While generative AI grabs headlines, most organizations are yet to achieve continuous process improvement from predictive and prescriptive analytics. Why? It’s largely about data, people, and a methodical approach to deploy AI to connect data and people. The good news is that if your organization has built a process mining capability, you are well placed to climb the ladder to achieve AI-driven process optimization. But to get there, you need a disciplined step-by-step approach along two tracks: a tactical management track and an operational management track. First, it’s about predicting what will happen if you leave your process as-is, and what will happen if you implement a change in your process. At a tactical level, a predictive capability allows you to prioritize improvement opportunities. At an operational level, it allows you to predict issues, such as deadline violations. The challenges here are how to manage the inherent uncertainty of data-driven AI systems, and how to change your people and culture to manage processes proactively, rather than reactively. One thing is to deploy predictive dashboards, another entirely different thing is to get people to use them effectively to improve the processes. Next, it’s about becoming preemptive: continuously optimizing your processes by leveraging streams of data-driven recommendations to trigger changes and actions. At the tactical level, this prescriptive capability allows you to implement the right changes to maximize competing KPIs. At the operational level, it means triggering interventions in your processes to “wow” customers and to meet SLAs in a cost-effective manner. The challenge here is how to help process owners, workers, and other stakeholders to understand the causes of performance issues and how the recommendations generated by the AI-driven optimization system will tackle those causes? And finally, as an icing on the cake, generative AI allows you to produce improvement scenarios to adapt to external changes. Importantly, the transformative potential of generative AI in the context of process improvement does not come from its ability to provide question-and-answer interfaces to query data. It comes from its ability to support continuous process adaptation by generating and validating hypotheses based on a holistic view of your organization. In this talk, we will discuss how organizations are driving sustainable business value by strategically layering predictive, prescriptive, and generative AI onto a process mining foundation, one brick at a time. Industry keynote talk by Marlon Dumas at the 5th International Conference on Process Mining (ICPM'2023), Rome, Italy, 25 October 2023

1. ICPM 2023 – Rome – October 25, 2023

6. https://github.com/AutomatedProcessImprovement/Simod

9. Organizational Pitfalls
Lack of buy-in from operations
Lack of trust in predictions
Prescriptions not linked with actions
Lack of processes to validate, monitor, and maintain
prescriptive models
Technical Pitfalls
Insufficient data availability & quality
Lack of uncertainty modeling
Drifts and out-of-distribution predictions
Correlation ≠ Causation

10. No amount of organizational
and technical readiness will
save you from the sin of using
simulation models without pilot
testing or predictive models
without A/B testing
0

11. Results achieved: Even though the prediction of
the most likely customers to report incorrectly
was reasonably accurate, the intervention did
not have a preventive effect. No root cause was
identified to explain why the intervention did
not have the desired effect.

13. +/- -
- +
Kairos: A Tool for Prescriptive Monitoring of Business Processes (demo)
https://kairos.cloud.ut.ee/

14. - Automate tasks X and Y
- Add +5 resources to task A
- Remove -2 resources from task B
…

16. Everywhere!
GenAI brings context recognition: What types of processes, activities, KPIs are we talking about?
LLMs enable conversational process optimization across all layers of the pyramid
Descriptive Process Mining
•Where are the sources of waiting time?
•Where are the rework loops?
•Where are we over-processing?
•What are the sources of variance?
•Which cases require the most touches?
•Where are we violating our KPIs?
•Are we abiding to our business rules and
policies?
•…
Predictive Process Optimization
•By how much would we reduce
order-to-delivery times is we:
•Shorten inter-batch cycles?
•Move resources to packaging?
•Automate verification steps?
•By how much would we reduce costs
if we:
•Consolidate touchpoints?
•Reorder verification steps to
reduce over-processing?
•Reduce rework rates?
•…
Assisted Process Optimization
•How can we slash KPI violation rates?
•What are the practices of the teams
with highest performance?
•What should we change to reduce
the number of touches?
•Which checks should we add to
reduce compliance violations?
•How can we cut the cycle time at
constant cost?
•How to allocate resources to optimize
time at constant capacity?
•…

17. Faster or Cheaper Horses
• Structured data prep (ETL scripting)
• Data querying (if your tool requires SQL coding)
• Data synthesis / enhancement (simulation?)
Better Than (Hallucinating) Horses
• Preprocess unstructured data
• Explain (or mis-explain) findings, patterns
• Figure out which options/questions to consider

19. Organizational Pitfalls
Lack of buy-in from operations
Lack of trust in suggestions / prescriptions
Prescription flooding / over-prescription
Prescriptions not linked with actions
Lack of processes to validate, monitor, and maintain
prescriptive models
Technical Pitfalls
Insufficient data availability & quality
Neglecting inter-process dependencies
Lack of uncertainty modeling
Drifts and out-of-distribution predictions
Unreliable prescriptions
Lack of feedback loop

20. No amount of organizational and
technical readiness will save you
from the sin of using AI-driven
improvement recommendations
without validation and pilot testing
or prescriptive models without A/B
testing
0

24. No amount of organizational and
technical readiness will save you from
the sin of deploying proactive or
adaptive process optimization without
building a capability to derive sustained
value from process mining, predictive,
and prescriptive optimization
4

26. • Lay the foundations, start climbing, keep climbing, don't hold off
Getting data for process mining is often a challenge. But there are both short-
term benefits (bottom of the pyramid) and long-term ones (top)
• Don't skip the layers
The lower layers of the pyramid provide a foundation to draw business value
from the upper layers.
• Align strategically and build governance incrementally
Apply these capabilities first and foremost to processes that matter.
Adopt these capabilities incrementally, one process at a time.
Build success stories internally, ensure each layer of the pyramid yields value.

27. Want to know more?