Many companies have already gone through basic digitalisation. ERP, CRM, and other IT systems are in place. Business processes are documented in policies or BPMN diagrams.
Yet a common paradox remains: digitalisation does not deliver the expected level of standardisation, predictability, or efficiency. Instead, companies face delays, hidden losses, and increasing complexity in day-to-day operations.
To understand this gap between documented processes and reality, more organisations are turning to Process Mining. It allows them to compare the target process defined in policies with what actually happens in practice, and to analyse deviations, delays, and rework. This changes not only how processes are understood, but also the quality of management decisions.
One of the core challenges of process management is that documented processes often reflect how work is supposed to happen, not how it actually happens.
Process Mining uses event logs from IT systems to show the real picture and build a digital twin of processes — a model based on actual execution data.
This makes it possible to identify so-called “shadow processes” — the real actions employees take that may not align with formal procedures. Instead of relying on interviews and assumptions, companies get a factual view of operations, including unnecessary approvals, manual steps, and returns to earlier stages.
In effect, process analysis shifts from assumptions to an X-ray of operational reality.
Customer expectations are rising. They expect fast, predictable service without delays. This pushes companies to respond faster and implement changes more quickly.
Traditional process analysis methods — interviews, surveys, observation — are slow, resource-intensive, and often subjective. By the time results are ready, they may already be outdated.
Process Mining turns analysis into an ongoing capability. Instead of reviewing processes once a year, companies can see deviations almost in real time and act early when something starts to go wrong.
Process analysis stops being a one-off project and becomes part of daily management.
A “growth at any cost” strategy eventually leads to operational complexity and inefficiency. In an environment of uncertainty, expensive capital, and volatile demand, companies are forced to focus more on internal efficiency.
Making hidden losses visible
Operational processes include many activities that do not create value: rework, unnecessary approvals, inefficient communication with customers. Process Mining makes these activities visible and measurable — in time, cost, and impact.
Scaling without adding headcount
Instead of hiring more people to handle growing volumes, companies can remove bottlenecks. Smoother process flow allows existing teams to handle more work within the same time, directly improving process profitability.
For a long time, decisions about process changes were based on the judgement of managers and stakeholders. In large organisations, this often distorts reality: leaders tend to see processes as they are designed, not as they actually operate.
Evidence for transformation
Process Mining provides hard evidence based on user actions in IT systems. It removes the need for debates about where the problem is and shifts the focus to how to fix it. Data shows the exact share of deviations from the standard path and their real financial impact.
Predictable outcomes
The same data makes it possible to test scenarios and estimate the impact of changes before implementing them. This helps prioritise initiatives with the highest return and reduces the risk of poor decisions.
Companies can finally move from collecting data to extracting value from it. Most organisations already have the necessary data in their systems, but only a few turn it into operational advantage.
As decisions become more data-driven, the next logical step is the use of AI.
AI requires a structured environment and clear cause-and-effect relationships to learn effectively. Applying AI to process analysis without real data leads to the same problems as human judgement — or worse. AI will generate assumptions instead of insights.
Process Mining provides the foundation:
This turns AI into a management tool rather than an experiment.
Digitalisation alone does not make processes manageable. Process Mining closes the gap between documented and actual execution. It responds to growing business complexity and creates a foundation for further automation and the use of AI.
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