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What I Should Have Figured Out Before Building an Automation

Automation works best after you understand the workflow and the outcome you need. Here’s how to map the work, choose an intervention, and test whether it helped.
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I built the automation before I understood the problem. That order matters: automating a poorly understood workflow can preserve unnecessary steps, encode workarounds, or make a bad handoff happen faster. The better starting point is the outcome you want, followed by a clear view of how work actually happens today.

What should you figure out before automating a process?

Start by describing the problem and the observable result that would make it better. “Automate intake” names a mechanism; “reduce waiting between receiving a request and assigning it” describes an outcome. Useful outcomes might include fewer handoffs, less rework, shorter waits, or more consistent completion.

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Choose one or two measures tied to that outcome, and record a baseline before changing the process. Depending on the problem, that might mean elapsed time, number of handoffs, rework, or completion errors. Microsoft recommends defining success criteria before deployment; without a baseline, a faster-looking workflow may still fail to improve the thing that matters. Microsoft Learn’s process guidance starts with the problem and outcome rather than a tool choice.

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How do you find the real problem in a workflow?

Map a contained process from a clear start point to a clear end point. Document the steps in the order they really occur, including who or what performs each step, the inputs required, the outputs produced, handoffs, delays, and workarounds. Include the process boundaries: what triggers the work, and what counts as completion.

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Use a flowchart, a digital diagram, or a low-tech layout with pencil and paper or sticky notes. The format matters less than capturing daily practice instead of an idealized version. As Microsoft Learn puts it, “Effective process mapping starts with understanding what actually happens today, not what is documented or intended.” Microsoft Learn and Business.govt.nz’s process-mapping guidance both support mapping current work before redesigning it.

Should you fix the process before automating it?

Often, yes. Automation is one possible intervention, not the default answer. If unnecessary steps cause the delay, remove them. If inconsistent work causes errors, standardize it with a checklist or template. If people lack the context or skill to complete a step, training may be more appropriate. Integration can help when repeated manual transfers between systems are the actual bottleneck.

Automation is a stronger candidate when a step is stable, repetitive, and based on usable data. Before choosing, assess each pain point against practical considerations:

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  • Frequency and repetition: How often does the work happen, and does it follow a consistent pattern?
  • Data readiness: Are the required inputs available in a usable digital form?
  • Error tolerance: What happens if the result is wrong, and can it be corrected?
  • Effort and impact: How much time or cost does the step consume, and who is affected?
  • Reversibility: Can you pause the change or undo an incorrect action?

The Australian National AI Centre presents templates, rules-based automation, streamlining, training, and integration as alternatives to AI, and distinguishes technical fit from business impact. Its prioritization scores and client-onboarding figures are illustrative examples, not general performance benchmarks or promises for other organizations. The Centre’s opportunity guidance is useful for thinking through that distinction.

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What should you design before building?

Define the intended use and what is out of scope. Identify who will be affected, what could go wrong, what would make the change worthwhile, and how you will recognize success. Make explicit which tasks belong to people and which belong to the system.

Design for exceptions as carefully as for the routine path. Decide who reviews uncertain or consequential cases, where exceptions go, who can escalate them, and how the workflow can be paused or stopped. The higher the impact of an incorrect action, the stronger the case for human review before that action is taken.

Document the redesigned workflow, responsibilities, decision rules, quality standards, training needs, and relevant data sources or systems. Give people doing the work a way to report problems and record design changes, so feedback can inform the next version. The National AI Centre’s project-planning guidance emphasizes planning roles, risks, and feedback rather than treating deployment as the end of the work.

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How do you test whether an automation helped?

Begin with a small proof of concept or pilot, not a broad rollout. Compare its results with the baseline using the measures you chose for the original problem. Check both speed and quality: a shorter cycle time is not an improvement if it creates more errors, rework, or problems for customers or staff.

  1. Run the pilot in the real setting. Use a limited scope and make sure staff know how to report unexpected results.
  2. Review results against the baseline. Measure the original outcome, not simply whether the automation ran successfully.
  3. Investigate failure patterns. Look for recurring exceptions, poor inputs, incorrect decisions, and steps where people still need workarounds.
  4. Adjust and repeat. Incorporate feedback, update the workflow and safeguards, then measure again before expanding.

A successful run proves that a workflow can execute; it does not, by itself, prove that the underlying problem improved. Microsoft’s strategy guidance and the National AI Centre’s measurement guidance both point toward defining intended results and monitoring performance over time.

What to do when you have already built it

If an automation is already in use, pause expansion long enough to understand the workflow it affects. Map the current process—including the automation, human checks, and workarounds—then identify the outcome it should improve. Establish a baseline where possible, review exceptions and risks, and decide whether the right next step is to simplify, redesign, add safeguards, or keep the automation. If the workflow can trigger consequential actions, use an appropriate review or pause mechanism while you assess it.

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