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 organisations move beyond AI assistants toward autonomous, goal-driven systems, agentic AI is reshaping how work gets done across the enterprise. This session explores how AI, automation, and enterprise applications are converging to accelerate innovation, streamline operations, and deliver measurable business value.Key takeaways:

• Understand the evolution from AI assistants to agentic systems and how autonomous AI is reshaping enterprise operations.
• Discover how AI, applications, and automation work together to streamline workflows, modernise legacy systems, and unlock greater business value.
• Build a trusted foundation for enterprise AI by enabling secure, scalable, and governed deployment of agentic systems across the organisation.

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

Most organisations aren't struggling to choose an AI model—they're struggling to prepare their enterprise for AI.

As AI agents move from pilots into production, fragmented data, disconnected business knowledge and inconsistent processes quickly become the biggest barriers to scale. The challenge isn't simply better AI; it's giving AI the context it needs to operate confidently across the enterprise.

In this session, Publicis Sapient shares practical lessons from enterprise transformation programmes, including what organisations consistently underestimate and how they're building trusted foundations for agentic AI without starting from scratch.

You'll learn:

• Why enterprise readiness matters more than model selection.  

• The role of enterprise context in scaling AI.  

• Practical approaches for building on existing technology investments.  

• Lessons learned from real-world enterprise transformations.  

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

What happens when AI turns every employee into a builder?AI changed who can build. Employees across the business can now create workflows – apps, agents, and automations, faster than ever before. The result is a growing wave of "wild code": AI-generated software, automations, and agents that solve real business problems but exist outside traditional governance, operational ownership, and support models.For IT, this creates a new challenge. How do you empower teams to move quickly without creating more risk, complexity, and technical debt? How do you support innovation without opening the door to shadow AI, data leakage, and unmanaged systems?In this session, you'll see:

• How IT can unlock AI-driven innovation across the business while maintaining the governance, visibility, and control needed to operate at scale.
• How organisations can provide teams with the freedom to build using the tools, models, and systems that best fit their needs, without creating unmanaged AI, wild code, or new operational 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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You have traces, eval scorecards, and token counts telling you your AI feature or chatbot is running. What they cannot tell you is whether it is actually working, whether it moved retention, conversion, or revenue at all. That is where agent analytics comes in: a way to watch what your agent is doing and what your users are doing in the same place, so a trace is no longer the end of the story. In this session, we will show what it looks like when trace and event analytics sit under one roof, with agent behaviour and user behaviour in the same analytics layer. We will also explore how to treat the choice of LLM, especially in chatbot-type features, as a testable hypothesis rather than a gut call.

• Why trace analysis alone leaves you unable to answer whether your AI feature is working, and how agent analytics closes that gap
• How agent behaviour and user behaviour sitting in one system ties AI features directly to business outcomes
• How to A/B test the LLM behind a chatbot feature against real retention, conversion, and cost tradeoffs, not vibes

Orchestrating AI Transformation That Builds Foundations, Drives Adoption, and Delivers Real ROI

Most organisations have moved beyond experimentation, yet many AI initiatives still struggle to scale and deliver measurable business value. Success depends not only on technology, but on the leadership, operating models, and adoption strategies that turn AI from isolated pilots into enterprise-wide transformation.

Key takeaways:

• Build the foundations for sustainable AI transformation by aligning strategy, governance, operating models, and business priorities from the outset.
• Overcome the adoption gap by addressing organisational resistance, embedding AI into day-to-day workflows, and driving lasting behavioural change.
• Turn AI investment into measurable outcomes with practical lessons from real-world transformation programs that successfully moved from pilots to enterprise scale.

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!