Unlock the full potential of AI by delving into the power of pristine, governed data that fuels AI with precision and purpose. This dynamic session will captivate you with groundbreaking insights into the synergy between Informatica's cutting-edge data solutions. Experience case studies and tangible demos that reveal how clean, accurate data forms the backbone of responsible AI innovation. Engage with stories of enterprise success, where ethical considerations meet data excellence, driving transformative outcomes.

Join Steve to explore how impeccable data governance ignites a future where AI advancements thrive with trust and integrity.

Steve Holyer

Data Platform and Governance Leader EMEA North
Informatica

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As enterprises adopt large language models (LLMs), ensuring proper validation and governance becomes critical to avoid risks and ensure compliance. Validating LLMs for accuracy, fairness, and accountability is essential to maintain trust and mitigate bias. This session explores the importance of establishing rigorous AI governance frameworks and validation processes to scale generative AI responsibly.

• Establishing clear governance standards to manage risks and ensure regulatory compliance.
• Validating LLMs for accuracy, fairness, and transparency to foster trust in AI systems.
• Implementing continuous monitoring and auditing to maintain responsible AI practices.

Sanja Hukovic

Head of Model and AI Risk Management
London Stock Exchange Group

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As AI capabilities expand exponentially, the imperative for responsible development and deployment becomes paramount for business success and societal well-being. Navigating the complex landscape of responsible AI requires a proactive approach that balances innovation with ethical considerations, ensuring long-term sustainability and trust in AI-driven enterprises.

•      Establishing cross-functional ethical AI committees to guide development and implementation decisions

•      Implementing robust AI governance frameworks that prioritize transparency, fairness, and accountability

•      Investing in continuous education and upskilling programs focused on responsible AI practices

Hatim Abdulhussein

Chief Executive Officer
Health Innovation Kent Surrey Sussex

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Barry Smith

Managing Partner
Smith & Guinan Ltd.

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James Fletcher

Responsible AI Lead
BBC

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Hellena Crompton

Data Protection Officer UK&I
dentsu international

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In an era where artificial intelligence is transforming industries, organizations must adapt their infrastructure to leverage AI's full potential. This session explores why a robust AI infrastructure strategy is essential for businesses to remain competitive and innovative. From scalability to integration, we’ll discuss the key components of AI infrastructure, common challenges, and how aligning infrastructure tightly with AI goals can drive efficiency, improve decision-making, and create new business opportunities. Attendees will leave with a clear understanding of why AI infrastructure is not just an IT necessity but a strategic enabler of growth.

Rick Moore

Global Head of Innovation
Digital Realty

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Delivering real business value from AI requires a synchronized approach across four key dimensions: infrastructure, data, code, and models. Each area involves different stakeholders with distinct priorities, creating friction when moving from lab experiments to live production. This session explores how to align these dimensions consistently throughout the AI lifecycle—ensuring smoother transitions from exploration to deployment.

• Aligning infrastructure, data, code, and models across the AI lifecycle for scalability.
• Breaking down silos by fostering collaboration between technical and business stakeholders.
• Implementing continuous validation to ensure AI delivers consistent business impact.

Amit Nandi

VP Solutions & Data Architect
Barclays Corporate & Investment Bank

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As enterprises scale their generative AI initiatives, choosing the right infrastructure strategy becomes crucial for maximizing performance, cost-efficiency, and data control. The evolving landscape of AI models, including the trend towards smaller, more efficient architectures, presents new opportunities and challenges in infrastructure decisions that can significantly impact an organization's AI success and competitive edge.

•      Conducting comprehensive TCO analyses comparing on-prem, cloud, and hybrid infrastructure options

•      Exploring smaller, domain-specific AI models to reduce computational demands and costs

•      Implementing flexible infrastructure strategies that adapt to evolving AI model architectures

Dara L. Sosulski

Managing Director, Head of Artificial Intelligence and Model Management
HSBC

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Join us for a 30-minute fireside chat featuring Graham Taylor, Senior Director & Head of Innovation at Capgemini, and Bobby Hyam, Senior Solutions Engineer at Glean. This session will explore current marketplace trends, the future of AI in business, and practical tips on scaling and operationalising Generative AI. The discussion aims to provide IT leaders with valuable perspectives and actionable strategies to harness AI for enhancing business operations and achieving strategic goals.

