Process mining for low-code automation identification

Process mining extracts actual process execution data from enterprise system event logs (SAP, Salesforce, ServiceNow) to automatically generate process maps that reflect how processes truly execute. Traditional process mapping relies on interviews, workshops, and documentation — showing how processes are supposed to work, not how they actually work. Process mining captures every exception, rework loop, and deviation that happens in practice but never appears in official documentation. This makes process mining far more accurate for identifying automation opportunities because it is based on real execution data rather than idealised process descriptions.

The most common reason low-code automation programmes fail to deliver expected ROI is poor target selection — teams automate visible or politically important processes rather than the ones that actually consume the most time and resources. Process mining eliminates this problem by providing objective, data-driven prioritisation: it identifies which process steps have the highest volume, the longest handling time, the most rework, and the most rule-based decision patterns — all strong predictors of automation ROI. Teams using process mining to select automation targets consistently achieve 3–5× higher ROI than those selecting targets through interviews or intuition.

Process mining requires event logs with three mandatory fields: a case ID (the unique identifier of the entity being tracked — an order number, ticket ID, or invoice number), an activity name (what happened — "Order Received", "Invoice Approved"), and a timestamp. Optional enriching attributes include the user who performed the activity, cost or revenue associated with the case, outcome (approved/rejected), and any relevant business attributes. Most enterprise systems (SAP, Oracle, Salesforce, ServiceNow, Jira) generate this data as part of their normal operation and can export it via pre-built connectors or SQL queries.

Celonis is the market leader in pure-play process intelligence, offering the deepest analytical capabilities, the most SAP connectors, and ML-based root cause analysis. It is best suited to large enterprises running complex multi-system processes, particularly SAP-centric environments. UiPath Process Mining is tightly integrated with the UiPath RPA and automation platform, making it the natural choice for organisations already using UiPath for automation. It includes task mining capabilities and has a more accessible UI for business analysts. For Microsoft 365 environments, Power Automate Process Advisor provides basic process mining built into the Power Platform at no extra cost.

Conformance checking compares the process as it actually executes (discovered from event logs) against a reference model of how it is supposed to execute (the official process design). It identifies and quantifies deviations: steps performed out of order, steps that are skipped, activities performed by unauthorised users, and mandatory approvals that are bypassed. Conformance checking is valuable for compliance and audit purposes — it provides objective evidence of policy adherence or violation — and for identifying rework and inefficiency that has become normalised in operational practice.

Process mining ROI is measured through the automation improvements it enables. Calculate: (annual time saved by automations selected via process mining × average hourly cost) + (reduction in rework cost) + (reduction in compliance incidents) − (process mining licence cost + implementation cost). In practice, the ROI case is built case by case: for each automation opportunity identified by process mining, quantify the current annual handling time (volume × average handling time per case), the percentage reducible by automation, and the cost of the automation project. Sum across the automation backlog to produce a portfolio business case.

Task mining captures desktop-level user activity — mouse clicks, keystrokes, application navigation — to understand exactly how individual tasks are performed, particularly in multi-application workflows that don't have complete system event logs. Use task mining when system event logs are insufficient to understand task-level detail (e.g. manual copy-paste between spreadsheets), when you're designing RPA bots and need precise UI interaction sequences, or when you want to understand variation in how different users perform the same task. Task mining complements process mining: process mining gives the end-to-end process view, task mining gives the granular task-level view.

The best candidates for initial process mining are high-volume, cross-system processes with good event log coverage: order-to-cash (from order receipt to payment collection), purchase-to-pay (from purchase request to supplier payment), incident-to-resolution (IT service management), and hire-to-retire (HR processes). These processes touch multiple systems, generate rich event logs, and typically contain significant rework and inefficiency that isn't visible in official process documentation. Avoid starting with processes that have poor event log coverage, highly variable case structures, or are primarily knowledge-work with few system touchpoints.