Most enterprises have modern data platforms, yet few see the AI value they expected. Gartner predicts that 60% of AI projects will be abandoned through 2026 due to a lack of AI‑ready data, and McKinsey reports that nearly two‑thirds of firms still fail to scale AI because of fragmented data and poor quality. The shift to data products is emerging as the answer: domain‑owned, discoverable, trusted assets built for consumption, not reporting.

This panel brings together leaders who have made the transition from centralised, request‑driven data models to federated, product‑oriented organisations capable of supporting AI at enterprise scale. They’ll discuss what the operating‑model change really required, how they overcame resistance, and what “good” looks like once data products become the backbone of AI delivery.

AI adoption is widespread, but true enterprise‑level impact remains rare. McKinsey’s 2025 survey shows that 88% of organisations use AI, yet only one‑third have scaled it, with 39% still stuck in pilots. Despite IDC reporting an average 3.7x ROI on generative AI investments, BCG finds that 60% of companies see little or no material value, while the top 5% of AI leaders achieve 5x the revenue gains of their peers. The difference isn’t tooling, it’s leadership ownership, measurement discipline and the ability to turn pilots into repeatable production blueprints.

In this rapid‑fire Lighthouse Showcase, five senior data and AI leaders each present one initiative that successfully moved from experimentation to scaled value, in just 5 slides and 5 minutes. They’ll share the metrics that mattered, the governance that unlocked funding, and the operating‑model shifts that enabled production‑grade AI.

A high‑density, insight‑rich session for leaders ready to move beyond pilots and build AI programmes that compound in value.

Most enterprises have an AI strategy; far fewer have an AI stack capable of delivering it. Gartner predicts that 40% of enterprise applications will embed task‑specific AI agents by 2026, yet McKinsey finds that only one‑third of organisations have begun to scale AI, despite 88% reporting regular use. The bottleneck isn’t ambition but architecture.

This keynote sets out what a production‑grade AI stack really requires: trusted data foundations, governed pipelines, and infrastructure built for transparency, auditability and scale. With Gartner warning that 60% of AI projects will be abandoned through 2026 due to poor data quality, the stakes have never been higher.

Through real‑world deployments across sectors, this session will outline the architectural decisions that determine whether AI compounds in value or stalls after the first use case, and offer a practical framework for assessing where to invest next.

As enterprises shift from batch analytics to real‑time AI, traditional ETL approaches can’t keep up. This workshop demonstrates how leading organisations are adopting DataOps practices, low‑code integration, and automated pipeline orchestration to deliver trusted data at speed. We explore real examples of API‑driven integration, CI/CD for data, and how DataOps reduces technical debt while enabling faster experimentation.

AI regulation is no longer theoretical. Prohibited practices have been enforceable since February 2025, general‑purpose AI rules since August 2025, and by 2 August 2026, the full EU AI Act high‑risk requirements take effect, with penalties of up to €35 million or 7% of global turnover. Around 35% of enterprise AI systems already fall into the high‑risk category, and compliance can cost €52,000 per system per year. For UK organisations, the regulatory perimeter still applies through EU subsidiaries and EU‑intended outputs, making governance a strategic capability.

This panel brings together technology, data and risk leaders who are treating regulatory readiness as a competitive advantage. They’ll explore how to build governance‑by‑design, conduct AI system inventories that double as strategic asset maps, navigate UK–EU regulatory interplay, and make the business case for investing ahead of the enforcement curve.

AI models are only as strong as the master data they rely on. This workshop explores how organisations are modernising MDM to support AI‑ready data products with consistent definitions, golden records, and governed domain ownership. Through real customer examples, we unpack how MDM reduces operational risk, improves customer experience, and accelerates AI use‑case onboarding across finance, supply chain, and customer operations.

As AI workloads scale, silent data failures have become one of the biggest hidden risks to model accuracy, regulatory compliance, and operational reliability. This workshop gives data leaders a practical look at how enterprises are deploying modern data observability to detect anomalies, enforce quality rules, and maintain trust in production pipelines. We explore real case studies on lineage‑driven root‑cause analysis, automated monitoring, schema drift detection, and how organisations are reducing incident resolution times from days to minutes.

AI adoption has surged, yet most organisations still struggle to turn experimentation into enterprise‑level impact. McKinsey’s State of AI 2025 shows that only 39% of companies report material financial gains from AI, while Deloitte finds that 70% of pilots never reach production due to weak data foundations, fragmented governance and rising regulatory pressure. At the same time, the EU AI Act is reshaping expectations for transparency, oversight and data integrity, turning trust into a strategic differentiator.

This opening keynote sets the scene for the day by exploring how leading organisations are building AI systems that are safe, governed and production‑ready. We’ll examine the shift from hype to value, the capabilities required to scale responsibly, and why trusted data and responsible AI now define competitive advantage.

The 2025 AICDI Global Insights Report shows that less than a third of companies have a dedicated team for AI governance, and only 13% have policies ensuring human oversight, revealing a major leadership and operating‑model gap.

Modern enterprises require a unified leadership blueprint where data, technology and security operate as one.

This fireside chat explores how the roles of CDO, CIO and CISO are converging as AI becomes mission‑critical infrastructure. We examine how leading organisations are redesigning operating models, governance forums and accountability structures to balance innovation, security, resilience and compliance.

Most enterprises have a responsible AI policy. Far fewer can point to a decision it actually changed.

The gap between principle and practice is closing fast, not by choice, but by necessity. Gartner predicts 40% of enterprise applications will feature AI agents by end of 2026. Systems that act autonomously, often without a human at the moment of decision. PwC found that 60% of executives believe responsible AI boosts ROI and efficiency, yet nearly half say turning those principles into operational processes has been their biggest challenge. And McKinsey's research is unambiguous: organisations seeing real AI returns are three times more likely to have strong senior leadership ownership of AI governance. Responsible AI is not a constraint on performance, it is a precondition for it.

This fireside chat cuts through the framework debate and into the operational reality. How do you embed governance into the way engineers build and models are monitored? How do you maintain human oversight when agents are making thousands of decisions a minute? And how do you measure whether your responsible AI programme is actually working?

McKinsey’s 2025 survey shows that half of high‑performing organisations are redesigning workflows to integrate AI, and workforce transformation is now a top CEO priority. Research also highlights that only 18% of organisations report high levels of data literacy across all business roles.

This panel explores how organisations are addressing the widening skills gap across data engineering, ML engineering, AI product management, governance and literacy. Speakers will share practical strategies for reskilling, redesigning roles, embedding AI into daily workflows and building a culture that embraces AI, not fears it.

As organisations race to operationalise AI, strengthen governance, and prepare for the regulatory shifts reshaping Europe, the leaders who will win in 2026 are those who turn today’s insight into tomorrow’s execution. Fast. This closing session distils the summit’s most important lessons across trusted data foundations, Responsible AI, regulatory resilience, data product operating models, and enterprise‑grade AI scale.

Attendees will be guided through a structured reflection to identify one high‑impact change they will implement in their data or AI strategy within the next 30 days — whether tightening lineage controls, accelerating a data‑product rollout, strengthening model oversight, or redesigning cross‑functional ownership.

By ending with shared commitments, this session transforms the summit from a day of ideas into a catalyst for measurable progress, equipping every leader in the room with a clear next step and the collective momentum to deliver it.