Bobby Hyam

Senior Solutions Engineer
Glean

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Graham Taylor

Senior Director & Head of Innovation
Capgemini

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This session will provide insights into the critical decisions and frameworks guiding the enterprise’s successful implementation of generative AI initiatives, emphasizing how to focus on selecting solutions that address meaningful business challenges.

• Developing a strategic mindset for identifying the readiness for AI Transformation.
• Overcoming the “solutions in search of a problem” dilemma by identifying and addressing core business needs.
• Framing the right questions to guide decision-making during complex transformation journeys.

Ravi Jay

Head of Agile Delivery
Jaguar Land Rover

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In the rapidly evolving landscape of generative AI, a value-led approach to implementation is crucial for achieving tangible business outcomes and sustainable competitive advantage. By aligning GenAI initiatives with strategic objectives and focusing on measurable impact, organizations can navigate the complexities of AI adoption and drive meaningful business transformation.

Delve into:

•      Developing a comprehensive value hypothesis to guide GenAI use case prioritization

•      Implementing iterative, agile methodologies for rapid prototyping and scaling of AI solutions

•      Establishing robust measurement frameworks to track ROI and continuously refine implementation strategies

Magnus Magnusson

Head of Advanced Analytics
Autoliv

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Dario Morelli

Head of Advanced Analytics & Artificial Intelligence (EU D&A)
Admiral Group

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Chris Darriet-Jones

Chief Data Architect
Post Office

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Cristina Gil-Sevilla

Head of Client Data Science
Schroders

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Haleon has embarked on a transformative GenAI journey, integrating Generative AI across its R&D and enterprise functions to drive innovation and enhance operational efficiency. From building specific GenAI capabilities to navigating compliance and regulatory challenges, Haleon’s experience offers valuable insights for any organization looking to scale GenAI initiatives. This talk will cover Haleon’s biggest GenAI use cases, their successes and struggles, highlighting what business leaders need to consider as they integrate GenAI into their strategy and product roadmaps.

• Aligning GenAI capabilities with enterprise strategy and ensure data-driven product innovation.

• Navigating compliance, privacy concerns, and ethical implications in GenAI applications.

• Fostering collaboration between data teams and product management for scalable, effective AI deployment.

Atif Ishaq

Head of R+D Data & Enterprise Generative AI
Haleon

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NatWest’s Cora+ chatbot has undergone significant evolution, transitioning from Cora to Cora+. This journey has been marked by milestones, customer feedback-driven enhancements, and challenges in implementing new initiatives such as generative AI.

We have prioritized daily monitoring to limit potential chatbot errors and performance issues and ensure our customers receive the best possible outcome.

We are also continuing to iteratively innovate to provide the best possible experience for our customers whilst using new and emerging technologies. Attend this talk to delve into:

• Understanding Cora to Cora+: A Brief History
• Transforming Customer Interactions with Generative AI Implementing Daily Monitoring
• Exploring Next Steps for Cora+

Philip Ector

AI Experience Manager
NatWest Group

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As generative AI moves beyond the initial excitement, organizations face the critical challenge of integrating these technologies into their operational fabric. This session explores the strategies and best practices for scaling GenAI initiatives while navigating regulatory hurdles, managing risks, and ensuring authentic, value-driven outcomes.

Business leaders can effectively operationalize GenAI by:

•      Implementing robust governance frameworks to ensure ethical and compliant AI use

•      Developing full-stack content solutions that integrate GenAI into existing workflows

•      Balancing automation with human oversight to maintain brand authenticity and mitigate

Paul Cardno

Global Digital Automation & Innovation Senior Manager
3M

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Modern AI demands a platform that seamlessly integrates retrieval, ranking, and generation while maintaining operational efficiency at scale. Vespa.ai presents an advanced AI engine that unifies complex AI workflows, enabling high-performance retrieval-augmented generation (RAG), optimized ranking, and real-time AI-driven search. This session will explore how organizations can leverage state-of-the-art infrastructure to enhance data operations, deliver precise results, and dramatically shorten production cycles.

