Agenda Day 1
CDO BFSI Agenda Day 1
8:00 am - 8:30 am
REGISTRATION & COFFEE
8:30 am - 8:35 am
IQPC WELCOME
8:35 am - 8:45 am
OPENING REMARKS FROM THE CHAIRPERSON
8:45 am - 8:50 am
LIVE POLLS
8:50 am - 9:35 am
PANEL DISCUSSION: THE ROI REALITY CHECK - WHAT’S ACTUALLY DRIVING VALUE?
Sara Robaty Shirzad -
Head of Data,
Flood Re
Karthik Srinivas - Head of Data and AI Governance, MS Amlin
Pressure is intensifying for data leaders across financial services to prove that AI is delivering real, measurable business impact not just technical progress. Many organisations still struggle to translate AI capabilities into tangible commercial outcomes at speed. The question on everyone's mind is how do you demonstrate short-term ROI from AI to secure sustained investment and support?
- Explore how data leaders are building frameworks to quantify the commercial value of data and AI, from individual use cases to the contribution of the data function as a whole.
- Learn how to move beyond technical metrics and communicate outcomes in terms that resonate at board level, whether the conversation is about AI investment, data infrastructure, or team ROI.
- Discover how to prioritise high-value opportunities and make the case for the foundational work that rarely gets the credit it deserves.
9:35 am - 10:05 am
PRESENTATION: FROM TRUSTED DATA TO TRANSFORMATIVE OUTCOMES - HOW BFSI LEADERS ARE REDEFINING GROWTH, SERVICE, AND RISK
Chris Lowe -
Senior Solutions Consultant,
Reltio
Banking, financial services, and insurance organizations are entering a new era where trusted, real time data is essential to growth, customer experience, operational resilience, and risk management. Hear how leading BFSI institutions are modernizing their data foundations in ways that have strong tangible impacts on the business.
- Explore ways to create unified customer, account, household, policy, and relationship views that power measurable business outcomes.
- Learn how better data can improve sales and service productivity, strengthen first call resolution, enhance predictive analytics, reduce manual effort, and unlock innovation across the enterprise.
- Discover why trusted data is now a strategic advantage for institutions competing in a digital first financial ecosystem.
10:05 am - 10:20 am
NETWORKING BINGO
10:25 am - 10:55 am THINK TANK: FIX THE FOUNDATION OR FAIL - BREAKING CORE BANKING SILOS AND CREATING TRUSTED DATA TO FUEL AUTONOMOUS AI AGENTS
Steve Holyer - Data Platform Leader, SalesforceIn the modern world, GenAI just generates text, but Agentic AI actually takes actions on behalf of the customer (e.g., transferring funds, updating records, opening accounts). If a generative chatbot gets a name wrong, it's embarrassing; if an AI Agent triggers a transaction on the wrong "Elizabeth" because of bad integration or bad data, it's a massive regulatory and financial disaster.
- Discover how AI for banking workflows can elevate data quality from a tech issue to a top-tier risk priority.
- Discuss how legacy core banking silos split a single customer's identity across multiple systems, creating high-risk blind spots for AI.
- Explore how to deploy a unified data management layer to clean, match, and govern customer data in real time, ensuring AI agents always act on accurate, verified information.
10:25 am - 10:55 am 1:1 BUSINESS MEETINGS
11:00 am - 11:30 am COFFEE BREAK & NETWORKING
11:00 am - 11:30 am 1:1 BUSINESS MEETINGS
11:35 am - 12:05 pm THINK TANK: THE COST OF GETTING AI WRONG - LESSONS FROM THE HIGHEST-STAKES ENVIRONMENTS IN BFSI
AI accountability looks very different depending on where you sit. For many data leaders, a model failure means a missed opportunity or a poor customer experience. In the highest-stakes environments in financial services, it means regulatory enforcement, control failures, and reputational damage. Yet the underlying data and governance challenges are the same across the sector: how do you deploy AI that is aggressive enough to be useful, auditable enough to be defensible, and grounded in data quality robust enough to hold up under scrutiny? This think tank draws on experience from the most demanding AI governance environments in BFSI to ask what the rest of the sector can learn from operating under maximum regulatory pressure.
