CDAO FSI UK 2026
Presentations
General Session & Track A
- Why many AI initiatives in financial services fail to move beyond pilot stage and the common organisational and data maturity barriers preventing enterprise scale.
- What separates AI leaders from laggards including differences in operating models, leadership alignment, and business ownership of AI initiatives.
- How governance is evolving from a control function into an AI enabler and what effective model risk and data governance looks like in AI-mature organisations.
- Where leading financial institutions are prioritising investment across data platforms, talent, operating models, and AI infrastructure to accelerate adoption.
- The critical foundations firms must fix first, whether that is data quality, architecture, skills, or business alignment, before AI can deliver measurable value.
Francesco Di Costanzo
Chief Executive Officer
JAJA FINANCE
- How the traditional CDO role is evolving in an AI-native financial services landscape.
- Identifying the boundary between data leadership and AI leadership, and what overlaps require attention.
- New organisational expectations for CDAO/CAIO executives, including collaboration with business units, compliance, and technology teams.
- Key skills, mindset shifts, and leadership qualities required to succeed as a next-generation data and AI leader.
- Insights from early adopters who have successfully transformed their CDO role into a CAIO function.
Vladimir Bendikow
Chief Data & AI Officer
FirstBank UK Limited
- How financial institutions can move from AI experimentation to production-ready deployment
- Embedding trust, transparency, and responsibility into AI systems from the ground up
- Governance frameworks that enable innovation without increasing regulatory risk
- Practical approaches to operationalising responsible AI across hybrid cloud environments
Joshua Majhutiak
Partner, Enterprise Strategy
IBM
- Why the shift from traditional machine learning to large language models demands a fundamentally different approach to data architecture and data foundations
- The talent challenge: what skills organisations actually need now versus what recruiters and the market think they need
- How to approach data chunking, preparation, and structuring so that LLMs can consume and process it effectively
- Managing hallucination risk in enterprise AI deployments — governance, validation, and keeping humans in the loop
Javier Morao
EMEA Data & AI Head
Citi Bank
- Turning regulatory mandates into strategic opportunities for data sharing and innovation.
- Open Finance use cases: enabling financial inclusion, better customer insights, and new revenue streams.
- Data sharing strategies: APIs, consent management, and security considerations.
- Leveraging ecosystems for competitive advantage while maintaining regulatory compliance.
Jean Perez
Director, Data Analytics & AI
Collinson group / Valuedynamx
- Interplay of deterministic models and agentic to deliver scalable value that is robust and reliable
- Overcoming applications that don’t talk to each other – building connectivity at the right grain with the right controls
- Centralised vs federated
- Lessons learnt in a large global professional services firm’s Data Management journey
Beatrice Russell
Global Data Management Office Leader
AON
Track B
A year ago, many organisations were still asking what a semantic layer is. Six months ago, the analytics industry decided that it would be the Next Big Thing. We know why; We all want AI at scale, but AI performs poorly without context. In this session we will address the key questions every CDO should ask before investing in a semantic layer.
We will cover how a semantic layer can drive value for your organisation, and the risks involved in acquiring and deploying one. We will also examine what a semantic layer will not do for you. Expect real-world examples and practical advice you can use to make the right decision for your enterprise.
Nick Barth
Senior Sales Engineer
STRATEGY