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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We are in the midst of a Cambrian explosion in AI, with an ever-increasing choice of sophisticated AI models both closed and open source that are available to enterprises.

As a result, the volume of AI workloads are expanding exponentially and enterprise are rushing to incorporate the latest AI capabilities provided by these latest models.

The challenge is that these Enterprises must adopt a structured and methodical approach to their AI infrastructure to ensure success.

In this Keynote, we’ll cover:

• How Enterprise Architecture has evolved after chatGPT
• Which AI capabilities you must champion in your enterprise
• Most importantly, where to place your intelligence?

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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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As AI agents take on more autonomous roles, the identity and access patterns built for humans and APIs no longer hold. This session draws on Auth0's real-world experience securing agentic workflows where misconfigured permissions can have serious consequences.

We'll Cover:

• How Auth0 handles agent authentication and authorisation in real-world agentic workflows
• How to maintain full audit trails and enforce least-privilege access for autonomous systems
• Why standards-based identity makes it easier to ship AI features without security trade-offs

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?

Most AI governance slows teams down because it treats every project the same. This session shares a simple framework for sorting AI initiatives by risk and autonomy, so low-risk work moves fast, and only the initiatives that need scrutiny get it.

We'll cover:

• Not every AI use case needs the same level of oversight. Governance should scale with risk and autonomy, not apply uniformly.
• A simple routing framework (based on autonomy, data sensitivity, error cost, and system integration) can cut decision time without cutting rigor.
• Good governance is a speed enabler, not a blocker. The goal is fewer bottlenecks, not fewer controls.

AI is changing how software is built, with more code being created faster and at greater scale. AppSec needs to keep up without simply creating more alerts for security and development teams to manage.
This session looks at how Checkmarx Fusion, Triage & Remediation can help teams find risk more accurately, fix vulnerabilities faster and maintain control as AI becomes part of software development.

We’ll cover:

• Capture — Find the risks that matter. Combine security engines and AI to improve vulnerability detection without sacrificing accuracy.
• Eliminate — Get from finding to fix faster. Prioritise real risks and reduce the manual work involved in remediation.
• Govern — Keep control as AI scales. Maintain visibility, policy and human oversight as AI becomes embedded in development.

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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AI tools have made it easy for teams to prototype internal apps and agents, but taking them into production often creates a governance gap: fragmented accounts, unclear ownership and unmanaged access to company systems.

This talk explores how organisations can give builders a fast path from prototype to production without compromising security or control.

AI can help engineers write code faster, but does that actually lead to better software? This session looks at the evidence behind AI in software engineering, how teams can measure its real impact, and what changes when AI-generated code moves into production.

Key Takeaways:

• Measure the real impact. Understand whether AI is actually improving engineering performance.
• Focus on outcomes, not more code. Faster development only matters if it leads to better software.
• Get AI-generated code ready for production. Put the right testing, reviews and guardrails in place before it goes live.

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!