- Examine what responsible AI deployment actually has to mean when the downstream consequence of failure is regulatory action, and how that standard applies across other high-stakes use cases.
- Debate how data quality and lineage requirements change when AI outputs need to be auditable, explainable and defensible, not just accurate.
- Explore how to build the human oversight and control frameworks that allow AI to move from rules-based systems into production in compliance-driven environments.
- Assess what operating AI at the highest governance pressure reveals about the data foundations the rest of the sector needs to get right before AI can scale reliably.
11:35 am - 12:05 pm 1:1 BUSINESS MEETINGS
12:10 pm - 12:40 pm
FIRESIDE CHAT: REVITALISING THE ENTERPRISE DATA FUNCTION - FROM FOUNDATIONS TO COMMERCIAL IMPACT
Mark Bird -
Group Chief Data Officer,
Legal & General
As financial services organisations invest in growth, AI capability and wider transformation programmes, senior data leaders face a consistent challenge: how do you use that moment of ambition to genuinely fix the foundations, rather than build on top of them? For those leading data at the enterprise level, that means grappling with questions that rarely have clean answers; aligning functions across business lines, making the commercial case for foundational investment, and building the governance and management practices that make change actually stick. Themes to be discussed include:
- What revitalising an enterprise data function actually means in practice and what you find when you look under the bonnet.
- Getting data leaders across business lines onto the same page: the organisational and cultural reality.
- Making the commercial case for data: translating foundational investment into language the board responds to.
- Governance and data management as enablers, not obstacles and how you shift that narrative internally.
- Using a transformation or growth agenda as leverage to do the data work that might otherwise get deprioritised.
- What it takes to make transformation genuinely stick, and what gets in the way.
12:40 pm - 1:40 pm
LUNCH
1:40 pm - 2:10 pm THINK TANK: THE ENVIRONMENTAL COST OF AI - WHO IS ACCOUNTING FOR THE IMPACT?
Shani Reynolds - Head of Data Management, First Sentier Group
AI's environmental footprint is growing- but remains poorly measured. A single generative AI query can use up to 5 times more energy than a traditional search, with AI projected to add up to 44 million metric tons of CO2 annually by 2030. With many organisations pursuing sustainability targets, the lack of clear visibility or consistent standards to measure AI's impact could soon be a problem.
- Share your current approaches to understanding and measuring the environmental footprint of AI.
- Debate the role of data leaders in balancing AI-driven innovation with sustainability obligations.
- Weigh in on practical strategies to reduce AI's environmental impact.
1:40 pm - 2:10 pm 1:1 BUSINESS MEETINGS
2:15 pm - 2:20 pm
LIVE POLLS
2:20 pm - 2:50 pm
PRESENTATION: THE INTELLIGENCE UNDERNEATH - BUILDING DATA ARCHITECTURES THAT MAKE AI SMARTER, NOT JUST FASTER
Pablo K. -
Head of Data Enablement,
Schroders
AI is already reshaping how data work gets done, but many organisations are applying it in one direction: using AI on top of data, rather than within it. The question is not only whether your data is ready for AI, but how you can use AI intelligently within the data function itself. The cost of scaling data products is not primarily a technology problem, it is a design problem, and organisations that build around what data they have rather than the problems they need to solve create architectures that are expensive, slow, and brittle under AI-driven demand. Leading data functions are inverting this model: shifting to demand-driven product design, treating data as modular reusable components benchmarked against measurable business outcomes, and applying AI selectively to compress cycle time where it adds genuine value.
- Understand why data architectures built around supply rather than demand compound cost and slow cycle time as AI scales, and what demand-driven design does differently.
