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Building an automation before defining the problem can turn a flawed or confusing workflow into a faster version of the same flawed workflow. Before choosing a tool, decide what outcome should improve, map how the work actually happens, and check whether automation is the right fix. Then pilot the change and measure whether it helps.
Why building first can lock in the wrong workflow
Automation acts on a process; it does not establish that the process is necessary, correctly understood, or producing the outcome people need. If the real bottleneck is an unnecessary approval, unclear ownership, or inconsistent inputs, automating the visible task may preserve the cause of the problem.
That is the practical lesson behind “I built the automation before I understood the problem”: choosing a mechanism too early can narrow attention to what the tool can do instead of what the work needs. The remedy is not to avoid automation, but to make it one option considered after the workflow and desired result are clear.
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Define the outcome before choosing a tool
Start by stating the problem in ordinary language and describing what would be observably better. Possible outcomes include fewer handoffs, less rework, shorter waits, or more consistent completion. These are examples, not guaranteed effects of automation.
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Choose one or two measures linked to the problem and record a baseline before changing the process. For example, if the problem is waiting, measure the current elapsed time; if it is rework, record how often work must be corrected. Microsoft’s process guidance recommends defining success criteria before deployment. A measure makes it possible to distinguish a workflow that merely runs from one that improves the intended outcome.
Map what happens today
Choose a contained process with a clear start and end. Record the steps in the order people actually perform them, including informal workarounds. For each step, note who or what does it, the inputs required, the output produced, and the usual time involved. Mark handoffs, waits, duplicate entry, and exceptions.
Do not map the ideal process from a policy document if day-to-day practice differs. Microsoft Learn puts the point plainly: “Effective process mapping starts with understanding what actually happens today, not what is documented or intended.” A current-state map gives the people involved a shared view of the work and can expose where the friction really sits.
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The map can be a flowchart, a digital diagram, pencil and paper, or sticky notes; Business.govt.nz’s process-mapping guide describes these as ways to lay out a process. The medium matters less than accurately capturing steps, boundaries, and handoffs.
Decide whether automation fits the pain point
Assess the candidate task against practical factors: how often it occurs, how repetitive it is, whether usable digital data is available, how costly or consequential an error would be, the effort it consumes now, and the impact on staff or customers. Also ask how easily a wrong result could be undone. A task may be technically automatable but still be a poor candidate if inputs are inconsistent or the impact of an error is high.
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Automation is only one intervention. The Australian Government’s National AI Centre suggests considering options such as templates, simple rules, streamlining, training, and system integration alongside AI. Match the intervention to the cause:
- Remove steps when the process includes work that does not contribute to the outcome.
- Standardize the work with a checklist or template when inconsistent execution is the problem.
- Clarify ownership or train people when confusion or capability is causing delays or errors.
- Integrate systems when repeated transfers between tools are the source of friction.
- Automate a stable, repetitive step when the task is well understood and the expected benefit outweighs the setup and risk.
The National AI Centre’s illustrative assessment example scores document chasing 23 out of 25, custom quote writing 20, and document tracking 18. Those scores demonstrate one prioritization method; they are example values, not general research findings or a ranking to apply to another organization. Its opportunity guidance distinguishes technical fit from business impact and advises addressing high-impact, low-fit problems before reconsidering AI.
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Scope the workflow and its safeguards
Before building, write down the intended use, what is outside scope, who will be affected, what could go wrong, and what result would make the change worthwhile. Define the division of work: which decisions or actions belong to the system and which require a person.
Plan for exceptions rather than designing only for the routine case. Specify how unusual inputs, missing data, failed steps, or uncertain results are handled; who receives an escalation; who reviews quality; and how the workflow can be paused or stopped. Where an automated action could have significant consequences, include human review at the appropriate point.
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Document responsibilities, decision rules, quality standards, training needs, and the systems or data sources involved. Keep a way for people doing the work to report problems and suggest edits. The National AI Centre’s implementation guidance emphasizes roles, review, feedback, and monitoring as part of putting an AI workflow into use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Pilot the change and measure the result
Begin with a small proof of concept or pilot rather than expanding a new workflow everywhere at once. Compare its results with the baseline using the measures chosen for the original problem. Check both speed and quality, ask the people doing the work what changed, and look for recurring failures or exceptions. Adjust the workflow before expanding it.
A successful run is not, by itself, evidence that the change is valuable. The relevant question is whether the process performs better in the setting where it operates, without shifting cost, errors, or effort elsewhere. Microsoft’s guidance on planning and process mining supports measuring against defined success criteria; the National AI Centre likewise recommends monitoring and iteration in implementation.
One National AI Centre client-onboarding example describes an expected reduction in onboarding time from 5–7 hours to 2–3 hours, and document-chasing rounds from three to one. These are illustrative expected benefits in that example, not measured general results or a prediction for other organizations.
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