In the rapidly evolving field of business process management, there is a growing need for analytical tools that can transform complex data into actionable insights. This research introduces a novel approach by integrating Large Language Models (LLMs), such as ChatGPT, into process mining tools, making process analytics more accessible to a wider audience. The study aims to investigate how ChatGPT enhances analytical capabilities, improves user experience, increases accessibility, and optimizes the architectural frameworks of process mining tools. The key innovation of this research lies in developing a tailored prompt engineering strategy for each process mining submodule, ensuring that the AI-generated outputs are accurate and relevant to the context. The integration architecture follows an Extract, Transform, Load (ETL) process, which includes various process mining engine modules and utilizes zero-shot and optimized prompt engineering techniques. ChatGPT is connected via APIs and receives structured outputs from the process mining modules, enabling conversational interactions. To validate the effectiveness of this approach, the researchers used data from 17 companies that employ BehfaLab's Process Mining Tool. The results showed significant improvements in user experience, with an expert panel rating 72% of the results as "Good". This research contributes to the advancement of business process analysis methodologies by combining process mining with artificial intelligence. Future research directions include further optimization of prompt engineering, exploration of integration with other AI technologies, and assessment of scalability across various business environments. This study paves the way for continuous innovation at the intersection of process mining and artificial intelligence, promising to revolutionize the way businesses analyze and optimize their processes.

Process mining has evolved into a mainstream approach to discover and improve business processes, and its market is projected to grow by 40-50% by passing $1 billion in 2022. It is being applied to numerous sectors and departments, ranging from healthcare to logistics. Case studies have shown that retailers, telco, and finance companies were some of the top beneficiaries of process mining. In this article, we explore experts' opinions, and we leverage our own research to predict process mining trends in 2022, and how businesses can benefit from these trends. Wil van der Aalst, the founder of process mining, states that he observes a shift towards more integrated tools and capabilities in the process mining market.

Covid-19 enforced countries to adopt online or hybrid learning in order to catch up to expected learning targets. Yet, many countries remain inefficient at moving to online or hybrid education. Also, though some countries manage to boost students progress (like Italy increased their progress with online tutoring by 4.7 % compared to traditional schooling), some others fail to generate the same outcome from the online learning. However, recently, education industry leaders have started identifying use cases of process mining to improve online learning platforms, teaching methodologies and learning habits of students. In this article, we explain what is educational process mining, what are the use cases, benefits and challenges of applying process mining to educational domains.

Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery. Leveraging AI and machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs and plan future financial actions to progress on their AI transformation. A recent MIT survey revealed that 60% of manufacturers are using AI to improve product quality, achieve greater speed and visibility across supply chain, and optimize inventory management. Implementing AI in manufacturing facilities is getting popular among manufacturers. According to Capgemini's research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third.

AI transformation is the next phase of digital transformation. Businesses are willing to invest in AI technologies to stay ahead of competitors. AI Transformation is a process that may take 2-3 years, but organizations can start to see the returns within 6 to 12 months. Digital transformation is required before companies can start their AI transformation because digital data is necessary for AI training and digital processes are necessary to roll-out AI solutions in most cases. Feel free to read about what digital transformation is and our extensive digital transformation guide if you believe that your company has not yet progressed on its digital transformation journey.