- Learn how to benchmark data product success through reusability and commercial impact rather than delivery volume.
- Discover how AI applied selectively within the data function compresses cycle time without industrialising noise.
2:50 pm - 3:20 pm
PRESENTATION: SCALING YOUR DATA STACK- BALANCING INNOVATION, DEMAND AND COST
As financial organisations continue to strengthen data quality across cloud platforms, managing the cost and complexity of evolving data tools becomes increasingly challenging. data leaders must balance the demand for modern capabilities with budget constraints and growing technology stacks. understand how you can prioritise the right upgrades while maintaining efficiency.
- Find out how you can prioritise high-impact data investments while ensuring upgrades deliver measurable business value.
- Discover strategies to streamline data quality across cloud environments without slowing innovation.
- Understand how data leaders are building scalable architectures that support long-term growth while remaining cost-efficient and flexible.
3:15 pm - 3:45 pm THINK TANK: LEADING IN THE MACHINE AGE - WHAT’S NEXT FOR DATA LEADERS?
Helen Packard - Head of Market Oversight Data & Intelligence, Financial Conduct Authority
Many tasks once owned by data professionals can now be automated or augmented by AI - meaning leadership itself is evolving. This raises a critical question: how must data leaders evolve to stay relevant and drive value? The need for strategic direction and business alignment is only increasing.
- Challenge assumptions, share perspectives and explore how AI is reshaping data leadership and what this means for the future of CDO's.
- Debate how AI is transforming the role of data leaders.
- Discuss how skill requirements are evolving in an AI-driven environment and how to keep pace.
- Weigh in on what data leaders should be doing now to future-proof teams, roles and career paths.
3:15 pm - 3:45 pm 1:1 BUSINESS MEETINGS
3:50 pm - 4:20 pm COFFEE BREAK & NETWORKING
3:50 pm - 4:20 pm 1:1 BUSINESS MEETINGS
4:25 pm - 4:55 pm THINK TANK: FROM JARGON TO FLUENCY - BUILDING THE PSYCHOLOGICAL SAFETY THAT MAKES DATA CULTURE ACTUALLY STICK
Data strategies fail not because of technology, but because of people. Across financial services, data leaders are investing in platforms and governance frameworks while the human side of transformation (how people feel about data, whether they trust it, whether they feel safe engaging with it) is left to chance. Shifting that requires more than communication plans. It requires leaders who understand how they show up, how they build environments where curiosity is encouraged and questions are welcomed, and how to translate complexity into language that every part of the business can act on.
- Share approaches to building data literacy across large, complex organisations without overwhelming the business
- Examine how the way data leaders show up and communicate shapes the culture around them
- Discuss what peer organisations are doing to move from data awareness to genuine data confidence
4:25 pm - 4:55 pm 1:1 BUSINESS MEETINGS
5:00 pm - 5:30 pm
PANEL DISCUSSION: THE TALENT CRISIS IN DATA - CAN YOU HIRE YOUR WAY OUT OF THIS?
Shani Reynolds -
Head of Data Management,
First Sentier Group
Luisa Pires - Head and Founder of the Analytics Enablement Hub, Bank of England
The rules of hiring in data and analytics are being rewritten- organisations now need talent who can connect data, AI and real business outcomes. With AI tools generating polished answers in seconds, distinguishing genuine expertise from surface-level knowledge is becoming increasingly difficult. At the same time, the pathway for developing junior talent is narrowing, as entry-level tasks are automated this creates long-term risks for workforce sustainability. This creates a new leadership imperative- building credible, future-ready teams in a market where roles, skills and expectations are constantly evolving.
- Understand how roles are evolving and why business fluency is becoming as critical as technical skill.
- Learn how to assess true capability in the age of AI, identifying talent that can apply judgement and commercial thinking.
- Explore strategies to close the junior talent gap while attracting and retaining high-performing individuals to build future-ready teams.
