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 or the role of the new CDO and CAIO

Data Leaders Panel: The evolving data and AI operating model – Synergy, governance and accountability

• How are CDOs successfully structuring their relationship with business-line AI leaders to ensure revenue-driven innovation is built upon a foundation of data integrity?
• As AI initiatives attract significant investment, what strategies can data leaders use to ensure that the underlying data quality and governance work remains a funded priority?
• How can the CDO’s mandate for data risk management be integrated into the business-line validation process before a model or agent goes live?
• With AI tools rapidly embedding within siloed business functions, what architectural frameworks can CDOs deploy to maintain a unified, enterprise-wide data strategy?
• How is the Office of the CDO leading the charge on enterprise-wide data and AI literacy programs to build a cohesive, data-driven organizational culture?
• In the event of an unintended model outcome, how should responsibility be shared between those governing the data inputs and those managing the model outputs?

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

• How should firms define and identify Critical Data Elements (CDE) in the age of AI?
• How can firms move away from ‘everything is critical’? Is there a minimum threshold data must pass to be considered mesh ready or Agent ready?
• Does the definition of critical content change when dealing with structured vs. unstructured data?
• What does a data governance framework look like that can bridge the structured and unstructured divide and establish clear accountability for data inputs for AI enabled workflows?
• What are the benefits of getting it right and penalties of getting it wrong?

Keynote: Sponsorship available

Panel: Building the intelligence layer – semantic architecture for AI-ready data products

• What does a production-grade semantic layer look like, what has been built, what failed, and what was learned?
• When should firms virtualise vs. physically consolidate data and how does that decision change when AI agents are the primary consumer rather than human analysts?
• How do you design an Enterprise Conceptual Data Model that spans both structured instrument data and unstructured content without becoming a multi-year modelling exercise?
• What is the right interface between a semantic layer and an agentic system: ontologies, context and knowledge graphs, vector stores, all of these?
• How do you govern the semantic layer itself, who owns the business glossary, who can extend the ontology, and how do you prevent definition drift across federated domains?
• How should firms sequence their build: data catalogue first, or domain-by-domain data products first and how does the answer differ for sell-side vs buy-side?

Keynote: Sponsorship available

Panel: Data products regulation – governing AI-ready data products in a shifting regulatory landscape

• How are regulators under frameworks such as DORA and the EU AI Act beginning to treat “data products” as a distinct, accountable asset class rather than just downstream data feeds?
• Who is accountable when a poorly governed data product feeds a non-compliant AI or agentic decision to the data product owner, the model owner, or the business line?
• What certification and documentation standards should a data product meet ownership sign-off, quality attestation, usage terms before it can be considered regulation ready?
• How should firms build regulatory requirements into the data product lifecycle from design through to retirement, rather than retrofitting compliance after the fact?
• What lessons can data product regulation borrow from existing critical data element (CDE) governance frameworks, and where do they fall short for AI-era data products?
• How do firms manage regulatory obligations for data products that span multiple jurisdictions, as UK, EU and US AI and data rules continue to diverge?

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

• How are firms transitioning from reactive data cleansing to proactive shift-left data quality engineering?
• What is the true, fully loaded cost of poor-quality data supplied by external vendors, once downstream cleansing, remediation and reconciliation effort across the business is accounted for?
• How can firms build data quality accountability and remediation costs into vendor contracts and SLAs, rather than absorbing the cost and complexity of poor vendor data internally?
• 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 including the cost and complexity created by poor vendor data in terms of operational efficiencies or savings to help fund AI and drive business performance?

Panel: Beyond Regulatory Compliance: Building Scalable AI Governance Through Data Controls

• How can existing model risk, EUC and conduct frameworks be adapted to govern AI without creating another layer of bureaucracy?
• Extending data ownership, lineage and accountability into shadow IT, spreadsheets and user-developed AI tools
• How do firms maintain data quality and control when AI is consuming and generating data at scale?
• Embedding conduct, accountability and human oversight into automated and AI-enabled workflows
• Creating consistent data governance standards across asset management, capital markets and the wider organisation
• Where should the responsibility for AI data governance sit between Data, Risk, Compliance, Technology and the business?
• Moving from regulatory-driven controls to a proactive, enterprise-wide governance model that can scale with AI adoption

Panel: Decisions as a product – from static assets to intelligent agents

• How are firms moving beyond report building to embrace the shift to agents that uncover unique insights and patterns?
• Why is the industry measuring decision velocity and consistency as the new gold standard for ROI, rather than traditional data download metrics?
• How can organizations maintain ownership/accountability of outcomes as AI takes on larger portions of the value chain?
• What can be done to reassure consumers/customers that proper controls remain in place in an AI-enabled workflow?
• Is there a threshold where automation becomes a liability? How do we identify when we have hit the ‘too much AI’ limit?
• How is the role of the data office changing to manage a digital workforce, and what are the strategies for transitioning safely from human oversight to agent-centric decision models?

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. The true cost of poor vendor data quality – who should pay to clean it up?
2. Data products regulation – building compliant, defensible data products
3. Scaling semantic layers and knowledge graphs for agentic AI
4. Governance and accountability in the agentic AI era
5. Structuring the CDO’s relationship with business-line AI leaders

Event ends