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What Process Intelligence does that process mining can’t
In the debut episode of the ‘Trust the Process Podcast’, we explore the origins and transformation of process mining through the lens of a grocery store navigating decades of change - from empty milk shelves in the 1970s to the rise of AI-powered supply chains in 2025.
The following is a transcript of the podcast, edited and organized for readability.
The evolution of Process Intelligence
It’s October 1979. You walk into your local grocery store, looking for a carton of milk. But the shelves are empty. No fresh milk today. You ask the store manager what happened, and they shrug—“Looks like we underestimated demand. The next shipment won’t be here until next week.”
Back then, ensuring fresh milk was available was a manual process. Store managers walked the aisles, checked stock levels, and placed orders by phone or fax. There was no real-time data, no automated tracking—just experience and gut instinct. And sometimes, they got it wrong.
Fast-forward to today. Now, the entire supply chain is connected. Stores predict demand, automate orders, and optimize deliveries in real time—ensuring that fresh milk is always in stock.
This is the story of over 40 years of evolution of this field. From manual tracking to workflow automation, to process mining, and now Process Intelligence.
In this episode, we’ll hear from four leading experts who have shaped the field. Wil van der Aalst, often called the 'Godfather of Process Mining,' will explain how Process Intelligence is evolving with AI. Lars Reinkemeyer, a pioneer in applying Process Mining in large enterprises, and Patrick Lechner, Head of Process Mining and RPA at BMW, will share insights on how businesses have adapted to these advancements. And Process mining consultant Gabriela Galic, will break down how this redefined the work of consultants nowadays and back then.
The Birth of Process Mining
Before we had process mining, how did businesses even understand their processes? Think back to our grocery store example. In the early days, stores relied on simple observation and instinct. There were no IT systems, no centralized tracking—just employees estimating demand and placing orders.
Managing Director for Process Mining Consulting at OFI Services and ex-Deloitte consultant Gabriela Galic explains how process consulting has changed over the years.
Gabriela Galic: “It started all with process modeling in the eighties and nineties, where we did interviews with customers to map out processes. Then process mining came in place, giving us the ability to analyze and visualize processes with data.”
As businesses digitized, workflow management systems emerged. For the first time, knowing the levels of stock of milk didn't rely on gut feeling but on data-driven insight.
This also had an impact on the operations in stores as employees would follow a clearly defined process of scanning, stocking the milk, and reporting back to the IT system.
The theory was that if you could model your process, everything would work exactly as it was designed.
Wil Van Der Aalst explains how different the reality really was.
Wil Van Der Aalst: “So in the mid-nineties, there were many tech organizations that were selling workflow management systems. There were around 200 at the time. So really, it was a big field. And the basic idea of workflow management technology was that you model your processes and then you push a button and you generate a system that is then ensuring that the process is executed as it is modeled. And of course, this is a bit naive, and that's why we got into the field of process mining.”
Having processes run through workflow management systems like e.g. SAP, Oracle or Microsoft Dynamics was a huge step towards better digitization and standardization.
However the execution of those processes did not always follow the prescribed target model. Wil tells us more about what how we should think about processes bottom-up instead.
Wil Van Der Aalst: "Yeah, so in the beginning when people were applying workflow management technology, they had this idea, we just model the processes and after modeling the processes, you push a button and you generate a system that is then making sure that the processes are executed as they are modeled. But this was not happening and that was for me, the trigger to start working on process mining where you don't start from modeling, you start from the actual data and the actual processes as they are being executed."
Process mining reveals the gap between theory and reality. So how does it actually work? Let’s go back to our grocery store example.
Every time a carton of milk is scanned—when it arrives, when it's stocked, and when it’s sold—that data gets logged.
Process Mining takes those digital breadcrumbs—timestamps, locations, interactions—and pieces them together to show the real flow of stock replenishment.
Say milk keeps running out every Friday afternoon. Process mining helps pinpoint the cause. Maybe deliveries are late. Maybe demand spikes at a certain time. Before, store managers had to rely on their gut feeling. Now, they can see exactly what’s happening and fix it.
That was the spark that launched process mining as a field. Fast forward, and it’s now a recognized Gartner category with a massive global community driving it forward.
Process Mining gave us X-ray vision into business operations. But as technology evolved, process mining itself was shaped by new innovations.
Process Mining Over the Years
Process Mining changed the game. It showed businesses how their processes actually ran—not just how they were supposed to. And over the years, new technologies emerged that influenced the field further.
Gabriela Galic: “2012, big data was a big hype where we were relying on massive data sets, which also supported process mining even more because we can analyze more data, so to say. And then afterwards we stepped into cloud computing where it helped us basically to be more flexible, to be more affordable, and to also be more accessible to everybody, and that more people are using process mining. And right afterwards, the trend of RPA came into place, which was a perfect match to process mining because then we are not only started to analyze processes, but at the same time we had a solution to fix inefficiencies and to improve our processes with bots.”
Big data took things to the next level. Our grocery store? Now part of a global chain, tracking millions of transactions in real-time to spot trends, optimize stock, and cut waste.
Then came cloud computing. Suddenly, store managers could monitor and tweak supply chains from anywhere, making sure shelves stayed stocked with exactly what customers needed.
With RPA, grocery stores moved from simply understanding inefficiencies to actively resolving them. Now, stock levels trigger automatic restocking. Self-checkout kiosks streamline payments.
