Designing scalable AI frameworks that emphasize fairness, transparency, and ethical decision-making in real-time applications.

Implementing robust training and monitoring processes to align AI models with organisational values and principles.

Tackling enterprise-wide challenges in building and scaling responsible AI, from technical hurdles to cultural and organisational shifts.

Supporting the adoption of responsible AI practices across industries.

Exploring the future of responsible AI in fostering trust and innovation.

Slot Reserved for Sponsor Partner

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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Luna Song, a Technology Risk Director at Morgan Stanley, will discuss why it is critical to prepare your enterprise for successful AI integration by first establishing the right internal structure. Drawing on her experience that spans several global markets, Luna will outline how they are navigating the risks of Generative AI and where they are finding success in adoption. She’ll share real-world use cases to highlight both the potential and the risks of AI. Attend to dive deeper into:

•      Identifying why a well-established internal company structure is crucial for effective AI integration.

•      Discovering how to balance AI automation with human oversight, to mitigate risks.

•      Unlocking insights into AI implementation within global markets, such as China.

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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Carol Wilson

Head of Data Ethics, Privacy and Records
Santander UK

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• Tools and techniques for model selection, training, and customisation, including foundation models, fine-tuned models, and centralised repositories to streamline AI development.
• How platform engineering for MLOps enhances the orchestration of AI models, focusing on adapting, deploying, and monitoring models effectively within end-user applications.
• Deploying and Orchestrating AI/ML Models: How to effectively deploy, distribute, and orchestrate AI/ML models to ensure your solution is accurate, reliable, and scalable.
• The importance of a robust infrastructure layer, including cloud platforms and specialized hardware (GPUs, TPUs), to support the intensive training and inference workloads of generative AI models.

Slot Reserved for Sponsor Partner

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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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 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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In today’s evolving work landscape, companies are constantly seeking ways to enhance productivity and operational efficiency. Generative AI promises to bring more efficiency to our work, but integrating it into the enterprise can be challenging. In this session, we will explore how to make generative AI work in your enterprise in a scalable and secure manner, reflecting the breadth and depth of your company’s knowledge. By harnessing the power of AI, companies can unlock new levels of efficiency and innovation across all employees. Join us to learn key lessons on securely deploying impactful generative AI solutions to unlock seamless collaboration, communication, and productivity.

Session Lead By Glean

As Retrieval Augmented Generation (RAG) evolves, genetic frameworks are emerging as the next frontier in AI, promising enhanced reasoning capabilities and more robust decision-making processes. These advanced systems have the potential to revolutionize enterprise AI applications by combining the strengths of RAG with evolutionary algorithms, leading to more adaptive and contextually aware AI solutions. Delve into:

•      Investing in research and development of genetic AI frameworks to stay ahead

•      Implementing pilot projects to explore genetic AI's potential in specific business domains

Developing cross-functional teams to integrate genetic AI with existing RAG implementations

Wayne Filin-Matthews

Global Head of Enterprise Architecture
AstraZeneca

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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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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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As generative AI rapidly transforms 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.

•      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

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

John Almasan

Senior Managing Director/Head of AI and Emerging Tech
TIAA

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Ray Ellis

Head of AI Security
Philip Morris International

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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 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

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

Arun Nandi

Head of Data & Analytics
Unilever

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Adriana Castro

Global VP Digital Transformation
The Adecco Group

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