AI for operations: finding and fixing process bottlenecks

Map before you automate

Automating a broken process makes it fail faster. Write down each step, who does it, how long it takes, and where rework happens.

Spot AI-suitable steps

Look for these patterns.

1. Reading documents to extract information (invoices, forms, emails).
2. Classifying or routing requests.
3. Drafting standard responses or reports.
4. Summarising updates for handovers.

Pilot with an owner and a check

Assign an owner, define success, and keep a human review for anything with consequences. Run for enough cycles to see real variation.

Pilot: classify incoming vendor emails into 6 categories.
Owner: procurement lead.
Check: agent confirms category before routing for first 4 weeks.
Measure: misroutes per week, time to first response.

Scale carefully

Expand only after results are stable, and document the process so it survives staff changes.

Common process-improvement mistakes

These prevent pilots from delivering real value.

1. Automating before understanding where time actually goes.
2. Choosing a rare task, so improvements barely matter.
3. Removing human checks too early.
4. Failing to document the new process, so it breaks when staff change.

Worked example: invoice intake

An operations team receives invoices by email in different formats. They map the process and find most time goes to reading invoices and typing fields into a sheet. They pilot an AI extraction step that fills the sheet, with validation rules checking totals and required fields.

Invoices that fail validation go to a person. After several weeks, the team measures time per invoice and error rates against the baseline, then decides whether to expand the pilot to other document types.