Gabriela Galic: “Process intelligence and RPA work very well together. First, process intelligence provides transparency, helping organizations identify bottlenecks and inefficiencies. Then, RPA steps in to automate repetitive manual tasks, improving efficiency and accuracy. It’s this combination that makes process intelligence so powerful.”
With the rise of No-Code/Low-Code platforms, process optimization was no longer limited to technical experts.
Gabriela Galic: “And last but not least, I have to mention it, the democratization of all the tools with low-code, no-code developments. It was also possible for people or not that tech savvy or who doesn't have deep tech skills to really improve their processes with no-code platforms or developments. And this really gave another boost or enhancement for process intelligence."
Grocery store managers and employees, without deep IT knowledge, could now build custom workflows to streamline operations. For example, a store manager could create a dashboard to track perishable inventory levels and trigger discount promotions for items nearing their expiration dates—without needing a developer.
The day to day of our grocery store operations and process consultants has drastically changed. And Process mining is about to be changed by the next fundamental tech disruption - moving from process mining towards Process Intelligence.
Some call it the next evolution. Others say it’s just a new label. After this break, we’ll find out.
Meet Process Intelligence
Predictive and GenAI are rapidly changing the field. Instead of just fixing inefficiencies, AI helps prevent them before they happen. Are we in a new era—one that calls for renaming the field?
Wil Van Der Aalst: “So the term process intelligence has been around for quite a while. For example, if you look at a process mining manifesto from 2011, you will see that the term is already being used here. I think the big innovation is the inclusion of AI technologies and machine learning. So you can see process intelligence as process mining, incorporating ideas from machine learning and AI.”
Process intelligence isn’t just process mining with a fresh coat of paint. As Wil explains, it’s about bringing AI into the mix - and using AI to look forward, not just back.
Wil Van Der Aalst: “The real shift with process intelligence is that we’re no longer just looking at the past. We are using AI to make forward-looking predictions. We are asking: What will happen next? How can we avoid bottlenecks before they happen? This predictive capability is what separates process intelligence from traditional process mining.”
Our grocery store can now improve supply chain logistics by coordinating deliveries based on predictive analytics. Process intelligence can forecast demand based on weather patterns, local events, and historical sales data, ensuring the right amount of fresh produce is stocked.
And that’s exactly the shift BMW has made.
Patrick Lechner has seen it firsthand—over the past decade, he’s watched BMW evolve from basic process discovery to real-time, intelligent process optimization.
He explains how they moved from simply seeing processes to actively improving them.
Patrick Lechner: "We started very simple with process mining—just discovering and understanding our processes. But as we moved forward, we realized it wasn't enough to just see what was happening; we needed to improve and act on it. That’s when we moved towards process intelligence, using real-time insights and automation to not just monitor but actually optimize processes at scale."
And that’s where the real payoff happens. Insight alone doesn’t move the needle—action does.
At Siemens, Lars Reinkemeyer saw firsthand that visualizing a process is just the start.
The real value comes from what you do with that insight.
Lars Reinkemeyer: "For me, definitely, it's a big step forward. You might want to call it a category, but I probably would leave this to Gartner and other smart people who define those categories. For me, the key difference is that process mining, as it says—mining—is focusing on the insight. So you mine a process to visualise how the process actually goes. You mine the process to say, on my purchase-to-pay process, I have a hundred thousand process variations, which has been a great thing we did in the first couple of years at Siemens, visualising the transparency, showing an X-ray. But that's not what it's about. It’s not about the insight; it's about the action you trigger and the value you bring to the organization."
Process intelligence is the next evolution of process mining.
Process mining showed us what happened — Process Intelligence helps us predict and improve in real time.
That’s where real value is created.
Take our grocery store. Process mining flags delivery delays and demand spikes.
But process intelligence? It’s the smart co-pilot that knows your business inside out and predicts when milk will run low and reorders before shelves are empty—maximizing efficiency and revenue.
Talking about knowing our business inside out - AI is not the only trend that has triggered the rise of a new process intelligence era.
Object-centric process mining (OCPM) provides large contextual information which feeds into our AI-driven decision making and makes it more accurate.
Wil Van Der Aalst: "Object-centric process mining enables organizations to analyze the root causes of process inefficiencies across multiple departments. If customer orders are delayed, the root cause might be in sales, production, or procurement. OCPM links all involved objects, providing a holistic view of where problems originate."
We will talk about this more in future episodes.
The problems that kept our grocery store manager up at night have changed. No more guessing when milk will run out. No more reacting too late.
Today, Process Intelligence doesn’t just track what’s happening—it predicts what will happen and acts before issues arise. It’s the difference between waiting for an empty shelf and ensuring it’s always stocked. And as AI and object-centric process mining take this even further, one thing is clear: The future of processes isn’t just about seeing—it’s about knowing and doing.
Listen to Trust the Process Podcast
Join us next time on Trust the Process as we explore why AI without Process Intelligence is like navigating with an outdated map—and how real-time process context is the key to making AI truly work.
Because when processes work, everything works.
Image Credit: Gifford Photographic Collection, Oregon State University. (21 May 2025). Berg's Supermarket, Salem, self-serve delicatessen Retrieved from https://oregondigital.org/concern/images/pg15bf33d
