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

• How nbn uses an internal technology exploration and development team to scale innovation
• How GenAI was rolled-out company-wide and is now being expanded to support agents
• How a low-latency data processing pipeline is making our network more reliable

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. 

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• 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

This panel brings together enterprise leaders governing AI at scale to share what’s working when the pressure is to ship fast. We’ll unpack the guardrails that enable speed, the controls that actually reduce risk, and the governance overhead that teams are cutting because it slows delivery without improving outcomes.

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We'll Cover:

• Governance that keeps pace with deployment speed
• How leaders balance competitive pressure with risk and accountability
• Getting tech, risk, legal and business aligned on “responsible” in practice
• Where governance adds value vs where it becomes overhead

Automation across finance, operations, and product teams often promises speed but delivers fragile workflows, duplicated logic, and unexpected failures when core systems change.Learn how to tackle cross-functional automation at scale, connecting multiple platforms without creating tight coupling, hidden dependencies, or operational risk. This session will unpack the practical decisions that made the difference, including where automation should stop, how ownership boundaries were defined, and the guardrails that allowed teams to move independently while protecting critical systems.

• Why cross-platform automation breaks when ownership and failure paths are unclear
• Practical design patterns for automating across teams without central bottlenecks
• How the right guardrails improve reliability, speed, and long-term maintainability

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?

Detecting market abuse at scale is no longer just a compliance challenge; it’s a data problem. In this session, Nasdaq shares how they embedded advanced AI into its surveillance platform to improve detection accuracy, reduce false positives, and scale consistently across complex global regulatory environments.

• Embedding AI into surveillance: How advanced analytics and machine learning are transforming detection accuracy while reducing noise and false positives
• Scaling across borders: Navigating diverse regulatory requirements while deploying a consistent, enterprise-wide surveillance capability
• From insight to action: Operationalising AI-driven alerts to enable faster investigations, stronger compliance outcomes, and real business impact

Cross-channel automation often breaks down when data arrives late, signals are inconsistent, or systems respond out of sequence. Learn how to use real-time data triggers to synchronise actions across digital channels and internal systems, creating faster, more coordinated customer and operational workflows without increasing complexity or risk. We will walk through the decisions that mattered most, including which signals were trusted, how timing and ordering were handled, and where automation needed human or system guardrails to prevent unintended outcomes.

• Why batch-based automation fails in real-time customer and operational journeys
• How to design event-driven triggers that stay reliable across channels
• Where real-time automation adds value, and where it creates hidden risk

This panel brings together leaders responsible for implementing AI programs to discuss how organisations assess whether their AI strategy is delivering on expectations once solutions move beyond pilots. The discussion will focus on how teams measure adoption, productivity, and innovation impact, and how these signals inform decisions around scaling, refining, or adjusting AI initiatives.

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We'll Cover:

• How organisations measure AI adoption and usage in practice
• Approaches to tracking productivity and efficiency gains across teams and workflows
• How leaders assess whether AI investments are supporting innovation and business outcomes
• The considerations used to decide when to continue, scale, or adjust AI programs

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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AI initiatives succeed or fail on the quality of their data. This session explores how organisations are preparing their data environments to support enterprise-scale AI  ensuring information is trustworthy, accessible, and structured for learning. You’ll hear practical strategies for transforming fragmented data pipelines into reliable, high-performance foundations that turn AI experiments into operational success.

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We'll cover:

• Assess Readiness: Learn how to evaluate whether your data is fit for AI adoption.
• Build Trust: Discover governance, quality, and accessibility practices that scale.
• Deliver Value: See how mature data foundations accelerate measurable AI outcomes.

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AI is beginning to influence how teams operate, how work is coordinated, and how roles evolve across the enterprise. But there’s still little agreement on what this means in practice, from where AI assistants fit into daily workflows, to how management layers, team structures, and training approaches may change.

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This interactive session puts these questions 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.

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We'll Cover:

• How AI assistants are starting to support individual roles
• How team structures and management practices may evolve
• Which skills and training pathways become more critical
• Early lessons from organisations experimenting with AI in daily work

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