In recent years, the business process optimization landscape has seen significant advancements, especially with the rise of technologies such as Robotic Process Automation (RPA), process mining, predictive process analytics, and digital process twins. These technologies, when integrated, drive the evolution of a new approach called AI-driven Process Optimization, which leverages data analytics and AI to achieve continuous process improvement.
What is AI-driven Process Optimization?
- RPA and APA, as their name indicates, focus on automation. In contrast, AI-driven Process Optimization is about identifying a wide range of process improvement options, covering the whole ESSA paradigm (Eliminate, Simplify, Standardize, Automate).
- RPA and APA focus on automating individual tasks in isolation, such as generating a report, responding to a recurrent type of inquiry, etc. In contrast, AI-driven process optimization is about bringing improvements to Key Performance Indicators (KPIs) across entire end-to-end processes, considering dependencies between dozens or event hundreds of tasks, decisions, and data objects.
The AI-driven Process Optimization Pyramid
To understand AI-driven Process Optimization, it can be visualized as a pyramid of capabilities:
- Descriptive Process Mining:
At the base of the pyramid, this involves techniques to analyze business processes using event logs from systems like CRM, ERP, and TMS. Key capabilities include:
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Automated process discovery: Discovering process models from data.
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Conformance checking: Detecting deviations from desired pathways.
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Performance mining: Linking performance measures to process elements.
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Variant analysis: Identifying deviations in process performance across different cases.
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- Predictive Process Mining:
Moving up the pyramid, this layer uses historical data to forecast future process performance. Capabilities include:
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Predictive process monitoring: Predicting future states of processes.
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Digital Process Twins (DPTs): Simulating the impact of process changes before implementation.
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- Prescriptive Process Improvement:
At this layer, the focus is on turning predictions into actions to optimize processes. Capabilities include:
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Prescriptive process monitoring: Recommending real-time actions to improve process performance.
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Automated process improvement: Suggesting process changes to balance competing KPIs.
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- AI-driven Process Optimization:
The pinnacle of the pyramid involves advanced interactions between AI and human actors, enabling:
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Conversational process optimization: Detecting and explaining performance issues to human actors and evaluating counteractions, by leveraging Large Language Models (LLMs).
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Adaptive self-driving processes: Automated systems determining the next steps in a process and escalating to human operators when necessary.
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The Augmented BPM Pyramid
Where to Start with AI-driven Process Optimization
Organizations can harness AI-driven Process Optimization by adopting a strategic and incremental approach:
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Lay the Foundations: Begin with process mining to gather and analyze data, setting the stage for more advanced capabilities.
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Climb the Layers Sequentially: Mastering the lower layers of the pyramid is essential before advancing to predictive and prescriptive capabilities.
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Align Strategically and Build Governance: Apply AI-driven optimization technologies to processes that align with strategic priorities and establish governance structures incrementally to ensure sustainable value creation.
Real-World Application and Success Stories
Several organizations have successfully implemented AI-driven Process Optimization to enhance their operational excellence. For instance:
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Australian Bank: A major Australian bank utilized process mining and data-driven process simulation for a comprehensive re-platforming of its operations. By integrating process mining into their BPM strategy, they were able to adapt to new regulatory requirements and better compete with fintech startups. This initiative led to a 20% improvement in process efficiency and a significant reduction in compliance-related incidents
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Global Logistics Company: A leading logistics provider implemented predictive process analytics to optimize their supply chain operations. By forecasting potential delays and disruptions, they could proactively adjust their logistics plans, resulting in a 15% reduction in delivery times and a 10% increase in customer satisfaction
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Healthcare Provider: A prominent healthcare organization employed digital process twins to simulate the impact of process changes in their patient care workflows. This allowed them to identify bottlenecks and streamline operations, leading to a 25% improvement in patient throughput and a significant enhancement in the quality of care provided.
Conclusion
AI-driven Process Optimization represents the future of business process management, combining data analytics and AI to drive continuous improvement and operational excellence. By understanding and implementing the capabilities within the AI-driven Process Optimization pyramid, organizations can unlock significant value and stay ahead in a competitive landscape.
For business analysts and process managers, the journey towards AI-driven Process Optimization is not only feasible but essential. Starting with process mining and progressively integrating predictive and prescriptive capabilities will pave the way for a fully AI-driven Process Optimization approach, ultimately leading to superior process management and enhanced business outcomes.
By embracing AI-driven Process Optimization, organizations can move beyond isolated automation-driven process improvements to a holistic, data-driven approach that continuously adapts and optimizes business processes, ensuring long-term operational excellence and competitive advantage.
This blog post is adapted from an original article by Prof. Marlon Dumas, published at Modern Analyst here.