How to identify workflows worth automating
A practical planning guide for ai automation.
Start with the business decision
Teams handling repetitive enquiries, CRM updates, reporting, or operational data across disconnected tools. Start by identifying one business goal, a decision-maker, and the constraints that will shape the engagement. A useful brief explains what needs to change and how you will assess the work.
What should you compare in proposals?
- Workflow mapping and feasibility assessment
- Integration design with agreed systems and permissions
- Automation implementation, exception handling, and testing
- Documentation, handover, and an agreed support plan
Compare proposals against the same scope. A lower headline fee may exclude production, tools, advertising spend, implementation, or ongoing support. Ask for a written breakdown rather than assuming these costs are included.
Which tasks should stay under human review?
Approvals, sensitive decisions, and uncertain AI outputs need a defined review path. Access permissions, data handling, monitoring, and fallback procedures should be agreed before a workflow goes live.
Which workflow is a sensible first candidate?
Look for a repeated task with stable inputs, clear rules, and a measurable baseline. Record task volume, time spent, error rate, tool costs, and the review effort needed after automation. Start with a limited pilot and a manual fallback.
Questions to settle before signing
- What is included, and what is explicitly excluded?
- Which costs recur and which are paid once?
- What approvals and source material are needed from us?
- What is the baseline, reporting period, and data source?
- What happens at handover or when the engagement ends?
Next step
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Prepared by Hyperlinq Technology. Editorial review: 6 November 2026.
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