Beat the rush and join us early for complimentary barista-made coffee and breakfast.

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AI that looks brilliant in a demo often struggles in the real world. In this keynote, I’ll share what a research engineering team learned translating promising AI prototypes into practical organisational capability, and why success depends less on model choice than on delivery discipline, platform thinking, governance, and trust.

• Why data, workflows, governance, and operating model matter more than most teams realise
• How to run fast, safe experimentation without undermining reliability or security
• What organisations need to get right before AI can deliver lasting operational value

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As AI workloads move into production, infrastructure stops being a backend concern and becomes a core business driver. This keynote breaks down how AI factories unify data, compute, networking and software into a single high-utilisation platform and why utilisation, not raw capacity, now determines cost, speed and scale.

We'll Cover:

• What an AI factory actually is, and how it differs from traditional AI infrastructure stacks
• How improving fabric utilisation and GPU efficiency increases token throughput while lowering unit cost
• Why predictable scaling turns infrastructure decisions into commercial and operational advantages

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As AI moves into real-world systems, responsibility must be built into the technology itself, not added later. Engineering teams are now expected to design AI that is fair, transparent, and accountable, while still delivering innovation at speed.

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This panel explores how organisations are embedding ethical considerations into AI engineering, from bias mitigation and model transparency to governance and accountability.

• Identifying and reducing bias in AI systems
• Improving transparency and explainability in models
• Embedding accountability and governance into AI development

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A case study on designing efficient data pipelines to handle large-scale data ingestion, processing, and storage for AI applications.

In this innovative session, attendees will be faced with a series of scenarios that they may face in their roles. Attendees will discuss the possible courses of action with their peers to consider the ramifications of each option before logging their own course of action.

Results will be tallied and analysed by our session facilitator and results will impact the way the group moves through the activity.

Will we collectively choose the right course of action?

As we deployed AI agents to automate accounting workflows in a highly regulated environment, we quickly learned that model-level safeguards weren’t enough. This session explores how we built a layered safety chain, spanning the user’s role, the agent, and the underlying services and data, to manage blast radius, permissions and autonomy in production. We’ll share how targeted beta rollouts and deliberate UI design helped define clear boundaries between essential human oversight and safe, scalable automation.

AI systems don’t fail like traditional software. They degrade, drift, and behave unpredictably, making them harder to monitor and harder to trust. Traditional observability tools weren’t designed for probabilistic systems, leaving teams without a clear way to measure reliability in production.

This panel explores how engineering teams are building observability into AI systems, from tracking model behaviour and drift to monitoring end-to-end workflows across multi-model and agent-based environments.

Select a topic of discussion and engage in an interactive roundtable discussion with a group of your like-minded peers.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights - and a travel voucher - as the top scorer takes the crown.

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As AI agents become autonomous, traditional security models break down. This session explores how Zero Trust principles and service mesh architectures can secure agent-to-agent and agent-to-service interactions, enforce policy in real time, and control blast radius without slowing innovation.

• Treat AI agents as dynamic identities
• Enforce policy at runtime
• Scale safely without slowing down

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A lively session on how organisations are navigating the growing pressure on AI infrastructure. From GPU shortages and rising cloud costs to sustainability targets and performance demands, engineering leaders are being forced to make trade-offs with no clear right answer.

This interactive think tank puts the question directly to the audience. Participants vote live on a series of real-world scenarios, explore the results together, and vote again as perspectives shift through the discussion.

• What is your biggest constraint in scaling AI today?
• Where should most AI workloads run long-term?
• If AI infrastructure costs doubled, what would you prioritise?
• Where do you see the biggest hidden cost in AI infrastructure?
• What will matter most in AI infrastructure decisions over the next 3 years?

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Unwind with your peers for a couple of drinks on us!