Automation should remove a reliable source of friction, not add an impressive diagram. Start with work that repeats often, follows visible rules, and produces an outcome someone can verify.
List the handoffs, not only the tasks
Repetitive work often hides between tools: copying a form into a CRM, renaming documents, notifying a manager, creating a project, or assembling a weekly report.
Write down the trigger, required information, decisions, destination, owner, and common exception for each handoff. This exposes where automation can remove waiting and duplicate entry.
Prioritize stable, frequent work
A strong first candidate happens often enough to matter and follows rules the team already understands. Avoid starting with rare processes, politically unsettled workflows, or decisions that depend on deep judgment.
- Frequency and time spent
- Cost of delay or error
- Clarity of inputs and rules
- Ease of checking the result
- Availability of APIs or reliable data access
Build a human recovery path
Every workflow eventually receives missing data, an expired credential, or an unavailable service. Production automation needs logging, retries, alerts, ownership, and a documented manual fallback.
Keep consequential approvals visible to a responsible person. AI can classify, extract, summarize, and draft; it should not silently make financial or sensitive customer decisions.
Measure the before and after
Record the current turnaround time, touches, error rate, and backlog before implementation. After launch, compare the same measures and review exceptions with the people doing the work.
A small automation that runs reliably is a better foundation than a broad one nobody trusts. Expand only after the first loop is observable and owned.