About this Presentation
This session explores how AI agents dramatically accelerate the TOC Thinking Processes, demonstrating their use in refining verbalization, root cause analysis, and solution guidance. It also highlights the critical need for expertise to efficiently manage AI task consumption ("tokenization") and avoid unnecessary costs, marking the shift to AI-driven Service-as-Software.
What Will You Learn
To help you get the most value from this session, we’ve highlighted a few key points. These takeaways capture the main ideas and practical insights from the presentation, making it easier for you to review, reflect, and apply what you’ve learned.
Learn why widespread AI adoption has not translated into consistent ROI and how tokenization has become the new system constraint limiting AI value. 2.Understand how applying TOC logic reframes AI from an answer-generator into a structured partner for augmenting human thinking and decision-making.
See how disciplined prompting, context formulation, and TOC Thinking Processes dramatically reduce AI cost while increasing rigor and usefulness. 4.Gain insight into how AI agents can be designed as focused, expert “digital workers” when orchestrated by humans using clear TOC rules.
Instructor(s)
Gilsiley Darú
Gilsiley Henrique Darú provides supply chain consulting and optimization for major companies such as HAVAN (Retail – Supply Chain), HVLE (Railway Optimization in Germany), and Malwee (Fashion – Production Planning). He leads the AI and Supply Chain Innovation Lab at Neogrid Software, a leader in supply chain integration.
With over 20 years of experience at firms including Datasul, WEG, and Malwee, he applies artificial intelligence and innovative solutions to transform corporate environments. At Neogrid, Gilsiley leads a team creating cutting-edge supply chain management solutions, leveraging his expertise in data analysis and AI to turn data into valuable insights and enhance client operational efficiency.
A Ph.D. candidate in Computational Mathematics (UFPR) and holding master’s degrees in Data Science (USP) and Numerical Methods (UFPR), Gilsiley also holds degrees in Mechanical Engineering and Data Processing (UDESC), along with postgraduate qualifications in Data Science (SENAI) and Software Engineering (PUC-PR).
An enthusiast of the Theory of Constraints and optimization in industrial planning, he helps companies find focus and improve flow using TOC Thinking Processes alongside optimization tools such as Operations Research, Discrete and Continuous Simulation, and Agent-Based Modeling, with strong expertise in logistics and business planning.
Academically, he shares his knowledge as a postgraduate professor in AI and Deep Learning. Gilsiley values collaboration and the continuous development of advanced analytical skills in all his initiatives.