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Before automating a workflow, record how it performs today and choose one or two measures that show whether the change improves the outcome your organization cares about. Then compare those same measures after deployment. A baseline makes automation a testable operational decision—not just a report of bot activity.
Start with the workflow and the outcome
Describe the work the workflow performs before choosing an automation tool. Define where the process starts and ends, what counts as one unit of work, and what better performance would mean. Depending on the job, the desired result might be faster processing, fewer errors, more consistent handling, lower cost, or another operational or financial improvement.
Name a process owner who can confirm the boundaries and interpret the results. Digital.gov’s federal RPA Playbook treats process selection, assessment, improvement, operational metrics, and business value as connected parts of an automation program; its guidance is useful beyond government as a planning reference, not a requirement for private organizations. Read the Digital.gov RPA Playbook.
Choose measures that answer a real question
There is no universal KPI set for every workflow. Select a small number of signals tied to the intended outcome, and define each one precisely enough to measure consistently. Microsoft recommends choosing one or two value signals, establishing a baseline, and using the results to guide what happens next.
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| Question | Possible measure | What to define |
|---|---|---|
| How much work arrives or gets completed? | Run count, case count, or throughput | The unit of work and the observation period. A run is not necessarily equivalent to a completed business case. |
| How long does the work take? | End-to-end duration or processing speed | The start and end events, and whether the measure includes waiting time, manual review, or rework. |
| How reliably does it finish? | Failed runs, error count, failure rate, or error trends | The failure definition and time window. Track counts alongside rates so a rate change can be interpreted in light of volume. |
| Does the process take different paths? | Routes or process variants; bottlenecks | Which paths are expected and which indicate delay, exception handling, or avoidable variation. |
| Did the organization get the intended benefit? | Outcome-specific operational or financial measure, such as consistency or cost | How the operational change connects to the business result, and what other costs or effects matter. |
Microsoft’s process-map documentation describes measures such as runs, failed runs, failure rate, average run duration, routes, and error trends. AWS also identifies errors, processing speed, consistency, and financial performance as potential measures. These are options, not a scorecard to collect in full. Microsoft: Improve automation monitoring with process map; AWS: Measuring success and ROI.
Capture a baseline before deployment
Measure the current workflow using the definitions you intend to reuse after automation. Use system timestamps and records where available; if a measure requires manual observation, sample cases consistently and document how they were selected. The baseline does not need to be elaborate, but it must be clear enough that someone can repeat it.
- Set the boundary: write down the start event, end event, included steps, and exclusions.
- Define the unit: specify whether you are counting a request, case, transaction, completed item, or another unit.
- Choose the period: select an observation window that captures the normal work pattern for the process, and record its dates.
- Record the signals: capture the chosen outcome measures and the underlying counts or timestamps needed to interpret them.
- Note context: record relevant changes in workload, route mix, staffing, policy, or error tolerance that could affect results.
AWS recommends using an operational baseline when calculating ROI and monitoring financial and operational performance against it. The appropriate error threshold is not the same for every automation: AWS advises that measurement criteria and thresholds should reflect the selected level of autonomy. AWS guidance on measurement and ROI.
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After deployment, measure the same workflow boundary, unit, events, period, and outcome signal where feasible. Compare like with like: a change in average duration is difficult to interpret if the post-deployment period contains a different mix of work or excludes time that the baseline included.
Show both the before-and-after values and the context needed to understand them. Changes in case volume, route mix, staffing, or error tolerance may help explain a result. A simple before-and-after comparison can show that a measure changed, but by itself it does not prove automation caused the change; the cited guidance supports baseline comparison, not a universal experimental design or attribution method.
Use process analysis when basic tracking is not enough
If work takes many routes or bottlenecks are hard to locate, a process map or process-mining approach may reveal patterns that a single average hides. Microsoft documents basic process-map insights, including runs and durations, and advanced insights for route or variant and bottleneck analysis.
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Microsoft marks the Insights feature as preview. Its documentation lists a Power Automate Premium license for basic insights and additional Process Mining capacity and permissions for advanced insights. Confirm current availability, licensing, capacity, and access requirements in the product documentation before making these features part of a plan: Microsoft process-map documentation.
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For a broader standards perspective, ITU-T Recommendation F.748.55, approved March 16, 2025 and listed as in force, covers technical requirements and evaluation methods for RPA systems, including workflow execution, management and monitoring, risk management, and analysis and insight. It is not a universal list of business KPIs or a mandate to use any particular baseline. ITU-T Recommendation F.748.55.
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Use the measured outcome to choose the next operational step: improve the workflow or automation, expand it where the result is repeatable and acceptable, or retire it if it does not deliver enough value. If the results are mixed, inspect which errors, routes, or parts of the process are driving them before deciding whether to change scope or measures. Microsoft’s value-measurement guidance frames measurement as evidence for deciding whether to scale, improve, or retire an agent—not merely as a launch report. Microsoft: Agentic AI maturity model—Business strategy.
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