We often read about Process Mining in the context of artificial intelligence and machine learning. This is despite the fact that Process Mining by its very nature has nothing to do with AI or Machine Learning at first. So where does that myth come from? The answer is obvious: marketing. After all, AI sells well and is therefore eagerly communicated. Tech start-ups like to wear the AI label, as a study by London-based MMC Ventures has shown. However, around 40 percent of all “AI start-ups” in Europe do not use artificial intelligence at all or at least not in their core product. Especially in the area of process analysis, however, AI and machine learning have real buzzword status. In this article we explain why Process Mining has nothing to do with AI initially, but can still be regarded as a disruptive digital innovation – and where LANA actually uses artificial intelligence to its users’ benefit.
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This is how Process Mining works. Without AI.
The process data form the basis for the application of Process Mining. IT systems automatically log all actions performed in the system. This happens regardless of whether the process activities are automated or executed manually by the employees. In addition to the activity itself, these logged events, so-called event logs, contain one or two time stamps, a unique identification number (Case ID) and associated attributes. With this information, the process can be statistically analyzed. And this is precisely what Process Mining does.
The log files provide a fact-based framework for making the process more transparent and thus more effective. Process Mining visualizes the activities in a responsive process model, on the basis of which targeted analyses of performance and resource expenditure are developed.
The future: The self-optimizing process
Now we have an idea of how Process Mining transforms and evaluates process data. However, artificial intelligence or machine learning has not yet been used at any point. AI is neither necessary for the visualization nor for the analysis of the data.
By definition, artificial intelligence is the automation of the intelligent behavior of IT systems. This should enable the system to tackle problems independently and make decisions on its own. In Process Mining, the system has to transform the data into a certain form and carry out statistical procedures. However, the decision on how to interpret the results and which measures are to be derived from them is up to the users.
Process Mining is an extremely accurate and efficient method to analyze business processes – but not a substitute for human decisions. However, this does not mean that artificial intelligence is irrelevant for automated process analysis. On the contrary, it is conceivable that process analysis with Process Mining will be fully automated in the future. The end product would then be a system that independently optimizes the process and creates simulations for various scenarios and forecasts.
This is why LANA is smarter
Process Mining identifies undesirable process developments and deviations, bottlenecks and other risks. This already offers companies great value because it specifically shows where problems and vulnerabilities lie and how they affect overall process performance.
But even more interesting is the question: Why? What is the cause of these problems? To answer this question as precisely as possible, Lana Labs has developed a complex machine learning algorithm. Machine learning is a branch of artificial intelligence in which digital systems learn from real data. In LANA, such an algorithm is used for the automated root cause analysis, which examines the root causes of critical process weaknesses. In the same way, the causes of high performance activities can be uncovered as well. The algorithm analyzes the process data according to patterns and rules and automatically identifies the most probable problem causes. You can read in this article how this algorithm operates in detail.
AI is not a necessity for digital innovation – but it helps
As we have seen, Process Mining alone has very little to do with artificial intelligence. Because AI is not really necessary for the desired results – transparency, evaluations of process performance, identification of deviations. At this point, however, LANA Process Mining is even smarter: LANA gets to the bottom of problems with machine learning. Based on the identified root causes, decision-makers can implement targeted and effective improvement measures. Artificial intelligence is therefore not a mandatory criterion for digital, disruptive innovation such as Process Mining solutions. But often AI can multiply the potential of digital innovation – as LANA Process Mining has proven.