Industrial AI and Autonomous OperationsDay 1
Connectivity and Network InfrastructureDay 1
Edge AI and Real-Time IntelligenceDay 2
IoT Security and Embedded SystemsDay 2
Physical AIDay 2
TechEx Learning Hub EuropeMulti
AI LeadershipDay 1
Enterprise AIDay 1
AI BuildersDay 1
Data & AnalyticsDay 2
Future AIDay 2
AI DeveloperDay 2
Threat Detection, Incident Response & Security OperationsDay 1
Cybersecurity Leadership & Enterprise RiskDay 1
Identity, Zero Trust & Security ArchitectureDay 2
Cloud, AI & The Future of Cyber DefenceDay 2
Green Investment, Digital Innovation, and Physical InfrastructureDay 1
Data Centre Services, Ecosystems & Business ModelsDay 2
Chairpersons welcome and opening remarks
As organisations confront the limits of cloud-centric architectures, leading enterprises are redesigning systems to bring intelligence closer to where data is generated. This session explores how deploying AI inference at the edge unlocks ultra-low latency decision-making, reduces operational costs, and ensures continuity in bandwidth-constrained or disconnected environments. Expect practical lessons on infrastructure redesign, model optimisation, and measurable business impact.
AI is moving out of the browser and into the physical world. A new generation of systems, like autonomous vehicles, warehouse robots, machine vision on the production line, and smart factories, now perceives its surroundings, reasons about them, and acts in real time. This is Physical AI. And unlike a chatbot, its mistakes have consequences in the physical world. That changes everything about where and how the intelligence has to run.
This session makes the case that for Physical AI, the edge is not an optimization but a prerequisite. Latency, intermittent connectivity, bandwidth limits, and data sovereignty all push inference out to where the sensors and machines actually live. But the real challenge starts after the models work: with operations. How do you deploy, secure, update, and monitor hundreds or thousands of GPU-equipped edge systems as a single fleet rather than as individually managed devices?
Drawing on real production deployments, we’ll explore why cloud-only architectures fall short, what an edge platform actually does, and how container orchestration brings cloud-native operational disciplines to distributed Physical AI devices. Attendees will leave with a practical litmus test for whether their organization can not just build Physical AI, but operate it reliably in the real world.
Driven by the need for speed, resilience, and autonomy, organisations are rapidly shifting toward edge-native system design. This panel examines the shift toward edge-native architectures, where intelligence is embedded closer to data sources, enabling faster, more resilient, and autonomous operations. Panellists will explore how to redesign data flows, integrate AI seamlessly across devices and networks, and navigate the complexities of interoperability in increasingly fragmented ecosystems. The discussion will also address how to future-proof infrastructure investments as edge computing, AI, and connectivity converge to form the backbone of next-generation digital operations.
Edge computing is moving into a new phase as more compute, software, and intelligence shift closer to where data is created and acted on. At the same time, the market is expanding unevenly across different parts of the edge stack. Some technologies are becoming more strategic while others are maturing or changing role. This session will look at how these shifts are shaping the edge, where the strongest growth is emerging, and how architectures are developing as the technology stack becomes more distributed.
The impact of AI is being felt throughout the technology world, and beyond. One critical theme, implicit in the emergence of ‘AIoT’ and ‘Physical AI’, is the expanding requirement for distributed AI running on edge devices such as cameras, robots and industrial machinery.
In this session, Matt will explore the growing demand for edge AI for use cases such as predictive maintenance, anomaly detection and video analytics, and the significant implications for orchestration of AI across the cloud, device, and intervening edge locations. It will particularly focus on the increasing need for capabilities to manage the newly distributed AI inferencing, including optimising models for edge devices, mechanism for updating distributed models in the field, feedback loops, optimisation of connectivity, compliance and other key issues.
As edge deployments grow into the thousands of devices, orchestrating AI models across distributed fleets becomes critical. This session explores strategies for centralized deployment, automated updates, and continuous monitoring to maintain peak model performance. Attendees will learn how orchestration platforms ensure consistency, reliability, and agility in complex edge environments, enabling organizations to scale AI without introducing operational risk.
5G solved coverage. 6G will solve cognition. As the first commercial 6G deployments move from standardisation bodies into vendor roadmaps, the question is no longer how fast connectivity will be – it is where intelligence should live. This session brings together network architects, chip designers, and IoT platform leads to map the emerging stack: sub-terahertz radios, in-network computing, ambient AI inference, and the edge-cloud continuum that will underpin the next decade of connected devices. Expect hard numbers,and a frank debate about which of the promises will actually ship.
Deploying AI models is only half the battle; maintaining reliability and trust in the field is equally critical. This presentation will cover approaches to edge observability, including monitoring, debugging, and performance auditing for real-time AI systems. Attendees will gain insights into building trustworthy AI systems that are transparent, maintainable, and resilient, ensuring operational continuity and regulatory compliance.
As intelligent systems gain influence, ethics must remain at the forefront. This talk explores how enterprises can design AIoT solutions that minimize bias, preserve privacy, and maintain trust. Through practical frameworks and examples, the speaker will show how to build systems that are not only powerful, but responsible too.
