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Discover how generative AI can help you move from written policies to executable business logic. In this webinar, we’ll showcase an assistant—developed as part of IBM’s approach to Decision Automation—that helps teams extract knowledge from everyday policy documents and user instructions to accelerate the early stages of automation projects.
The assistant uses GenAI to generate a structured data model and a decision model composed of business rules, with the goal of capturing the core business logic and making it ready for execution. You’ll see live examples of how this approach enables faster alignment between business and IT, reduces interpretation gaps, and sets a strong foundation for scalable decision automation.
Whether you're exploring automation or scaling existing efforts, join us to see how GenAI can simplify the path from policy to implementation.
Speaker:
Pierre Feillet
Pierre is Chief Architect at IBM France Lab for Decision Automation. With over 20 years of experience at IBM and ILOG, he has specialized in automating business applications using business rules—often combined with machine learning, big data, and more recently, generative AI. He has contributed to the development of IBM Operational Decision Manager runtime, the integration of predictive models into Automation Decision Services, and the embedding of GenAI capabilities in Watson Orchestrate’s Skill Studio. Pierre’s recent focus has been on narrowing the gap between natural language and automation, leveraging LLMs in interactive assistants to support decision modeling and execution, and advancing the analytics of decisions to drive continuous improvement. He has also served as Technical Director for the Artificial Intelligence for Digital Automation project with Université Paris-Saclay, and is an active member of the French Association for Artificial Intelligence (AfIA).