Rewrite in newsletter blog style with the same wordcount for CEOs and CTOs, 'As businesses continue to strive for operational efficiency, AI-based automation is becoming increasingly popular. However, to successfully implement this new technology, organizations must look beyond their usual processes and begin leveraging process mining. 

Process mining extracts valuable information and insights from event logs, providing accurate visualizations of processes that can be monitored in real-time. Essentially, it’s centered around discovering and refining models of how things function using event logs. While initial applications were limited to traditional business systems like ERP, the use of process mining has now expanded into various technical, human, and business processes.

The improvements in AI and machine learning algorithms have driven innovation in recent years, supporting the analysis and understanding of complex processes. Furthermore, new tools like digital twins have emerged, facilitating the visualization and assessment of intricate processes. These advancements have expanded the horizons of process mining and opened up numerous possibilities for the optimization of efficiency across different processes. One such technology born out of this growth is hyperautomation. 

What’s Hyperautomation?

Hyperautomation combines traditional automation with innovative AI-related technologies like Machine Learning and Deep Learning and information systems automation tools like as BPM, iBPM, iPaaS, Low code/No code, and RPA. This comprehensive approach is of immense benefit and promotes empowered teams, enhanced decision-making, and improved compliance and risk control, particularly when applied to various domains such as customer relationship management, supply chain, and finance.

A business-driven and disciplined approach, hyperautomation is gaining attention as it rapidly identifies and automates as many processes as possible. The foundation of hyperautomation is RPA, which can be fully utilized by integrating it with complementary technologies such as process mining, AI, and analytics. This holistic approach to automation has significant benefits, including cost reduction, improved customer experiences, and streamlined processes.

What are the benefits of Hyperautomation for Enterprises?

Hyperautomation not only contributes to cost reduction, but also helps in improving customer experiences by refining customer journeys and shortening processing times. By optimizing the entire delivery chain, hyperautomation promotes cooperation among different stakeholders, such as suppliers, logistics providers, and carriers. This collaborative approach leads to enhanced efficiency in Order-to-Cash and Procure-to-Pay processes, which can revolutionize the finance sector. Hyperautomation has the potential to transform industries by automating processes across the entire value chain, resulting in increased productivity and competitiveness.

How Does Process Mining Drive Hyperautomation?

To fully leverage the benefits of hyperautomation, however, identifying which business processes to automate can be challenging due to cognitive biases, inaccurate assumptions, and insufficient knowledge of operations. This is where process mining comes into play, providing the necessary precise understanding of process performance. 

Process mining allows organizations to accurately analyze their current processes, identify bottlenecks, and pinpoint areas that can be automated to achieve maximum efficiency. By combining hyperautomation with process mining, businesses can make informed decisions on which processes to automate, leading to successful implementation and optimized outcomes. 

This integration of complementary technologies ensures that hyperautomation is implemented strategically and with a deep understanding of existing processes, enabling organizations to unlock its full potential in driving digital transformation.

Process Mining plays a key role in implementing hyperautomation – allowing us to identify current bottlenecks as well as predict future process changes – ultimately leading to more autonomous systems capable of higher speed and complexity in decision-making. 

Process mining combines data science and process analytics to discover, validate, and improve workflows by mining log data from information systems like ERP or CRM tools. It employs a data-driven approach to optimize processes, uncover bottlenecks, and identify automation opportunities. By examining different perspectives such as control-flow, organizational, case, and time, process mining provides comprehensive insights for decision-making. The IEEE published the Process Mining Manifesto advocating its adoption, while factors like digital transformation and hyperautomation will also drive market growth.

Three main types of process mining, credited to computer scientist Wil van der Aalst, include discovery, conformance, and enhancement. Discovery involves creating process models solely based on event log data, while conformance checks confirm if the intended process model matches the real-world execution. Enhancement, also known as extension or performance mining, utilizes additional information to improve existing process models, optimizing processes and addressing bottlenecks. Overall, process mining plays a critical role in achieving operational excellence and successful digital transformation efforts.

Process mining vs. data mining

While data mining employs various algorithms and methods to explore datasets, process mining analyzes event logs and process-related data to uncover processes. Gaining a thorough understanding of processes involves recognizing trends, patterns, and discrepancies in processes, visualizing actual process flows in detail, identifying automation opportunities, and discovering new ways to boost process efficiency.

How does process mining function?

Process mining tools extract actual process models from raw event logs. By obtaining event logs from each case and combining them, these tools reveal how processes perform in reality. 

The workings of process mining can be understood through the following steps:
Process mining tools integrate with several business systems of record (such as ERP and CRM systems) to gather event logs from the company’s systems.
The tools extract the sequence of activities for each case from the event logs.
At this stage, differences between cases become evident. These disparities arise due to manual adjustments or errors in the process.
After determining the activity sequence for each case, process mining tools begin to “merge” these sequences.
As variations occur, the real process will be more complex than the planned one. This outcome also allows the company to comprehend where its process deviated.

The versatility of process mining in addressing different stages of RPA implementation has proven beneficial. By using it, organizations can identify potential areas for automation within their business processes and prioritize them based on ROI potential. Moreover, process mining plays a critical role in hyperautomation, as it involves integrating various tools and technologies to enhance an organization’s ability to automate work. Data-driven analytics generated by combining analyzed data with AI/ML can help organizations discover new opportunities for optimization and automation.

Effective change management is essential for successful process automation, as well as obtaining leadership buy-in and aligning team incentives. AI is critical to hyperautomation, as it empowers bots to undertake tasks with greater intelligence. Incorporating cognitive technologies like ML, NLP, OCR, and AI into RPA through process mining can significantly enhance process efficiency and accuracy.

Ultimately, process mining can help organizations extract more value from hyperautomation by providing the insights needed to optimize processes, streamline operations, and achieve greater efficiency and effectiveness. As the technology evolves, it is likely to be integrated into other critical use cases, such as compliance, auditing, sustainability, and business architecture, and enable models to access APIs and eliminate repetitive workflows.'



