complains that 'the team drowns in email'. That is a symptom, not a use case. Investigation shows the real pattern: forty per cent of inbound messages are status enquiries whose answers already exist in the order system. The use case becomes precise: automatically match status enquiries to order records and draft a reply for agent approval, measured by first-response time and agent hours freed. Scope excludes complaints and refund requests, which stay fully human. Six weeks later the team can report first-response time halved on the covered category — a defensible result because the use case was narrow enough to measure.

Before scoring a use case as attractive, check what it needs to run every day: which systems hold the input data, whether access is permitted for this purpose, how messy the records are, and who fixes them when they break. A brilliant use case with data trapped in scanned PDFs and personal spreadsheets may cost more in preparation than it returns in benefits — while a duller use case with clean, accessible data ships in weeks. Rank candidates on value multiplied by data readiness, and be suspicious of any proposal that cannot name its data sources.

Name the weekly process, handoffs, and approval step — then pick document AI, copilot, or automation.