Marcello La Rosa, CEO, Apromore, offers a C-Suite perspective on driving process optimization.
In today’s fast-paced business environment, executives face pressure to boost operational excellence and stay competitive. Generative AI (GenAI) stands out as a disruptive technology with the potential to transform the future of work while process mining is rapidly gaining traction for its strategic impact on digital transformation.
Process mining extracts actionable insights from event logs generated by enterprise systems and applications. providing a data-driven view on business processes. It highlights friction points in customer experience, deviations from intended workflows and compliance issues.
For C-suite executives, understanding these nuances is crucial for making informed strategic decisions that drive efficiency, profitability and compliance.
Process mining is rapidly growing with the market expected to expand over 40% in the next decade, reaching $31.52 billion by 2032, according to Global Market Insights.
Gartner estimates that by 2026, 25% of global enterprises will have embraced process mining platforms as a first step to creating a digital twin for business operations, paving the way for autonomous business operations.
As a subset of artificial intelligence focused on generating new data instances, GenAI brings several transformative benefits to process mining:
1. Process Discovery
GenAI helps managers to navigate the complexity of real-life processes discovered by process mining software. In processes like loan origination at a bank or order-to-cash at a manufacturer, which involve hundreds of steps and thousands of pathways, GenAI reveals hidden patterns and anomalies. By providing a conversational interface, it allows users to quickly get answers to questions like “Where are the highest sources of waiting time in my process?”, “Where are the top 3 rework loops by impact on cycle time?”, or “Why do we violate our time-to-yes obligation?”. The conversational interface helps analysts to uncover insights that would otherwise take them weeks of effort to identify.
If the underlying LLM is trained on the organization’s data and documents, GenAI contextualizes the discovered friction points and compliance deviations within the organization’s setting. Users can understand the true impact of these anomalies on the company’s KPIs and KRIs and link them to strategic objectives.
By diving into root causes and relating them to other aspects of the organization, GenAI goes beyond standard root-cause analysis techniques that only look for information within the event log used for process mining.
2. Process Simulation
Digital process twins allow managers to test process changes in a risk-free environment by simulating change scenarios. Process mining techniques provide the groundwork for such scenario testing by automatically generating accurate digital process twins from event data.
Still, the use of digital process twins requires expert users who understand the complex statistical parameters required to simulate the organization’s processes. GenAI reduces the time needed for simulation parameter tuning and lowers the entry barrier for process simulation, making it possible for business stakeholders to get answers to their what-if analysis questions without requiring technical expertise.
Users can ask natural language what-if analysis questions like “How much would we shifting FTEs to packaging reduce order delivery times? ” or “What would be the impact on our time-to-yes Service Level Agreement (SLA) if we received 20% more loan applications a day?”. The GenAI builds and tests the simulation scenarios required to answer these questions and presents the results in an easy-to-consume form such as a dashboard.
3. Process Optimization
The first two applications make process mining more accessible and accelerate time-to-value, while the third focuses on prescriptive optimization. Here, the objective is to prescribe the most effective changes to achieve performance and compliance targets such as “Which changes can I apply to meet my loan applications time-to-yes SLA?”, or “What changes can I implement to deliver a 20% uplift in cycle time without compromising controls?”. To recommend changes that address these questions, the GenAI generates dozens of possible configurations and tests each of them thorough simulation, considering the operational constraints and business goals of the organization.
GenAI accelerates the identification of optimal configurations, saving time and resources, and reducing the time-to-market for new initiatives. However, to avoid hallucination issues GenAI techniques suffer from, GenAI should be used only for generating new simulation scenarios to be tested. The actual testing should be performed by simulation engines to ensure statistical reliability.
From Tactical Analysis to Operational Support
The previously discussed applications focus on tactical analysis for mid-to-long term improvement. GenAI can also be used to recommend on-the-fly interventions and understand their impact on ongoing processes. This enables answering questions like “Today, which loan applications are at risk of breaching our time-to-yes SLA and what can I do to avoid it?” These on-the-fly recommendations from GenAI should be tested, to rule out hallucination. This can be done using causal machine learning techniques, which are effective at determining which treatments have the highest causal effect on a desired process outcome (like lifting cycle time by 20%) but do require a set of possible treatments to be provided first. That’s precisely where GenAI helps.
Conclusion
The integration of GenAI into process mining offers significant opportunities to accelerate initiatives related to operational excellence, digital transformation and compliance monitoring, especially at scale. GenAI can dramatically shorten time-to-value, particularly for non-technical users. The real game changer is GenAI’s ability to explain findings and patterns that are not easily seen and to generate interventions, both at tactical and operational levels, that are not easily thought of. However, GenAI is still in its infancy and requires guardrails to avoid pitfalls.
As GenAI continues to evolve, it will likely provide further applications in process mining. Staying on top of these developments will enable organizations to unlock new opportunities, remain agile, gain competitive advantage and fuel growth.
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