• Unified AI Architecture: Design an end-to-end platform combining indexing, vector search, hybrid retrieval, and scalable AI inference.
• Implementing optimized RAG strategies to enhance response accuracy while maintaining low latency.
• Leveraging AI-driven ranking models for flexible, high-precision query resolution, including visual search.
• Optimizing data operations, ensuring real-time data ingestion, processing, and governance to maintain high-quality AI outputs.
• Deploying a customizable, high-performance AI system for enterprise search, document digitization, and personalized AI applications.

Piotr Kobziakowski

Senior Principal Solutions Architect
Vespa.ai

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BT’s journey in delivering a robust Generative AI platform has revolutionized how they approach data science, natural language processing (NLP), and AI-driven decision-making. This platform enables faster time-to-market, cost reduction, and seamless integration, empowering teams to develop and deploy AI agents at scale. In this session, explore how BT's GenAI platform is setting new standards for enterprise AI deployment and delivering on next-generation AI capabilities.

•      Leveraging a multitenancy platform to accelerate AI delivery and reduce costs.

•      Building and deploy NLP-driven agents across enterprise use cases to enhance decision-making.

•      Integrating AI with existing infrastructure for smoother workflows and faster innovation.

Akshita Gupta

Senior Manager Generative AI
BT

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As generative AI becomes increasingly integral to business operations, it introduces novel security challenges that demand immediate attention and innovative solutions. Addressing these security concerns is crucial for safeguarding sensitive data, maintaining regulatory compliance, and ensuring the responsible use of AI within enterprise environments, ultimately protecting both business interests and stakeholder trust.

•      Implementing robust prompt security measures to prevent injection attacks and data leaks

•      Developing AI-specific data loss prevention strategies integrated with existing enterprise security infrastructure

•      Establishing comprehensive monitoring and control systems for GenAI usage and output

Ray Ellis

Head of AI Security
Philip Morris International

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Mike Hales

Digital Product Owner - Conversational AI
EE

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David Stevenson

Chief Data Scientist
Leonardo

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The way humans interact and collaborate with AI is taking a dramatic leap forward with agentic AI. AI agents are the new thing. What are they? How does GenAI lower the barriers for organisations to get value from AI without risking your data, processes, governance and culture? We will share an overview of:

• How AI agents work versus AI, ML and RPA
• How to identify valuable workflows/business processes for agentic AI reinvention and innovation.
• How to choose between open source and proprietary technologies
• How to prepare your organization for absorbing the impact of agentic workflows.
• Share demos from agents we have built to solve real business problems. And solutions Reply has built to encourage sustainable adoption of agents across our 15,000 people. 

Ian Spatz

Partner
Data Reply

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Dimitris Vlitas

Partner, Head of AI
Data Reply

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As generative AI evolves, agentic systems and fine-tuning techniques are emerging as powerful tools for creating task-oriented models that deliver precise, controllable outcomes. This panel explores cutting-edge research and practical applications of these technologies, offering insights into how enterprises can leverage them to create more efficient, targeted AI solutions.

Business leaders can harness agentic systems and fine-tuning by:

•      Investing in research partnerships to stay at the forefront of AI advancements

•      Identifying specific tasks where fine-tuned models can deliver significant efficiency gains

•      Developing strategies to balance model control with adaptability in enterprise applications

Detlef Nauck

Distinguished Engineer and Head of AI & Data Science Research
BT

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AI agents represent a paradigm shift in how businesses interact with and leverage artificial intelligence, offering unprecedented levels of autonomy and task completion capabilities. As these intelligent agents evolve to handle complex workflows and decision-making processes, understanding their potential and implementing effective user interfaces becomes crucial for organizations seeking to maximize efficiency, innovation, and competitive advantage.

This panel discussion will delve into:

•      Identifying high-impact use cases for AI agents across various business functions

•      Investing in developing intuitive, user-friendly interfaces that facilitate human-AI agent collaboration

•      Implementing robust governance frameworks to ensure responsible and ethical AI agent deployment

Adriana Castro

Global VP Digital Transformation
The Adecco Group

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Emma Irwin

Solutions Engineering
Dataiku

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