Process Mining — The X-Ray of the Process Nobody Has Done Yet

Three Hours That Disappear Every Day

On paper the process looks simple: an invoice arrives, an invoice gets paid. In reality that same invoice passes through seven screens, four systems, and two departments before anyone clicks “approve.”

Nobody sees this. Not because people are not looking — because they are looking in the wrong places. Operational reports tell you how many invoices were processed. They do not tell you that 23% of them went back to the previous stage because a single field was missing. And that this loop costs your company hundreds of hours per year.

Process mining changes this situation from the ground up.

What Process Mining Is and Why It Matters Now

Process mining is a technology that analyses the digital traces left by every step of a business process — timestamps, identifiers, statuses — and reconstructs the actual flow of operations. Not declarative, not drawn on a whiteboard during a workshop. Real.

Every invoice, order, service ticket, complaint — when it passes through an IT system, it leaves behind an event log. Process mining collects these logs and builds a visual process map from them. The effect resembles an X-ray: you can see every loop, every blockage, every place where a case got stuck for three days because someone was waiting for an approval email.

The process mining market is growing at a pace that is hard to ignore. In 2025 its value was estimated at USD 2–3.6 billion (Fortune Business Insights, Research and Markets). Forecasts point to a CAGR exceeding 40% per year. It is one of the fastest-growing segments of business software in the world — and not without reason.

Deloitte’s Global Process Mining Survey 2025 studied over 120 organisations. The results? 80% of users confirm that process mining delivers real business value. 46% reduced process cycle times. 41% reduced manual work. And 49% report increased satisfaction with technology over the last twelve months. At the same time, only 25% of companies connect process mining with AI — which means most of the potential is still lying dormant.

What You See When You Switch On the X-Ray

Imagine that the order approval process in your company has — according to the documentation — seven steps. Process mining shows that in practice there are forty-three. Forty-three path variants through which an order travels across the organisation.

Accenture

After connecting a process mining tool to the order processing system across 50 countries, they discovered 14,000 process variants for approving requisitions. Fourteen thousand ways a single purchase order could travel through the system.

In one country the average approval time was 60 hours. After identifying bottlenecks and implementing changes — it fell to 15 hours. A reduction of 75% without replacing the IT system, without new software. All it took was seeing what was actually happening.

This is not an exception. It is a pattern that repeats across companies of every size:

NEC Corporation

After deploying process mining, eliminated 700 hours of annual manual work in the invoice processing process alone.

Credito Emiliano

Reduced credit card approval time from four days to one — saving €500,000 per year.

Johnson & Johnson

Achieved a 30% reduction in touch time across operational processes.

ThyssenKrupp Elevator

Identified a redundant approval step that added 2–3.5 days to the service cycle — and discovered that 10% of all requests were being cancelled due to missing data, a problem that could have been solved with a single input validator.

The common thread across all these cases? None of these companies knew about the problem before they saw it. The data had been in the system for years. What was missing was the tool capable of reading it.

Three Things Process Mining Does Better Than a Traditional Audit

Traditional process mapping relies on interviews, workshops, and declarations. People describe how they think the process runs. The problem is that people do not lie — they simply do not remember exceptions, do not see the loops, and do not count the time spent waiting.

Process mining works differently on three dimensions.

First — objectivity. The algorithm has no opinion. It reads event logs and reconstructs what happened. When your manager says “the process takes two days” and the data shows an average of eight days with a median of five — you know who to talk to.

Second — scale. A person can analyse a few dozen cases during a workshop. Process mining analyses all of them — every invoice, every order, every ticket from start to finish. Patterns that were lost in statistical noise become visible.

Third — continuity. A workshop gives a snapshot. Process mining gives a film. You can compare January with February, the Krakow branch with the Gdańsk branch, supplier A with supplier B. You see trends, not just states.

Where Process Mining Delivers the Greatest Return

High-volume transactional processes — that is the natural territory of this technology. Accounts payable, order-to-cash, procure-to-pay, complaints handling, client onboarding. Wherever the same process repeats itself hundreds or thousands of times per month, and every delay or loop costs money.

Deloitte reports an interesting shift in company expectations. In 2021, 77% of users cited process improvement as the primary goal. In 2025 that figure fell to 61% — but at the same time the proportion expecting cost savings rose from 46% to 59%. Companies have moved from the phase of “we want to know” to the phase of “we want to earn from that knowledge.” That is a signal of maturity.

A separate dimension is regulatory compliance. Process mining can compare the actual process flow against a reference model and immediately highlight deviations. For companies in regulated sectors — finance, pharmaceuticals, energy — this is not a gadget. It is an audit tool that operates 24 hours a day.

Process Mining in the QA10 Architecture

In our knowledge base we define process mining as the algorithmic analysis of system logs to reconstruct the actual flow of processes. In practice, QA10 goes further.

Process mining is the first step in our methodology — the diagnostic phase that precedes any solution design. Before we propose automation, before we draw the first RPA diagram, before we calculate the implementation NPV — we need to know how the process actually looks. Not how it should look. How it looks.

This approach eliminates one of the most common mistakes in digital transformation: automating the wrong process. If your invoice approval process has 14,000 variants, automation will not solve the problem — it will entrench it. First you need to see. Then simplify. Only then automate.

We tell our clients plainly: the result of a process mining analysis may be uncomfortable. It may show that the problem does not lie in the IT system, but in the way department X communicates with department Y. Or that a procedure on which the budget has depended for three years generates more costs than value. That is good information — because only on that basis can you make decisions that genuinely change something.

And only then does the cost layer make sense — Activity Based Costing puts a price on every step, NPV and IRR translate those prices into an investment decision. Process mining is the first diagnostic layer. Without it, every subsequent method builds on sand.