Conference Day One - Tuesday 14th April

At Citi, GenAI is a core enabler of transformation, not a side project. An advanced digital assistant is already boosting operational efficiency by 25%, supported by modernised infrastructure and workforce adoption.

• Delivering 25% uplift in financial accounting efficiency

• Modernising infrastructure for scalable GenAI solutions

• Upskilling teams to enable enterprise-wide adoption

Ramy Erfan

Vice President, Business & Technology Transformation
Citi

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With dozens of AI agents built across BP’s functions - from data retrieval to task orchestration - the challenge isn’t whether the technology works, it’s how to make it usable, safe and enterprise-ready. Natalia leads work on NatWest's internal Multi-Agent Control Platform (MCP) and emerging agent-to-agent (A2A) protocols, helping unify fragmented agent systems into cohesive, business-ready workflows. This session shares lessons on scaling adoption, governance, and data integration.

• Designing agent-to-agent systems for secure, predictable enterprise workflows at scale

• Solving for adoption: architecture, data readiness, and investment in business-side change

• Building practical guardrails and evaluation methods for real-world agentic reliability

Natalia Konstantinova

Lead Enterprise Architect in AI
NatWest

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• Designing deterministic agent flows with explicit logic, guardrails, and structured handoffs
• Embedding human approvals and escalation paths directly into high-impact workflows
• Validating agent performance continuously using repeatable evaluation and SME review

Nick Jewell

Senior Solutions Engineer
Dataiku

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Zurich is deploying agentic analytics across global units, from claims tools to enterprise hackathons. With use cases already in production, this session explores how trust and validation underpin adoption.

• Deploying agentic analytics for global claims performance

• Running hackathons to identify enterprise use cases

• Building trust through validation and human-in-the-loop design

Madhur Hemnani

Head of Data Strategy and Data Governance
Zurich Insurance

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As organisations push to deploy increasingly powerful models, many are discovering a fundamental limitation: intelligence without context leads to poor decisions at scale. From disconnected data to shallow prompts, the absence of context is often what turns promising AI initiatives into noise rather than value. Unlocking meaningful outcomes requires more than smarter models - it demands a deeper understanding of how context is captured, structured, and applied across systems and workflows. Attend this session to delve into:

• Identifying where lack of context is limiting AI performance and decision quality
• Structuring data, prompts, and workflows to deliver more relevant, grounded outputs
• Moving beyond model capability to design systems that embed context at every layer

David Fearne

VP of AI
NTT Data

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As the financial services sector edges into the world of agentic AI, success won’t come from speed alone - it will come from building responsibly. At Schroders, Konstantina is helping lead the charge to ensure AI systems are grounded in strong data governance, trust, transparency and human-centric design. This session shares case studies on preparing for the next wave of adoption.

• Embedding agentic AI into enterprise systems while protecting privacy and governance by design

• Aligning AI literacy, change management and regulatory readiness across a global organisation

• Building trust by co-creating transparent, human-in-the-loop systems - no black boxes allowed

Konstantina Kapetanidi

VP, Global Data Solutions & Head of Data Science, Europe
Visa

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Agentic AI promises productivity gains but comes with significant costs. This panel unpacks the economics of scaling autonomy and how to build ROI strategies that work.

• Building the business case for agentic adoption

• Managing compute, storage, and operational costs

• Unlocking value through orchestration and cross-functional scale

Robert Chilvers

VP Data & AI
dLocal

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Detlef Nauck

Distinguished Engineer and Head of AI & Data Science Research
BT

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Laura Scavino

AI Director
NTT Data

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Natasha Sarin

Lead Product Owner - Natural Language Engineering & Generative AI
Lloyds Banking Group

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Assessing and optimizing the return on investment (ROI) for Generative AI projects is paramount as businesses increasingly invest in this transformative technology.

-       Learning to measure the tangible and intangible ROI factors to justify and enhance generative AI projects.

-       Discovering strategies for aligning Generative AI goals with business objectives and market demands.

-       Gaining insights into continuous monitoring, model refinement, and adaptation to maximize Generative AI project ROI. 

Charlotte Bemand

Director of Digital Futures
HBK

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JLL has transformed millions of property and lease data points into scalable AI solutions. This session shows how robust foundations drive ROI and operational safety in real estate.

• Building reliable data foundations for AI adoption

• Turning insights into savings, safety, and better decisions

• Embedding governance and security for trusted innovation

Will Cohen

EMEA Exec Director of Technology
JLL

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AI has never been more central to enterprise strategy — yet most organisations struggle to translate pilots into production outcomes. On average, companies run multiple AI pilots, but only a small fraction deliver measurable business value, and scaling from prototype to production can take months. Despite strong strategic intent, gaps in infrastructure, data access, and connectivity continue to limit ROI.

