Conference Day One - Tuesday 1st April 2025
This agenda is subject to change.
Generative AI is transforming industries, and board-level insight is critical for balancing innovation, value, and risk. This panel discussion explores how boards are evolving their approaches to AI, driving value creation while maintaining governance. Attendees will learn strategic frameworks that support sustainable AI adoption across enterprises.
· Aligning AI initiatives with long-term business goals to maximize value.
· Evaluating the cost-benefit balance of AI investments to ensure sustainability.
· Developing risk management frameworks that address ethical, financial, and operational AI risks.
Implementing Generative AI that directly impact customer processes is a significant challenge for enterprises, especially in highly regulated industries like banking and financial services. Navigating regulation, risk, and the demand for explainability makes scaling GenAI complex but essential for delivering customer-centric innovation. This session explores how Lloyds is navigating successfully bringing GenAI use cases to life within customer-facing operations.
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.
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
Siemens’ journey in building a large-scale, composable AI foundation platform showcases how strategic, centralized innovation can drive enterprise-wide transformation. This session dives into how Siemens created a cost-effective, scalable platform through a unique crowdfunding approach, empowering over 70 use cases across its Digital Industries business.
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.
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
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
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
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
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
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.
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:
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
As generative AI reshapes the business landscape, ensuring widespread AI literacy and access within organizations is crucial for driving innovation and maintaining competitive edge. By empowering employees across all levels with AI knowledge and tools, companies can unlock new realms of creativity, efficiency, and problem-solving capabilities, leading to transformative outcomes and a future-ready workforce.
• Implementing comprehensive AI literacy programs tailored to different roles and departments
• Developing user-friendly AI platforms and interfaces that encourage widespread adoption and experimentation
• Establishing clear governance frameworks that balance accessibility with responsible AI use
As organizations move beyond proof-of-concepts, the challenge shifts to implementing generative AI solutions that deliver tangible value across the enterprise. This panel explores strategies for scaling GenAI initiatives, from building robust frameworks to managing costs and ensuring continuous improvement in a rapidly evolving technological landscape. Learn how business leaders are effectively operationalizing value-adding GenAI by:
• Developing enterprise-wide frameworks that accelerate deployment and ensure consistency
• Implementing cost-effective strategies for model fine-tuning and scalable infrastructure
• Fostering a culture of continuous improvement and shared capabilities across teams