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Before automating a workflow, record how it performs today and decide what a better result would look like. That baseline lets you compare the same work after deployment and decide whether to improve, expand, or retire the automation. You do not need a sprawling scorecard: start with one or two measures tied to the outcome that matters.
Start with the workflow and the outcome
Define the process before choosing a tool. Describe where the workflow starts and ends, what counts as one unit of work, and who owns the result. For example, a team might define a case as one complete request, from submission to resolution. If different teams use different boundaries or definitions, their measurements will not be comparable.
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Next, name the intended improvement in practical terms: shorter completion time, fewer errors, more consistent handling, or lower operating cost. Digital.gov’s RPA Playbook treats process selection, assessment, improvement, operational metrics, and business value as connected parts of an automation program. Its guidance is framed for federal agencies, but the measurement principle is useful more broadly.
Choose one or two signals that reflect the problem and outcome. Microsoft’s business strategy guidance recommends outcome-first planning, baseline measurement, and using evidence to guide what happens next. More metrics are not automatically more useful.
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Choose measures that match the job
Use measures that describe the workflow, not just what the automation software is doing. A bot being active or completing many runs does not by itself show that the organization is getting a better result. First decide what a unit of work is and use a consistent observation period.
| Question | Possible signals | Define before measuring |
|---|---|---|
| How much work moves through the process? | Run or case count; throughput | The unit of work and the observation period |
| How long does it take? | End-to-end duration; processing speed | The start and end events, and whether waiting time is included |
| How reliably does it finish? | Failed runs; error count or rate; error trends | What qualifies as a failure and the time window for the count or rate |
| How much does the path vary? | Routes or variants; bottlenecks | Which routes matter and how cases are grouped |
| Does the result matter to the organization? | Cost, financial performance, consistency, or another outcome-specific measure | How the operational change connects to the intended business result |
These are candidates, not a universal scorecard. Microsoft’s process-map documentation describes runs, average duration, failed runs, failure rate, error trends, routes, and bottlenecks; AWS also identifies processing speed, errors, consistency, and financial performance as possible measures. Pick the few that help answer your particular decision.
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Capture a baseline you can repeat
Measure the workflow before deployment using the definitions you intend to use afterward. Where available, use system timestamps and records rather than relying on memory. If cases must be timed manually, record the method and apply it consistently. Capture enough context to interpret the result, including volume and route mix when those affect the workflow.
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- Process boundary, owner, and unit of work
- Start and end events for any duration measure
- What counts as success, failure, or an error
- Observation period and data source
- Chosen outcome signal and any relevant context, such as route mix
A baseline is the reference for the after measurement, not a promise that every later change was caused by automation. AWS guidance on measuring success and ROI recommends an operational baseline for ROI calculations and monitoring financial and operational performance against it.
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Compare the same signal after deployment
Repeat the measurement with the same process boundary, unit of work, event definitions, observation period, and outcome signal wherever feasible. For instance, if the original duration included time waiting for approval, excluding that wait after deployment would make the comparison misleading.
Put a change in context. A higher case count, a different mix of routes, or a changed error tolerance can affect results independently of the automation. A before-and-after comparison can show what changed; by itself, it does not establish that automation caused the change. The official guidance supports baseline-to-after measurement but does not prescribe one universal experimental design or attribution method.
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For failure measures, report counts alongside rates and state the observation window. A rate without its underlying volume can be hard to interpret: a small number of cases may make a modest number of errors look unusually large, while a busy period may hide a rising count behind a similar rate.
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A small, stable workflow may be measured from ordinary system records. More involved processes can have multiple routes, handoffs, or bottlenecks that a single average obscures. Microsoft’s process-map documentation describes basic and advanced insights for examining process runs, duration, failures, routes, and bottlenecks.
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Microsoft marks the Insights feature as preview in that documentation. It lists a Power Automate Premium license for basic insights, and additional Process Mining capacity and permissions for advanced insights. Product status, licensing, capacity, and access requirements can change, so check the current documentation and your organization’s entitlement before relying on those features.
Turn the measurement into a decision
Compare the result with the intended outcome and the error tolerance appropriate to the workflow. AWS notes that measurement criteria and error thresholds should reflect the chosen autonomy level; a process that can act without human review may need a different tolerance from one where people check each result.
Use the evidence to choose a next step: improve the workflow or automation if the result falls short, expand it when the outcome is strong and the process remains reliable, or retire it if it does not deliver enough value. Measurement is most useful when it changes an operational decision, rather than serving only as a launch report.
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For standards context, the ITU lists Recommendation F.748.55 (03/2025) as in force. Approved March 16, 2025, it addresses technical requirements and evaluation methods for RPA systems, including workflow execution, monitoring, risk management, and analysis. It is not a prescribed business KPI list, so it does not replace choosing measures that fit your workflow and outcome.
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