CDAO Financial Services & Insurance UK 2026 schedule

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know.  Have fun! 

• 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.

• 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.

• Why clean, governed, and harmonised data remains the most persistent barrier to AI success in financial services.
• Common pitfalls in large-scale data modernisation programmes, including legacy system integration and cross-functional alignment challenges.
• Approaches for building scalable governance frameworks that balance agility with compliance. • Techniques to measure the impact of data quality on business outcomes, including KPIs for risk, operational efficiency, and customer experience.
• Case studies of organisations that have successfully transformed data foundations to enable AI initiatives.

• How UK FSI organisations are balancing innovation with regulatory scrutiny, including practical examples from retail banking, investment, and insurance sectors.
• Strategies for data leaders to manage AI risk while maintaining progress on digital transformation and AI adoption.
• Emerging governance models, frameworks, and board-level oversight structures that are proving effective in large-scale financial institutions.
• How boards are increasing expectations for accountability, explainability, and risk mitigation in AI deployment.
• Lessons learned from firms that have faced regulatory challenges or compliance audits around AI and data use.

Panelists: Vinod Chawda, AI Governance Chief Strategy & Innovation Office – Deutsche Bank

• Moving from reporting to decision intelligence
• Event-driven architecture in FS
• Real-time fraud and CX improvements
• Operational challenges scaling real-time data
• Where real-time delivers ROI

• Where GenAI and agentic AI are delivering real, measurable value in banking operations today — beyond the pilots
• The infrastructure, data, and governance foundations needed before deploying AI agents at scale
• Managing risk and accountability when AI systems are making or influencing decisions
• Lessons from early implementations, what worked, what failed, and what teams wished they'd known sooner

Panellists: Available

• Comparing approaches: which architecture is delivering tangible results in UK banks?
• Lessons from large-scale implementations: successes, pitfalls, and adoption challenges.
• Organisational readiness considerations: governance, team structure, and culture.
• Technology vs. culture: how to align data architecture strategy with operational and regulatory needs.

Facilitator roles: Available

Roxanne Howdle-Rowe, Managing Director, Group Head of Data & Analytics Office – British Business Bank

• What AI is truly changing in underwriting workflows and where human expertise remains critical.
• Separating hype from measurable transformation: practical examples from insurers.
• Human vs. machine decision frameworks: balancing automation with accountability.
• Areas where AI is delivering quantifiable underwriting value, including speed, accuracy, and risk mitigation.

• How insurers are balancing automation with customer trust and regulatory expectations.
• Areas where analytics is reducing claims leakage and improving operational efficiency.
• Operational adoption challenges: systems integration, staff training, and cultural readiness.
• Ethical considerations in automated claims decisions, including transparency, fairness, and auditability.

• How insurers are restructuring their data functions for modern analytics and AI initiatives.
• Operating model evolution: centralised, federated, or hybrid approaches.
• Balancing legacy systems with modern data platforms to achieve a unified customer view.
• Lessons from Lloyd’s and GI markets on prioritising value, compliance, and innovation.

Three simultaneous roundtables running in parallel. Attendees remain seated at their chosen table throughout. Speakers rotate between tables every 30 minutes ensuring every group hears from every facilitator.

Each table will have a printed prompt card with 3 discussion questions to guide conversation and encourage audience participation.

Facilitator roles: Available

Table 1 — Data Sovereignty & Regulatory Compliance

How are organisations navigating data residency, cross-border data flows, and evolving regulatory requirements without slowing down their data strategy?

Table 2 — Data Governance in the Real World

Beyond frameworks and policies — what does effective, business-led data governance actually look like when you're mid-transformation and under pressure to deliver?

Table 3 — Data Architecture for AI Readiness

Data mesh, data fabric, cloud-native pipelines — which architectural approaches are genuinely moving the needle and what does it take to get there?

• 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.

• Multi-cloud vs. hybrid strategies: aligning architecture with business objectives.
• Sovereign data considerations and regulatory restrictions.
• Managing risk and innovation trade-offs in cloud adoption.
• Lessons learned from successful banking implementations: governance, monitoring, and compliance.

• How parametric products are redefining insurance through data-driven automation.
• Real-time triggers, automated payouts, and event-driven insurance solutions.
• Leveraging new data sources for underwriting, risk assessment, and customer engagement.
• Demonstrating tangible customer experience improvements and operational efficiency gains.

• Navigating IFRS17, Solvency II, and other compliance frameworks effectively.
• Opportunities for automation and reducing manual reporting burden.
• Building robust, auditable reporting pipelines while maintaining agility.
• Preparing for future regulatory outlook and proactive data strategy alignment.

• What long-term strategies are successful businesses adopting to ensure resilience during periods of economic turbulence?  

• How are companies reshaping their approaches to data, governance, and AI to remain adaptable in the face of market volatility?  

• How can businesses leverage data and AI to future-proof their operations against ongoing economic uncertainties?  

• What role do governance frameworks play in helping companies navigate financial instability and ensure sustainable growth? 

Moderator:  Salma Bakouk, CEO & Co-Founder - SIFFLET 

Ishant Aggarwal, Document AI Lead – Lloyds 

Dr Madhura Shivaraju, Senior Data and AI Specialist - UBS 

• Why the CDO operating model is evolving as organisations shift from governance-focused data teams toward value creation, AI enablement, and business alignment.
• The rise of data products and how FS firms are introducing product ownership, SLAs, and internal customer thinking into their data functions.
• Centralised vs federated data ownership and how institutions are balancing domain accountability with enterprise governance and regulatory control.
• How leading firms are funding data teams and moving from cost-centre models toward value-driven investment tied to business outcomes.
• Designing data organisations for AI scale including the skills, structures, and collaboration models needed to support enterprise AI adoption.

• How leading FS organisations are quantifying ROI from data and AI investments.
• Moving from technical KPIs to business impact metrics executives care about.
• Practical frameworks for measuring value across revenue growth, efficiency, and risk reduction.
• Communicating data value in financial terms boards understand.
• Examples of successful monetisation and value-realisation strategies.
• How CDAOs can secure continued investment in difficult budget environments

• Identifying key themes emerging across both tracks.
• Where challenges overlap and where priorities diverge between banking and insurance.
• Lessons and insights for leaders on bridging organisational silos and driving cross-sector learning.
• Opportunities for collaboration and innovation across FS domains.