5 business process automation examples worth starting with

1. Route enquiries to a responsible person

When a new enquiry arrives, record the source and selected service, check for duplicates and assign it to an owner using agreed rules. Create a task so that a message does not disappear inside a shared inbox. If the service or destination is unclear, send it to a review queue.

For example, a website request can go to the person responsible for new projects, while an existing-customer request follows a different route. The automation should preserve the original message. Do not silently discard incomplete enquiries or let an AI guess determine whether a prospect is worth speaking to.

2. Check whether a document package is complete

When files arrive, compare them with an agreed checklist and flag missing items. Keep file names, submission dates and status together so the reviewer can see what is still needed. A checklist with explicit requirements may be sufficient without AI.

If AI helps extract information from a document, keep its output separate from the original and require review before consequential updates. A system may identify a possible date or category; a person should resolve ambiguity. Plan access and retention around the information being processed, including any third-party service that receives it.

3. Send approval reminders with context

Create reminders for open approvals approaching their due time. Include a link to the request, the owner and the decision needed. Stop reminders once the request is approved, rejected or cancelled.

Avoid turning a workflow problem into a notification problem. Agree an escalation path and a sensible reminder schedule. Test reassignment, absence and paused requests. The system should remind the responsible person without making the approval on their behalf.

4. Prepare a recurring operations summary

Pull the agreed project, task and approval data into a scheduled summary. Include the reporting period, exceptions and data refresh status. Send the summary only to the people who need it.

If a source fails, mark its information as unavailable and notify the owner. Do not publish an apparently complete report using missing values as zero. Start with a consistent factual summary; AI-written commentary is optional and should not invent causes for changes in the numbers.

5. Draft follow-ups for human review

Use the enquiry and approved business information to prepare a reply draft. The draft can summarise the request, suggest clarifying questions and point to the next step. A person checks the message before sending it.

This is a practical use of AI because the output is visible and editable. Restrict the information the assistant can use, and do not let it invent prices, promises or policies. NIST’s AI risk framework describes the need to define responsibilities around human and AI roles. Apply that idea by naming the reviewer and documenting when a draft must be escalated.

Design the failure path before switching it on

Every workflow needs a response to a duplicate event, unavailable integration, revoked access and partial completion. Record what happened and make failures visible to an owner. Retrying a failed action must not accidentally create duplicate tasks or send the same message repeatedly.

Test with representative examples in a controlled environment. Include edge cases, then run a small monitored pilot. Keep a documented manual fallback and agree how the automation can be paused. Measure completion, exceptions and review effort before describing any time saving as a result.

• Trigger: what starts the workflow?
• Inputs: which information is required and permitted?
• Action: what changes, and in which tool?
• Review: who checks uncertain or consequential output?
• Recovery: how are failures, retries and duplicates handled?
• Ownership: who monitors and improves the process?