Overview

In today’s industrial environments, complexity is increasing at an unprecedented pace. With interconnected systems, IoT devices, and advanced digital monitoring, the sheer volume of data generated daily can easily overwhelm even the most experienced operators. This is where Multi-Agent Generative Systems (MAGS) come in. Leveraging the power of Generative AI, MAGS offers a transformative approach to tackling some of the most pressing industrial challenges. Unlike conventional systems, MAGS reduces the cognitive load on operators, functions with the precision of industrial control systems, and delivers continuous, data-driven improvements through machine intelligence. These capabilities make MAGS not only a valuable tool but a crucial tool for Generative AI in industrial settings.

Speaker

Gavin Green, Vice President, Strategic Solutions

Gavin is a visionary leader in the realm of industrial technology solutions, spearheading a team of innovators at XMPro. With over five years of experience at the forefront of this dynamic field, Gavin has successfully positioned XMPro as a trusted partner for clients seeking to optimize operations, enhance performance, and reduce costs through transformative digital strategies.

His core competencies span IoT, business process design, databases, IT strategy, and professional services, enabling him to architect holistic solutions that seamlessly integrate emerging technologies into existing industrial ecosystems.

Gavin has a BIT in Information Technology and Informatics from the University of Pretoria.

The material presented has not been peer-reviewed. Any opinions are the presenter’s own and do not necessarily represent those of IChemE or the Process Management & Control Special Interest Group in full. The information is given in good faith but without any liability on the part of IChemE.

Time

15:00 - 16:00 GMT

Software

The presentation will be delivered via Microsoft Teams. We recommend downloading the app from the Microsoft website, rather than using the web portal.