Process Mining Before Automation: Find the Right Work to Fix
Process mining can reveal where work actually stalls before you automate it. Use event data to prioritize a workflow, validate changes and avoid scaling waste.
A recurring digitalisation mistake is automating the workflow that is easiest to describe, rather than the one creating the most operational friction. Teams may know the official procedure, yet not how work moves across queues, systems and exceptions in practice. Recent coverage of expanded digital services, including the IRS mobile app launch reported by kens5.com, is a reminder that a digital touchpoint is only one part of a service: the underlying process must also handle submissions, decisions and follow-up. Process mining offers a way to examine event records from existing systems and map actual process paths. Used carefully, it helps leaders decide what to improve before investing in automation.
Start with an observable process, not a tool
Process mining uses timestamped event data associated with cases or transactions to reconstruct how work moved through systems. A case might be an invoice, service request, application or customer order; events might record submission, review, approval, rework and completion. The value is not a polished diagram by itself. It is a comparison between the intended process and the routes visible in the data: where items wait, loop back, skip steps or require manual intervention. This is different from asking staff to recall the process in a workshop. Interviews explain why a path exists; event data can show how often and where it appears. Neither alone tells the whole story, so combine them.
Before selecting software or commissioning automation, define one bounded process and its start and finish. Confirm which systems record its events, whether case identifiers can be linked across them, and whether timestamps have consistent meaning. If a work item disappears between systems, the resulting map will be incomplete. Likewise, a status change does not necessarily mean work occurred at that moment. Treat the initial model as a diagnostic hypothesis, not an authoritative account. Operations owners should validate representative paths with the people doing the work, then distinguish necessary exceptions—such as compliance review—from avoidable handoffs or duplicate entry. This framing keeps the exercise tied to an operational decision rather than an abstract data project.
- Choose a process with a clear case identifier, business owner and measurable outcome before evaluating vendors.
Prioritize by impact, feasibility and risk
A process map can surface many deviations, but not every deviation merits automation. Prioritize candidates using three questions: does the friction affect an important outcome, can the cause be changed, and is the required data reliable enough to support action? Waiting time at a handoff may matter more than a longer but necessary review. Repeated rework could indicate confusing policy, poor input quality or inconsistent system validation; automating the existing loop would preserve the underlying defect. Pair event patterns with operational measures such as completion time, backlog, error correction or customer effort, where available. Establish a baseline before changing the workflow, and agree how the owner will judge improvement afterward.
Next, assess feasibility and control boundaries. A predictable, rules-based routing decision may be a suitable automation candidate; a judgement-heavy exception may need clearer guidance or human review instead. Identify the systems of record, the data fields needed, integration constraints and who is authorized to approve changes. Map personal or sensitive data involved and limit analysis to what the purpose requires. Do not assume that an analytics platform’s access to logs grants permission to use them for every purpose. Security, privacy, retention and audit requirements should be part of the design, especially when an automated step can change a customer outcome, payment, entitlement or regulated record.
- Rank proposed improvements against a documented baseline, accountable owner, implementation effort and control risk.
Design a safe pilot and measure the result
Turn the selected opportunity into a small intervention with explicit boundaries. Document the current path, the proposed change, eligible cases, excluded cases and a fallback route. For example, an automated completeness check may return a request for missing fields, while ambiguous cases remain with a trained employee. Make the decision visible in the relevant system, record its reason and retain a way to correct errors. Test ordinary paths and important exceptions using realistic cases before rollout. A pilot should also cover operational failure: what happens if an integration is unavailable, an event is delayed, or the automation receives incomplete data? These are process design questions, not details to postpone until launch.
Measure both intended gains and unintended effects. Compare the pilot with the baseline using agreed indicators—for instance, elapsed time, rework, unresolved backlog or manual touches—while checking that quality and control obligations have not worsened. Avoid attributing every change to automation if workload, policy or staffing changed at the same time. Review actual exceptions with frontline staff, then decide whether to expand, revise or stop. Keep ownership with the business process lead; engineering can build integrations and monitoring, but cannot define acceptable operational trade-offs alone. Once a workflow is stable, maintain its rules, event definitions and access controls as the surrounding systems evolve.
- Set a review date and stop condition before the pilot begins, not after a problem appears.
Digitalisation works best when process understanding precedes software selection and automation. Event-based process analysis can expose actual routes and focus attention, but it does not replace staff insight, sound data governance or business ownership. Start with one bounded workflow, verify the evidence, fix causes rather than symptoms, and test a controlled change against a baseline. Scale only when the process owner can demonstrate better outcomes without weakening quality, privacy or control. This disciplined sequence makes automation an operational improvement rather than a faster way to reproduce existing friction.