Before automating a workflow with AI, establish how work actually moves through it. The approved flowchart may show a clean sequence, while real cases loop back, wait in queues, or skip a step for a legitimate reason. Process mining uses recorded events to reconstruct those paths. It can expose a bottleneck worth fixing, but it does not decide on its own whether the fix should be a rule, a form change, an integration, or an AI model.
The useful business question is not “Can we draw a process map?” It is “Can we identify a change that improves the outcome of a complete case?” This guide explains how to define a small process-mining study, check the event data, interpret the result with the people doing the work, and choose a measured AI automation pilot only where it fits.
What a process-mining study can reveal
Imagine a service request moving from submission to document review, assignment, decision, and completion. Each recorded transition is an event. A shared case identifier connects events belonging to the same request; an activity name describes the step; a timestamp places it in sequence. Microsoft's process-mining data guidance specifies these essential fields, and IBM's event-log documentation describes a comparable structure.
With usable data, a team can compare common and unusual paths, locate repeated rework, and distinguish a long wait from a long activity. It can also examine whether a route that looks exceptional on paper is normal in practice. These findings are a starting point for investigation, not proof that a particular employee or team is underperforming.
Recorded events are not the whole story. A phone call, informal approval, offline spreadsheet, or undocumented workaround will not appear unless it is captured in the source systems. Review real cases and talk to the people who handle them before treating a map as a decision-grade account of the process.
Why discovery should precede AI automation
Speeding up one step may not shorten the end-to-end journey. An AI classifier can assign a request instantly, while the receiving team still lacks capacity. The model's latency falls; the customer's wait does not. Similarly, routing incomplete requests faster may increase the volume of returns for missing information.
Process mining shifts attention from model output to case outcome. Once a repeated delay is visible, ask whether it comes from an unclear form, unnecessary approval, missing integration, unstable policy, or genuinely difficult interpretation of unstructured input. The simplest effective fix might be ordinary software or a policy change. AI adds evaluation, oversight, and maintenance costs, so it should solve a specific problem rather than decorate a workflow.
The article on running automation in shadow mode covers testing a proposed solution without letting it change live records. The discovery work here comes earlier: deciding what deserves a solution at all.
Choose a narrow process and a clear case boundary
Start with a process that has an identifiable beginning and end, such as a service request from receipt to final response. Avoid a first pilot that spans many companies, unrelated request types, and several unconnected systems. A narrow scope makes missing events and conflicting definitions easier to find.
Bring the process owner, a data owner, and operational staff into the initial review. Agree on the case identifier, the meaning of a successful outcome, the status names that matter, and the data that should remain outside the analytical extract. A customer is rarely a useful case ID if one customer can raise several requests. An order ID may not be sufficient when one order has multiple payment attempts. The wrong boundary produces a plausible-looking but misleading map.
Select the pilot for both analytical value and ability to act. A process with rich logs but no authority to change the underlying workflow may yield insight without a useful automation experiment. A process with poor event capture may need instrumentation before it needs an AI model.
Prepare and question the event log
At a minimum, each event needs a case ID, an activity, and a reliable time. Separate start and end times can improve the distinction between active work and waiting. Optional fields such as channel, request type, or responsible unit can support comparison, but every additional attribute needs a reason to be collected and an access boundary.
Trace a few records manually. Do all events for one case share an identifier? Do systems use the same time zone? Is a supposed completion event earlier than submission because of clock conversion? Are duplicate events being mistaken for repeated work? A chart created from flawed ordering should not be used to justify procurement or staffing decisions.
Normalize labels with care. Several names may describe the same activity, but two similarly named activities may represent different business decisions. Preserve meaningful differences, especially returns for missing information and re-reviews after an exception. Keep a small data dictionary naming who owns each definition and where it came from.
Keep personal data out of the extract where possible
The process path usually does not require a customer's phone number or the full text of correspondence. Pseudonymous case IDs, relevant categories, and event times may be enough. Limit access to the pilot population, define retention, and plan deletion. Training or evaluating an AI model on the data is a separate decision from mapping a process; do not treat the extract as blanket permission for model use.
A practical example: enterprise service requests
Suppose a company believes that slow service comes from manual request classification. It plans to buy an AI router. A review of its event log instead shows that long-running cases often return from the first assignment to a “more information required” state. This is an illustrative scenario, not a claim about EasySaz customers or measured industry rates.
The team inspects a small sample of returned cases with frontline staff. It finds that the intake form does not show the evidence needed for different request types. A conditional form and a document-presence check may address the bottleneck before any model is considered. AI might later suggest missing evidence for free-form requests, but the rule for contacting a customer and the human owner of that action still need to be explicit.
Measure the same outcomes again after the change: rate of returns, time to first handling, customer waiting time, and final resolution. Do not equate more automated actions with better service. More follow-up contacts or premature closures would undermine a claimed improvement. Our guide to auditing an automation outcome in the destination system explains why issuing a command is not the same as achieving the intended result.
Where AI belongs in the design
AI may be worth testing when inputs are varied and unstructured, manual interpretation is costly, and proposed outputs can be checked against evidence. Examples include suggesting a request category, extracting fields from a document, or flagging a case for review. Even then, define the model's scope, uncertainty handling, review route, and stop condition before production use.
If the real problem is an ownerless queue, an ambiguous approval right, or a broken system connection, a language model is unlikely to fix it. For deterministic decisions, a transparent rule may be cheaper and easier to audit. Compare alternatives using full implementation and maintenance costs, tolerable error, explainability, and customer impact—not just accuracy on a clean test set.
When discussing a project with EasySaz AI solutions, bring one documented bottleneck, a permitted data sample, the desired case outcome, and a list of decisions that must remain human-owned. That is a more useful brief than asking for “AI across every process.”
Turn discovery into a testable pilot
Write down a few bounded hypotheses rather than a large list of AI opportunities. For each, record the log evidence, a plausible alternative explanation, the person able to change the workflow, and the consequence of an incorrect change. Choose an experiment whose side effects can be contained.
Establish a baseline for a defined case population and period. Look beyond an average: the distribution and the slowest cases may matter most. Keep definitions stable before and after the intervention. Changes in volume, seasonality, or acceptance policy can distort a simple comparison. Small samples should be reported as early signals, not as proven return on investment.
Run the candidate in observation mode or behind human approval first. Store the model's suggestion, the actual decision, and the eventual case result separately. Define reasons to pause, such as an increase in returns, incorrect routing of sensitive cases, or inability to explain a decision. A regular review of errors may show that the model needs work, that the underlying policy needs work, or that automation is the wrong approach.
What belongs in the decision report?
A concise report should state the process scope, data quality limits, observed routes, bottleneck validated with staff, non-AI options, proposed intervention, implementation cost categories, and acceptance criteria. A process map without those definitions is insufficient for an investment decision. Avoid ranking individuals from incomplete logs; the purpose is to improve how work is organized.
A sound next step
Process mining before AI automation helps teams target an end-to-end bottleneck instead of accelerating an arbitrary task. Start with a small process, trustworthy case and time data, operational validation, and a repeatable baseline. Compare a simpler fix with an AI-assisted one, then pilot only the option whose outcome can be measured and reversed.
If you want help defining the scope, send EasySaz a brief description of the process and a non-sensitive sample. A responsible proposal should follow a review of log limitations and decision ownership, not precede it.