• 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

• 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

• Financial services: automating compliance checks, fraud detection and financial forecasting
• Retail & e-commerce: personalising customer experiences at scale and autonomous demand planning
• Logistics & operations: workflow orchestration and supply chain optimisation
• Public sector & government: autonomous processing, citizen services and defence applications
• Cross-sector lessons: what separates successful deployments from costly failures
• Measuring impact: the KPIs and business metrics that boards want to see

• Current and emerging UK regulatory requirements for autonomous AI systems
• 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
• Speaker to be confirmed

• 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

• 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: what to look for in an agentic AI partner
• Culture and trust: overcoming internal resistance to autonomous decision-making

• 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

• 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

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