In today’s rapidly evolving landscape of technology and digital transformation, process mining is emerging as a critical tool for organizations striving to optimize their operations. Although its roots trace back over 20 years to academic institutions in Northern and Central Europe, including the Eindhoven University of Technology and the Technical University of Aachen, as well as the development of industrial software such as ARIS and Celonis, the concept has only recently garnered significant attention from the broader market. The COVID-19 pandemic catalyzed this change, compelling businesses to confront the urgent need for transparency in their operations, resulting in a notable surge in interest in data-driven insights.
In 2023, this need for transparency drove process mining to receive a significant validation when Gartner recognized it as a new commodity in the business process management (BPM) sphere. It published its first Magic Quadrant for Process Mining Platforms on the best tools on the market, leading to an exciting race for innovative solutions. In 2024, we have witnessed an exciting race to create the ultimate process mining solution, especially those based on artificial intelligence (AI).
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The main challenges of implementing a process mining and AI project
However, embracing this technological shift is not without its challenges. Organizations must confront cultural and operational hurdles, requiring strong leadership and a commitment to fostering a climate of change. This frees the project team to focus on the rest of the challenges they face to achieve the ultimate goal: A complete digital twin of an organization’s process map, which will form the base of the pyramid for augmented process management, capable of harnessing the full power of new algorithms based on AI.
The development from process mining to augmented process management
1. Unrealistic expectations
The market explosion since 2023 has significantly contributed to the emergence of process mining as a key driver of efficient digital transformation. However, this surge in visibility has also had drawbacks, impacting the maturity of the discipline within organizations. The allure of compelling commercial messages often leads to inflated and almost automatic expectations immediately following the acquisition of a product. In reality, no vendor can fully understand the unique technological and human landscape of an organization well enough to provide a preconfigured solution that encompasses both process and technology maps.
2. Misalignment between marketing and reality
While the results showcased in commercial success stories are not inherently unrealistic, modern marketing often fosters unrealistic expectations for rapid outcomes in process mining initiatives. Launching such projects requires significant organizational effort that goes beyond data connection and analysis. It involves effectively managing skilled personnel and securing budget resources.
There are four primary scenarios for applying process mining:
1. Proof of concept (PoC): This initial phase involves using a valid dataset to validate the capabilities of a mining tool through discovery analysis and volumetrics, completing the task in a few hours.
2. Proof of value (PoV): Distinct from PoC, this phase relies on operational knowledge, specifically the AS-IS model and current data map, to generate precise and actionable analyses, typically requiring about eight weeks of work.
3. Digital twin of the process: This ambitious goal aims to create a comprehensive model of all relevant tasks and their interactions within the business process. Although challenging due to various systems and legacy technologies, achieving a digital twin greatly improves process efficiency and supports advanced analytics.
4. Global adoption of the discipline: Consistent creation of digital twins enables the establishment of a center of excellence for process mining, focused on best practices, training and monitoring technology’s impact on key performance indicators (KPIs).
To realize true return on investment (ROI), organizations must mature in their process mining efforts and activate transformational mechanisms to implement recommended improvements. If we fail to consider these essential factors and allow ourselves to be swayed by aggressive marketing rhetoric, we risk becoming disillusioned with the discipline and hinder the opportunity to fully leverage process mining’s potential. Therefore, approaching this endeavor with realistic expectations and a dedication to continuous improvement is essential.
3. Data quality and availability issues
Process mining relies on two key pillars: Business knowledge from key personnel and execution data stored in information systems. While modern systems typically manage execution traces effectively, many core systems are highly customized or outdated, complicating data connectivity and extraction. To overcome these challenges, it’s essential to involve technical experts who can ensure data availability, quality and integration, leading to the development of a robust data model. The success of process mining initiatives and the potential to utilize digital twins for process intelligence projects are directly tied to the quality of execution data. Therefore, prioritizing data quality and accessibility is crucial for effective analysis and improved decision-making within the organization.
Influence of data quality on process digitization
4. Skill and cultural barriers
The organizational effort required for a process mining initiative is inherently transversal, necessitating collaboration across various departments and expertise areas. Traditionally, these projects have involved professionals with a business focus, leaving process management departments lacking the technical skills needed for data extraction, transformation and loading (ETL). It’s crucial to engage technical departments to ensure data accessibility and incorporate hybrid roles within process teams to enhance analytical capabilities.
Resistance to change is a common challenge, as some individuals may be reluctant to embrace new responsibilities, while others may attempt to take the lead without alignment with organizational goals. Effective leadership and oversight from the initiative’s sponsor are vital, as this person should have the authority to engage all relevant departments. Additionally, the process owner plays a key role, being deeply invested in process improvement and facilitating critical validations. They will champion the project’s findings and ensure the successful implementation of proposed enhancements.
Data is key to process mining success
The success of process mining initiatives, like any analytical discipline, relies heavily on the accuracy, quality and availability of the information being analyzed. This includes data on the behavior of processes within information systems, as well as insights into their structure and their relationship with the supported business functions. To enhance the likelihood of success, organizations should focus on three key points:
1. Set realistic expectations: Foster motivation and alignment throughout the initiative by establishing achievable goals.
2. Establish governance: Implement a clear governance strategy to facilitate efficient collaboration among the various departments involved.
3. Ensure continuous involvement: Maintain active participation from key project roles throughout the initiative’s development. Continuous monitoring by the process owner is essential, along with oversight from the initiative’s sponsor.
While process mining is a relatively young discipline, most major technology consulting firms now have specialized teams equipped to execute technical plans and support clients with various tools. However, each organization will face the challenge of creating digital twins of their processes gradually and thoughtfully, based on their maturity in process science and developing an effective corporate methodology.
Although this approach may not be as glamorous as some popular narratives, rushing into implementation without a comprehensive understanding often leads to disappointment and inefficacy. Such experiences can hinder organizations from fully leveraging the valuable insights that process mining can provide.