Niresh Rajah, Chief Data & AI Officer, Chair – UK AI for Financial Services, Fellow at Imperial College and Associate Fellow at Oxford University 

• The UK Government's AI Action Plan and its implications for business
• The US-UK Tech Prosperity Deal in practice: shared compute, large-scale datasets and collaborative research programmes
• The AI Safety Institute's evolving role and what pro-innovation regulation looks like
• International alignment: EU AI Act, US Executive Orders, and what UK firms need to know
• Accessing Government-backed AI programmes, accelerators, and funding streams

• Defining agentic AI: what distinguishes it from traditional and generative AI
• From chatbots and co-pilots to fully autonomous agents: the evolution of capability
• Strategic opportunities: customer service, sales automation, compliance, financial forecasting and personalisation at scale
• Risk landscape: where autonomous decision-making creates legal, operational and reputational liability
• Building the board-level business case: ROI frameworks and the metrics that matter

Sapan Dogra, Managing Director, Data & AI, Accenture 

• The competitive imperative: why UK businesses must act now or risk falling behind
• Current adoption rates and how the UK compares to the US, EU and China
• The productivity dividend: measurable gains from early adopters across key sectors
• The US-UK Tech Prosperity Deal: what the Anthropic, Nscale, OpenAI, Microsoft and NVIDIA investments unlock for British firms
• From pilot to production: setting the ambition for the day ahead

Panellists 

Emily Campbell-Ratcliffe, Head of AI for Local Growth, Department for Science, Innovation and Technology

Anthony Hills, Regional Director, UK&I, Enterprise and Public Sector, Nvidia

This session will provide actionable insights for C-Suite executives covering

• Effective change management and internal communication
• Technical and operational realities of deployment, including infrastructure readiness, integration with legacy systems 
• Other specific challenges of orchestrating multi-agent systems

AI Disruption Use Case I – Banking, rewired for the age of AI, with a real-time agentic balance sheet

Project Atlas is on a mission to make banking instant and hyper-personal – customers should not have to wait days or weeks for generic, off-the shelf products. Delivering that requires re-wiring a bank from the inside out using frontier AI technology. A bank, in its simplest form, is a balance sheet, recycling deposits into loans. Today that work is done by slow, siloed, human committees meeting at best weekly, at worst annually. Our balance sheet agent, Aurora, collapses these processes into a single, continuous AI engine. Aurora is based on advanced deep reinforcement leaning enabling our bank to price deposits, allocate capital and underwrite loans continuously – so we can generate in real-time bespoke, hyper-personal products, at better prices, without taking on more risk

Nick Pedersen, Co-Founder and CEO, Project Atlas

AI Disruption Use Case II

Philippe Reynier, Co-Founder & CEO, Cigno SA

AI Disruption Use Case III

Adam Shaw, CEO, Data for Good 

• Current and emerging UK regulatory requirements for autonomous AI systems. What are the implications of the recent Data Use and Access Act 2025 and how does it affect Agentic AI deployments, particularly concerning data accessibility, security, and public trust?
• FCA, ICO and sector-specific compliance considerations
• Liability frameworks: when an agent makes a mistake, who is accountable?
• The Government's pro-innovation approach — and where the guardrails are
• Preparing your legal and compliance teams for the agentic age

• Governance frameworks for autonomous systems: accountability at board and executive level
• Human-in-the-loop design: when and how to maintain meaningful oversight
• Dynamic policy enforcement and real-time compliance monitoring in production
• Ethical guardrails: bias, fairness, transparency and explainability in agentic systems
• Data privacy in agentic workflows: managing sensitive data handled by AI agents
• Practical steps to audit-readiness: documentation, logging and explainability

Panellists

Professor Lukasz Szpruch, School of Mathematics, University of Edinburg; Director of Finance and Economics Programme, The Alan Turing Institute 

• Change management: building buy-in from board to front line
• Upskilling and reskilling: developing internal AI capability and literacy across the workforce
• Data and infrastructure readiness: what you need in place before deploying agents
• Integration challenges: connecting agentic AI to legacy systems and existing workflows
• Procurement and vendor selection: how to navigate the complex vendor landscape, assess third-party AI risks and what to look for in an agentic AI partner 
• Culture and trust: overcoming internal resistance to autonomous decision-making

Panellists

Ashutosh Pandey, VP Project Management, Quality and Process, Eutelsat Group

Jessica Ridella, Global Technology Managing Director, IBM UKI 

How is Agentic AI reshaping enterprise cybersecurity risk and what strategies are needed to secure the “agentic enterprise”? Update on the emerging market for specific AI cyber insurance policies 

Addressing the acute skills shortages and cultural shifts required for successful agentic AI adoption

• Leveraging initiatives like the UK Government’s AI Skills Boost to upskill the workforce for the agentic era. Practical strategies for “digitizing skills at scale”
• How executive roles will evolve: the AI-augmented C-suite
• Decision-making in the age of autonomous systems: what remains distinctly human?
• The digital workforce of tomorrow: humans and agents working in tandem
• New job categories, skills and career paths created by agentic AI deployment
• Managing a blended workforce: performance, accountability and organisational culture
• Board-level AI governance: what directors and non-executives need to understand

Panellists

Michael Smets, Professor, Said Business School, University of Oxford 

• Latest tools and frameworks: CrewAI, LangChain, AutoGen and what comes next
• Multi-agent collaboration: orchestrating teams of AI agents to tackle complex tasks
• Advanced reasoning models and their practical business applications
• Deployment strategies: cloud-native, on-premise and hybrid enterprise architectures
• The open-source vs proprietary debate for enterprise agentic AI
• Horizon scanning: where Agentic AI will be in 12-24 months and how to prepare

•  Identifying the right agentic AI use cases for your organisation: where to start
• Building the internal business case: engaging your board and securing investment
• Governance and risk frameworks to establish before you deploy
• Quick wins vs long-term transformation: a phased and pragmatic approach
• Measuring success: KPIs, ROI modelling and reporting to the board
• Resources and support: UK Government programmes, industry bodies and the ecosystem

Panellists

Keith Davies, Group Chief Risk and Compliance Officer, Admiral Group Plc 

A.  Agentic AI for Customer Experience: Automating complex service journeys

• Designing end-to-end autonomous customer journeys across digital and voice channels
• Personalisation at scale: using agents to tailor products, communications and pricing
• Managing customer trust and transparency in AI-driven interactions
• Handling edge cases, complaints and escalations in automated service environments
• Measuring CX impact: NPS, satisfaction, retention and revenue uplift

B.  Agentic AI in Operations: Workflow orchestration and supply chain optimisation

• Mapping your operations for agentic AI deployment: where the highest-value opportunities lie
• Workflow orchestration: connecting disparate systems through autonomous agents
• Supply chain use cases: demand forecasting, logistics, procurement and inventory
• Reducing operational costs and error rates through intelligent automation
• Integration with ERP, CRM and enterprise data platforms

C.  Agentic AI for Decision Support: Predictive analytics and autonomous planning

• Moving from dashboards to autonomous insight and action
• Data quality requirements for reliable and trustworthy agent outputs
• Risk management in AI-assisted and autonomous decision-making
• Use cases: financial forecasting, fraud detection, demand planning, regulatory reporting
• Maintaining human accountability when agents influence major decisions

Niresh Rajah, Chief Data & AI Officer, Chair – UK AI for Financial Services, Fellow at Imperial College and Associate Fellow at Oxford University