AI Innovation Summit

As enterprises expand their use of ChatGPT, cloud-based AI and a growing range of models, costs can escalate when every task is routed to the same premier capability. This session explores how leaders can build a balanced AI portfolio by determining where frontier-level intelligence is justified, where lower-cost alternatives are sufficient, and how process design, modernisation and ROI should shape those choices. Leave with a clearer approach to scaling AI across mission-critical environments without allowing deployment costs to erode business value.

The four mission streams reconvene to turn Day 1's challenges into a delivery blueprint. Building directly on The Catalyst, partner-led working sessions move each stream from diagnosis to design — shaping the practical, prioritised blueprint each theme will carry into execution.

Track 1: From Experimentation to Enterprise Impact: How AI Leaders Are Scaling Beyond the Pilot Stage​
Track 2: From Cost Centre to Growth Engine: Proving Business Value from AI Investments
Track 3: Governance That Accelerates Innovation: Building the Foundations for Scalable AI​
Track 4: Fix the Foundation First: Why Data Readiness Determines AI Success​

Most AI programs don't stall because the technology fails. They stall because leaders can't see what AI is doing well enough to trust it at scale. As tools, agents and models spread across the business, sprawl creates risks like the July 2026 Hugging Face breach, where AI agents escaped their sandbox and compromised a real company with no attacker involved. This session gives a practical plan for scaling AI securely: track policy alignment, sort use cases to spot misuse and prove value, monitor what agents actually do, and assess development risk through AI security posture management. Leave with the visibility you need to move AI past the pilot and show measurable outcomes.

Many AI initiatives remain trapped in pilot because organizations wait for perfect data, underestimate adoption or treat automation as a substitute for human expertise. This session explores how Edward Don redesigned a complex RFP process from five weeks to five days by combining AI with institutional knowledge and human judgment. Leave with a clearer approach to selecting commercially meaningful problems, moving forward despite imperfect foundations and building AI capabilities that increase capacity, improve through feedback and earn employee trust.

AI initiatives often stall because leaders use the same language to describe very different ambitions. In this live whiteboard clinic, executives will bring a real use case and determine whether AI is being asked to help people understand, think, do or deliver, at what organizational scale and with what level of human control. Leave with a clearer definition of the initiative, the missing foundations exposed and a practical next move for progressing it.

Enterprise AI portfolios are filling with plausible ideas, but not every use case deserves to reach production. In this live investment committee, leaders will assess competing AI opportunities, respond to new information around business value, exposure, autonomy, opacity, data readiness and adoption, and decide what should be approved, redesigned or rejected. Leave with a practical decision map for matching each initiative to the right level of investment, oversight and post-deployment review.

Every business is operating in real time, but the data powering AI often isn’t. Critical enterprise data remains fragmented across systems, clouds, and applications, and can lack the freshness, context, and trust AI needs. As organizations move from AI experimentation to enterprise-scale deployment, closing this gap becomes increasingly important.

In this keynote, we’ll explore what it takes to build and scale AI across the enterprise, starting with the data foundation. We’ll discuss how leading organizations are bringing together data at rest and in motion to deliver trusted, real-time context for AI across hybrid environments. Attendees will gain insight into how a stronger data foundation can help move AI beyond pilots, enable AI systems and agents to make more informed decisions and take meaningful action, and ultimately drive business value at scale.

As AI moves from experimentation into increasingly consequential parts of the enterprise, the challenge is no longer simply whether to govern it, but how to create enough control without slowing the innovation you are trying to enable. This session explores how leaders can build proportionate, risk-based guardrails that give teams clarity on where they can move quickly, where greater oversight is required, and how governance needs to evolve as AI scales.

Traditional ERP is dead and leaders know they cannot wait for slow and costly cloud re-implementations that delay progress. Agentic AI ERP shifts the core from a passive system of record to an intelligent system of action that automates work, learns continuously and delivers real outcomes fast. Skip upgrades and migrations that add no value. Join this session to learn how you can leapfrog your competitors stuck in subscription lock-in migrations and realize true self-funded AI outcomes in weeks, not years.

Most enterprises hold a portfolio of AI pilots and very little in production. The common diagnosis is a technology problem. The real constraint is that AI is managed with delivery methods built for deterministic software, and AI is probabilistic.
Stephen Straus, co-founder and CEO of KUNGFU.AI, an AI-native management consulting and engineering firm, has led 230+ engagements across 30+ industries, since 2017. He shares the stage-gate model his teams use to move AI from proof of concept into production, drawn from his new book Wayfinding.

Complex enterprises rarely fail at a single point; disruption often emerges through hidden dependencies across applications, infrastructure and customer journeys. This session explores how AI can strengthen Failure Mode and Effects Analysis by connecting historical incidents, telemetry, system changes and service dependencies to identify where risk is most likely to propagate. Leave with a clearer approach to prioritising resilience around the customer and business journeys that matter most, moving from reactive incident response towards earlier, more consistent intervention.

The mission streams converge for a candid, joint verdict. The culmination of the mission-track journey: streams come together to separate what's real from what's hype, and to lock in the concrete commitments each theme carries back to the business. The output every delegate leaves with.

Track 1: From Experimentation to Enterprise Impact: How AI Leaders Are Scaling Beyond the Pilot Stage​
Track 2: From Cost Centre to Growth Engine: Proving Business Value from AI Investments
Track 3: Governance That Accelerates Innovation: Building the Foundations for Scalable AI​
Track 4: Fix the Foundation First: Why Data Readiness Determines AI Success​

As AI moves from answering questions to taking action, enterprises face a new set of challenges around scale, control and value. This fireside explores what happens at that inflection point: how to manage the economics of adoption, avoid isolated optimisation across complex systems, and decide how much autonomy agents should be given. Leave with a clearer view of what needs to be in place before AI can move safely and effectively from insight into execution.

AI tools are making individuals faster, but those gains often disappear across disconnected teams, slow handoffs and fragmented workflows. This session examines how leaders can turn personal productivity into enterprise performance by redesigning how people, agents and information move through the organization. Leave with a clearer view of how to build shared context, accelerate cross-functional execution and make AI adoption visible in business outcomes rather than isolated efficiency gains.

Few people have had a closer view of AI moving from promise into everyday professional work than Greg Lambert and Marlene Gebauer. Across more than 300 episodes of The Geek in Review, they have spent years interrogating the people building, buying and implementing new technology across a knowledge-intensive industry where judgment, trust and accountability matter. This closing fireside draws on that vantage point to cut through the hype and ask what organizations are actually learning about productivity, adoption, human judgment and the unintended consequences that emerge once AI becomes part of how work really gets done.

AI investment continues to rise, yet enterprise value is still being lost between strategic ambition and operational execution. In this Oxford-style debate, two executive leaders will argue whether AI value is won through board-level direction, investment discipline and responsible governance, or through redesigning the workflows, decision rights and human-AI relationships where the business actually operates.