Agenda & Speakers

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

Kick off the day with a welcome from your MC and a look at what's ahead across both streams.

Most enterprise AI is now live, but production is exposing the infrastructure and architecture gaps prototypes never had to face.

This keynote frames what it takes to run AI at enterprise scale, reliably, in systems that can't afford downtime.

• Why scaling AI is now an infrastructure problem, not a model problem
• What breaks first when AI moves into critical, always-on systems
• The architecture decisions that determine whether AI scales or stalls

With the EU AI Act landing in stages and the UK pursuing a regulator-led approach rather than a single statute, responsible AI has become a moving target for boards and technical teams alike.

This session covers what is actually required now and how to build governance that keeps pace with the rules.

• What regulators expect from AI governance today, in practice
• Building a responsible AI framework that adapts as rules change
• Where governance ownership should sit between technical and executive teams

As AI agents start talking to other AI agents without a human in the loop, traditional access controls don't hold up.

This panel covers how leading teams are applying zero-trust principles to agent-to-agent communication.

• Why agent-to-agent interactions need their own trust model
• Authentication and authorisation approaches built for autonomous agents
• Where zero-trust architecture for agentic AI is still immature

Moving an AI model from a successful pilot to full production can uncover challenges that simply don't appear during testing. As usage grows, teams need to manage performance, reliability, infrastructure, and deployment without disrupting the wider business.

This case study explores how one team scaled an AI model into production, the problems they encountered along the way, and the lessons they learned from the experience.

• The performance and scaling bottlenecks that only emerged at production volume
• How the team approached model versioning, rollback, and safer deployments
• What they learned from the process and what they would do differently next time

Recharge with refreshments and structured networking with your peers.

A hands-on, interactive session working through a real enterprise AI scenario as a room. Details announced soon.

AI models are only as effective as the data that supports them. Slow, unreliable, or poorly designed data pipelines can impact model performance and make it difficult to scale AI successfully.

This case study explores how one team redesigned its data pipeline to improve speed, reliability, and model performance, while keeping existing systems running throughout the transition.

• How they identified data pipeline issues that were affecting model quality and performance
• How they redesigned the pipeline to reduce delays and improve data reliability
• How they managed the transition without disrupting existing AI workloads

Moving agentic AI from a small pilot into core business functions brings new challenges around scale, security, and governance. These challenges become even more important when AI agents begin working with sensitive financial, employee, and business data.

This case study explores how one organisation expanded agentic AI workflows across finance, HR, and other business functions, while putting the right controls in place and demonstrating measurable value.

• How they designed agentic workflows that could scale beyond the initial use case
• What guardrails and controls they introduced when agents began accessing sensitive business data
• The measurable impact on productivity, efficiency, and business outcomes after rollout

For organisations in regulated industries, adopting AI requires more than policies and good intentions. Teams need practical technical controls that allow AI to operate safely, reliably, and within regulatory requirements.

This panel explores how organisations are building guardrails into AI systems, testing them before deployment, and balancing innovation with the need for security, compliance, and oversight.

• How to design practical AI guardrails that meet regulatory and compliance requirements
• How teams are testing and validating AI before it is used in sensitive or regulated processes
• Where technical controls are essential and policy alone is no longer enough

Small-group, discussion-based sessions where you'll work through real enterprise AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Enjoy a complimentary lunch while connecting with fellow attendees.

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 voucher — as the top scorer takes the crown.

AI models can change in performance over time as customer behaviour, data, and business conditions evolve. Without the right monitoring in place, these changes can go unnoticed until they begin affecting customers, operations, or revenue.

This case study explores how one team discovered a model drift issue that nearly became a significant business problem, and how they built better observability into their AI pipeline to identify and respond to issues earlier.

• How a model drift incident exposed gaps in their monitoring and almost resulted in a measurable business impact
• How they introduced the right metrics, alerts, and performance thresholds to identify model drift before it became a bigger problem
• How improved AI observability helped the team identify and respond to issues in days rather than weeks

AI adoption is growing quickly, but proving its business value remains a challenge. Moving from a successful pilot to long-term investment requires organisations to show more than adoption rates or productivity gains: they need clear, measurable outcomes the business can understand.

This keynote explores how organisations can define AI success, measure the real impact of their investments, and build a stronger business case for taking AI initiatives beyond the pilot stage.

• Why many AI pilots struggle to demonstrate clear and measurable business value
• How to define credible AI ROI using cost savings, productivity, revenue, risk reduction, and other business outcomes
• How to turn early results into a business case that secures investment and supports AI at scale

As AI systems move into production and become part of critical business processes, organisations need to know when performance starts to change or something goes wrong. Without the right visibility, issues with models, data, infrastructure, or AI agents can go unnoticed until they affect customers or the business.

This interactive think tank will use live audience voting to explore how organisations are monitoring AI in production, where the biggest gaps remain, and what teams are prioritising next.

• Do you have the monitoring in place to detect model drift before it causes a business impact?
• Where is your biggest visibility gap: infrastructure, data pipelines, AI agent behaviour, or measuring ROI?
• Who is responsible when an AI system fails or behaves unexpectedly in production?
• Are your AI guardrails built into your technology, or do they mainly rely on policies and processes?
• Where will your next AI investment go: infrastructure, observability, governance, or scaling AI workflows?

