According to a 2021 CIO report, 77.3% of CIOs placed digital transformation as their top priority. However, with the immediate need that the pandemic placed on businesses to offer remote working and more online services and products, this should come as no surprise. Instead, it has simply brought the agenda for digital transformation forward.

Only a few years ago, pen and paper were enough to map processes and trying to find areas for improvement. Once a report was done, the situation might have changed completely and the report was left obsolete.

Instead of using pen and paper, process mining technology uses Artificial Intelligence and Machine Learning to automatically extract, visualize, and make sense of operational data from organizations' IT systems. Process mining software like QPR ProcessAnalyzer help you understand this dynamic visualization (or "digital twin") of your organization and make interesting findings: where costs can be saved, processes can be simplified, automation needs to be improved most, etc.

Not only can you find root causes for your problems, but also predict problems and get automatic alerts when problems are about to arise - so you can proactively take action.

This blog opens up how Artificial Intelligence and Machine Learning are used in process mining: cluster cases, predict problems, and more. 

Intelligent Process Mining helps compliance officers, internal auditors, business leaders, process analysts and business controllers to detect, understand, predict and prevent compliance violations. Typical use cases include:

• Internal Audit - Provide assurance that internal control processes are operating effectively
• Anomaly Detection - Identify suspicious cases that differ significantly from the majority.
• Trade Compliance - Ensure that operations comply with international export, trade, and financial laws.
• Process Owners - Enforce compliance with agreed processes

Machine learning helps organizations to better utilize the process mining results for improving processes. Traditional process mining is capable of delivering great visibility to as-is processes, pointing out process problems, bottlenecks, and RPA opportunities. In addition to these core benefits, machine learning algorithms provide a great deal of new information using the same process mining source data.

Process mining has become a key enabler of data-driven business operations, as it visualizes operations and pinpoints process inefficiencies and otherwise hidden information. This new release of QPR ProcessAnalyzer is aimed at those who want to find new ways to boost their operational efficiency: identify and eliminate operational waste, deliver accurate KPI measures, and take action based on business notifications.

With these, you gain the insights to track, analyze, improve, and automate operations. The latest product features even notify you about the potential business opportunities that can be utilized with swift action.

Is it your responsibility to improve business performance, increase revenue, and cut costs? You probably know there is room for improvement in the efficiency of your operations and a huge amount of data available - but you don’t know where to begin.

Maybe you feel stuck because you don’t know exactly what is causing issues in your business in the first place: your automation projects aren’t turning out to be as profitable as you were promised, your digital transformation journey is slow and cumbersome, and your customers are switching to competitors.

These are very common problems that can be solved by focusing on business processes and utilizing the available process data.

Let me explain why that is and how you can do it.

Have you heard about process mining, but are still unsure about how it works and why you should use it in your organization? In this blog post, you’ll find answers to the most common questions that we get from companies considering if, and how, they could use process mining to gain insights into their operations and business processes.

Although digital transformation is mentioned in just about every corporate playbook, many organizations are still failing to achieve the desirable outcomes with their digital transformation initiatives.

The fact is that merely offering digital services to customers or internal stakeholders is not enough in itself anymore - unless the digital processes are working seamlessly. According to Gartner, “digitalization is a top priority, but efforts are stalling”, and in 2020 “only 40% of organizations have brought digital initiatives to scale”.