In this session, we will explore:

• Infrastructure bottlenecks: why many AI pilots fail to scale due to constraints like GPU capacity, hybrid cloud complexity, and security challenges
• Bridging the gap: optimising data location, performance, and connectivity for operational AI
• Scaling AI: building high-performance, secure, interconnected foundations to accelerate time-to-value

Darren Chan

Senior Solutions Architect
Digital Realty

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With diverse AI tools proliferating, enterprises face platform sprawl. Charlotte at HBK explores how to create value in a mixed AI economy without losing agility or control.

• Selecting AI tools based on business outcomes

• Ensuring interoperability to avoid lock-in risks

• Balancing innovation with sustainable pricing models

Charlotte Bemand

Director of Digital Futures
HBK

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Agentic systems need clean, interoperable data to operate safely. This panel explores how enterprises are adapting architectures and governance to support autonomous decision-making.

• Building data architectures for multi-agent systems

• Enabling real-time orchestration and interoperability

• Embedding governance for safe and auditable operations

Charlotte Bemand

Director of Digital Futures
HBK

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German Faraoni Heidenreich

Global Director, IT & Digital
Reckitt

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Mattia de Dominicis

SVP, Head of R&D and Innovation
Imperial Brands

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Chris Lowe

Head of Solution Consulting EMEA
Reltio

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At BMW, every car has a story - from production to end-of-life. Ahmed leads efforts to capture and orchestrate that story through modular digital twins, designed to power internal use cases across compliance, automation, battery tracking, and real-time monitoring. From a proprietary digital twin platform that integrates AI workflows, to use cases spanning regulatory readiness and software update diagnostics, this session explores how BMW is operationalising its data infrastructure - turning every vehicle into a living, evolving data product.

• Building scalable digital twin architecture from production to post-lifecycle automation

• Embedding AI across quality checks, compliance, battery tracking and software monitoring

• Designing modular data products for internal teams to self-serve and scale use cases

Ahmed Ebada

Senior AI Product Manager
BMW Group

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HSBC is building AI architectures that scale across people, platforms, and processes. This session explores how to balance quick wins with long-term adaptability.

• Leveraging people, platforms, and processes to drive scale

• Designing AI with re-useability and open standards

• Aligning architectures with business and regulatory needs

Neil Thewlis

Head of AI and Model Transformation
HSBC

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The AA has been steadily building its capabilities in generative and agentic AI, testing use cases across insurance, claims, HR, finance, and customer operations. Alongside this, a Centre of Excellence has been established to provide governance, share learnings, and guide adoption. This session highlights how learning and governance are driving scale.

• Exploring early GenAI and agentic AI use cases across core business functions

• Establishing a Centre of Excellence to coordinate adoption and drive best practice

• Capturing lessons learned to prepare for enterprise-wide scale-up next year

Leave with practical insights into how the AA is turning experimentation into enterprise-scale impact.

Raghava Krishna

Technology Director – Digital & AI
The AA

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AI isn’t just transforming engineering processes - it’s reshaping how teams, leaders, and organisations operate. For large multinationals, the real challenge is less about connecting to an API and more about equipping people to work alongside intelligent systems in meaningful, sustainable ways. In this keynote, Irina shares lessons from Fugro and other highly regulated industries on what it takes to lead responsibly in the age of AI.

• Upskilling workers to collaborate effectively with AI systems, not compete against them

• Helping senior leaders make informed AI decisions beyond hype and headlines

• Building human governance structures that complement technical and regulatory controls

Irina von Rosén

Founder and Managing Partner
WM Global Capital

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As GenAI becomes more embedded in financial services, the challenge is not only enabling innovation but doing so responsibly at scale with trust, transparency, and strong governance. Sibel, from Mastercard’s AI Governance team, supports this work by contributing to evaluation frameworks, governance guidance, and guardrails that help teams experiment safely across the enterprise.

Sibel works on model evaluations, governance checks, and documentation that help teams understand what is required to use AI responsibly. She also supports efforts to ensure new AI use cases align with Mastercard’s standards, allowing teams to explore new capabilities without compromising trust or security.

Her current focus includes:

- Strengthening Mastercard’s AI governance processes and evaluation standards

- Helping teams explore new AI capabilities in a responsible and controlled way

- Contributing to tooling and frameworks that improve consistency across AI reviews

Sibel B Sonuc

Manager, AI Governance
Mastercard

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