Wrap-up of the day's key takeaways across both streams — and your chance to win some epic prizes.

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

That's a wrap on the 2027 series. See you next year.

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

Kick off the day with a welcome from your MC and a look at what's ahead across both streams.

As enterprise AI moves beyond experimentation, this session will examine how leading organisations are shifting from isolated use cases to enterprise-wide operating models that connect data, governance, automation and accountability to deliver measurable business value at scale.

• How AI leaders are moving from fragmented pilots to enterprise AI operating models that support scale, speed and measurable outcomes
• What organisations must align across data, risk, technology and business teams to embed trusted AI and automation into core workflows
• Where 2027 will separate leaders from laggards, from agent-enabled workflows and AI governance to workforce redesign and ROI accountability

This keynote will explore how the EU AI Act, UK regulator guidance and evolving standards are reshaping how enterprises design, govern and deploy AI with confidence.

• What the current UK and EU policy direction means for enterprise AI governance, accountability and risk management
• How standards and compliance requirements are influencing data use, model oversight and vendor selection
• What leaders should do now to build AI systems that are auditable, responsible and ready for change

This panel will examine why so many AI initiatives stall after the pilot stage, and what enterprises need to get right in data, governance, operating model and talent to turn promising experiments into durable production capability.

• What changes when AI moves from sandbox to scale, from proof-of-concept enthusiasm to production-grade reliability, support and accountability
• How resource gaps differ between large enterprises and organisations early in their AI journey, and what each group can realistically prioritise next
• Where leaders should invest first when budgets, skills and internal support are uneven, so progress does not depend on a handful of AI champions

Hear how an enterprise reduced fear, confusion and resistance around AI by addressing the concerns of employees, executives and customers with clear communication, visible safeguards and hands-on support.

• How the team identified where hesitation was coming from, whether it was job security, trust, unclear value or fear of change
• What messages, education and engagement tactics helped executives, employees and customers feel more confident about AI adoption
• The change management lessons other organisations can use to make AI feel useful, safe and worth embracing

Recharge with refreshments and structured networking with your peers.

A hands-on, interactive session working through a real enterprise AI scenario as a room. Details announced soon.

Hear how one organisation tackled unclear AI ownership, inconsistent controls and rising compliance pressure to make enterprise AI safer and more scalable.

• How the team identified the governance gaps blocking AI from moving beyond pilots
• What practical controls, standards and workflows were introduced to keep AI deployment moving without slowing innovation
• The lessons learned for other organisations trying to build trust in AI while managing regulation, privacy and internal risk expectations

Learn how simplifying a crowded AI tool landscape reduces duplication and confusion, and creates clearer ways of working that made teams faster and more effective.

• How organisations can identify overlapping platforms, duplicated workflows and hidden waste across their AI and automation stack
• The practical steps needed to rationalise tools, streamline ownership and reduce confusion for business and technology teams
• What metrics and operating changes prove the simplification is working, from lower costs to better adoption and clearer accountability

This panel will explore how organisations can bring employees, executives and customers along the AI journey by aligning communication, trust, training and leadership at every stage of adoption.

• How to build executive confidence in AI programs so leaders champion change
• What employees need to hear, learn and experience for AI adoption to feel useful, safe and relevant to their day-to-day work
• How to manage customer expectations when AI changes service, decisioning or engagement models, without damaging trust
• The change management practices that help AI programs stick, from clear ownership and phased rollout to feedback loops and measurable adoption

Small-group, discussion-based sessions where you'll work through real enterprise AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Enjoy a complimentary lunch while connecting with fellow attendees.

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 voucher — as the top scorer takes the crown.

Explore how leaders can evaluate AI programs honestly, recognise when a use case is underperforming, and make the hard call to pause, simplify or scale down before more time and budget are lost.

• The early warning signs that an AI program is not delivering, from weak adoption and poor data quality to unclear value and rising operational drag
• How to build a practical review framework for deciding whether to fix, pause or stop a program, rather than endlessly funding a weak initiative
• How to reframe scaling down as a strategic decision that frees resources for higher-value AI use cases

This keynote will explore why education, understanding and training are essential to successful program adoption, and how organisations can equip both employees and customers to use new tools with confidence.

• Why training is not a tick-box activity, but a core enabler of adoption, value realisation and long-term program success
• How to design learning that meets different needs across workforce roles, leadership levels and customer segments
• What gets in the way of understanding new programs, from jargon and poor communication to lack of confidence and inconsistent support
• How organisations can turn education into a competitive advantage by building trust, improving usage and reducing resistance to change

This think tank will examine how organisations can align AI priorities, governance and execution across teams, and how to make responsibility for each program clear from the start.

• How to define ownership for AI programs so business, technology, data and risk leaders know who is accountable at each stage
• What governance model helps avoid duplication, competing priorities and shadow AI initiatives across the enterprise
• How leadership teams can set shared standards for data, security, compliance and value tracking without slowing delivery
• What practical decision rights should sit with central teams versus business units when AI programs move from idea to implementation

Wrap-up of the day's key takeaways across both streams — and your chance to win some epic prizes.

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

That's a wrap on the 2027 series. See you next year.