Opening and Welcome
Andrew Delaney, President & Chief Content Officer, A-Team Group

Practitioner Keynote: Moving beyond the AI hype to deliver tangible, bottom-line business value

Data Leaders Panel: Converging data and AI leadership – CDO, CTO and CAIO: who owns what?

As AI moves into production across capital markets, the boundaries between the CDO, CTO and Chief AI Officer are blurring. Some firms are combining the roles, while others are redrawing mandates. This session explores how leading firms are structuring data and AI leadership. It looks at who owns data, platforms, models, agents and outcomes, and how firms keep the data foundations funded and build the talent they will need.

• Are the CDO, CTO and CAIO roles converging, combining or competing? Which models are working: a single CDAO, or separate mandates with clear boundaries?
• Who owns what across data, platforms, models, agents and business outcomes? Where do the handoffs sit, and where do they break down?
• As AI attracts the investment and the headlines, how do you keep data quality and governance a funded priority?
• When an AI or agentic outcome goes wrong, how is accountability shared between data inputs, technology platforms and model outputs?
• With AI tools embedding across business lines, how do you maintain one enterprise-wide data and AI strategy?
• As junior roles are automated away, how do you build the talent pipeline and preserve the critical thinking and judgement firms still need?

Keynote: Sponsorship available

A-Team Research Report Update

Andrew Delaney, President and Chief Content Officer, A-Team Group

Panel: Re-defining governance and accountability for the AI era

This session explores how firms must evolve from manual, static governance to a model-ready framework that focuses effort on the data that matters most and establishes clear accountability for the data feeding AI and agentic workflows.

• How should firms define and identify Critical Data Elements in the age of AI, when agents can consume almost any data?
• How can firms move away from “everything is critical”? Is there a minimum threshold data must meet to be considered AI or agent ready?
• How can governance be automated and embedded into pipelines and workflows, rather than applied manually after the fact?
• Who is accountable for the data inputs to AI-enabled workflows, and how is that ownership evidenced?
• How can firms extend existing frameworks such as model risk, EUC controls and BCBS 239, rather than building a separate governance layer for AI?
• What are the benefits of getting it right, and the penalties of getting it wrong?

Keynote: Sponsorship available

Panel: Scaling with trust – data quality engineering and the true cost and complexity of poor data

Learn how to transform data quality from a back-office cost center into a high-performance engine that ensures AI reliability while creating the operational savings needed to fund innovation.

• How are firms transitioning from reactive data cleansing to proactive shift-left data quality engineering?
• What unique data quality challenges do RAG (Retrieval-Augmented Generation) architectures introduce?
• How can machine learning be deployed to autonomously detect anomalies and heal broken data pipelines?
• What is the role of data observability in moving from static dashboards to continuous AI validation?
• How do you manage data drift in real-time to ensure AI models remain accurate and deterministic?
• How can firms reframe data quality metrics in terms of operational efficiencies or savings to help fund AI, or drive business performance?

Keynote: Sponsorship available

Panel: Building context for agents: data catalogs, semantic layers and trusted data

Lifecycle stage: Organise and enrich. Agents are only as accurate as the context they are given. This panel looks at how firms build the catalogs, semantic layers, ontologies, business definitions and quality signals agents need to understand their data, who owns that context, and whether it measurably improves the answers agents give.

• What context do agents need to give accurate answers and how much are firms seeing it improve accuracy or reduce errors?
• How can firms model business meaning so agents interpret data consistently across desks, products and entities?
• Is the catalog now the agent’s context layer and MCP the integration layer? How are firms connecting catalogs, glossaries and lineage to the agents that use them?
• How can quality scores and data contracts be passed to agents as trust signals, so an agent knows when not to rely on a piece of data?
• Who should own business definitions when AI can generate them, and how can firms stop meaning drifting across business lines?
• What business outcomes does better context deliver: faster answers, fewer errors reaching clients, or less rework?

Panel: Data products in practice – building governed, trusted data products for reuse across the enterprise

This session explores how to build governed, trusted data products that can be reused across the enterprise, from regulatory and client reporting to risk, operations and AI, while avoiding proliferation and using data contracts to sustain trust.

• Which governed datasets are firms turning into data products, and what measurable value are they creating for regulatory, management or client reporting?
• What makes a data product ready for AI and agents to use, and how is that different from a product built for reports and dashboards?
• As users and agents query data products in natural language, how do firms keep answers consistent and explainable?
• How are data contracts helping firms agree quality and schema between producers and consumers, and avoid one-off changes that break reuse?
• How are firms valuing, charging back or selling data products, internally and to clients?
• Where are data products creating new revenue or improving client experience, not just cutting cost?

Champagne Roundtable Discussions

Join a roundtable discussion for a deep-dive, interactive discussion with your peers on some of the day’s most important themes. Address common problems, benchmark your progress and come away with practical solutions and takeaways!

1. AI-driven data quality and observability
2. Skills, talent and the future of the data management profession
3. BCBS239: The regulation no one wants to discuss
4. Proving your AI: auditability, traceability and human accountability

Event ends