Main Day 1 - Tuesday 4th November

Europe’s AI journey is entering a critical phase - marked by breakthroughs in compute, rising energy demands, and fragmented policy landscapes. This session offers a panoramic view of the current European AI ecosystem and what’s on the horizon. From the convergence of high-performance computing, AI, and quantum to the infrastructure and policy challenges that could shape, or stall, progress, we unpack what leaders need to watch now.

• The current state of AI adoption and innovation across Europe
• How AI, HPC, and Quantum are starting to converge—and what that enables
• Strategies for protecting both physical and digital AI infrastructure
• Balancing global interoperability with regional regulation and data protection
• Strategic considerations for enterprises building long-term AI capabilities in Europe
• The future of data centres: cost, energy, investment, and sustainability

Maria Costeira

Technology Expert - AI & Quantum Engineering
Independent

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Emily Campbell-Ratcliffe

Head of Private Sector AI Adoption, Governance & Skills
Department for Science, Innovation and Technology (DSIT)

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Kamran Naqvi

Chief Network Architect – EMEA
Broadcom

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How do leading enterprises build AI platforms that go beyond isolated pilots and enable scalable, production-grade AI applications? In this panel, senior platform and infrastructure leaders share practical insights into architecting AI platforms that deliver reusability, governance, and speed, at scale.

• Designing reusable, composable AI platforms for enterprise-wide adoption
• Striking the balance between centralized control and developer autonomy
• Tooling and orchestration strategies for managing the end-to-end model lifecycle
• Infrastructure choices for supporting hybrid and multi-cloud AI workloads
• Embedding governance, performance, and cost-efficiency into platform design

Vijay Vammi

Enterprise MLOps Services & Technology Owner
AstraZeneca

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Matt Sherriff

AI Platform Manager
The Telegraph

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Ajwad Ali

Head of AI Engineering
AXA

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Infrastructure teams must move beyond patchwork solutions and design integrated, full-stack platforms purpose built for scale. This session explores how leading enterprises are seeking to architect a comprehensive, total infrastructure ecosystem that aligns hardware, software, and operational layers - from GPUs and networking to orchestration tools and model lifecycle management. Building high-performance, scalable foundations that enable AI innovation without sacrificing reliability or control.

• Core components of a scalable AI infrastructure stack: compute, storage, and orchestration
• Integrating ML frameworks, pipelines, and developer tooling into infrastructure design
• Strategies to support flexibility, performance, and cost-efficiency at scale
• Avoiding bottlenecks and rework: aligning architecture to long-term AI roadmaps

As AI workloads evolve from experimentation to enterprise-scale deployment, infrastructure leaders face new challenges in performance, cost, latency, and scalability. This session, led by Renato Paco, Global Director of Cloud & Infrastructure at Sky, explores the infrastructure, resource, and financial considerations common to Media companies to build a scalable, optimized environments for AI transformation at scale

• Building hybrid environments that integrate on-prem and cloud-based GPU clusters to support elastic, high-throughput AI workloads.
• Capacity Planning for AI Workloads: Forecasting compute demand across model types, including considerations for batch size, concurrency, and inference latency.
• Balancing Cost, Latency & Accuracy: Frameworks for evaluating trade-offs and optimizing infrastructure for business outcomes.
• Applying non-functional testing, load simulation, and observability to validate infrastructure under real-world AI traffic patterns.
• Futureproofing Compute Tooling: Building adaptable, shared environments that support innovation, multi-tenancy, and evolving AI algorithms.

Renato Paço

Global Director Cloud & Infrastructure
Sky Group

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Wells Fargo’s internal AI platform—named Trachyon—serves as the critical middleware between internal users and external LLMs to drive everything from an internal chatbot, personalization at scale and regulatory compliance. It strengthens governance, enables seamless model swapping, and ensures robust guardrails. In this session, Robin will explore:

• Governance-first orchestration – how Trachyon enforces data sanitisation and privacy
• Poly model & poly cloud architecture – the flexibility to plug in different LLMs and clouds without breaking workflows
• Scaling secure high volume use cases – powering Fargo’s massive interaction volume using Trachyon’s secure pipeline

Robin Mobasseri

Executive VP of AI and Analytics Implementation and Services
Wells Fargo

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Vijay Vammi, Enterprise MLOps Services & Technology Owner at Astrazeneca join us to share his team’s journey to build scalable, observable, and developer-friendly platforms that balance innovation with control. Moving the business towards MLOps to better align engineers with your AI platform infrastructure.

• Enabling product velocity while protecting innovation through scalable ML pipelines
• Embedding observability and performance monitoring into every stage of the model lifecycle
• Unifying workflows through a single internal developer platform to reduce silos and increase adoption
• Driving adoption of GenAI and platform standards through internal consulting, prompt design, and user-centric tooling
• What does success look like? Measuring impact for your AI and ML investments.

Vijay Vammi

Enterprise MLOps Services & Technology Owner
AstraZeneca

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This session explores the evolution of personalisation and recommendation systems in the banking and finance sector - from traditional machine learning pipelines to cutting-edge multi-agent LLM architectures and emerging technologies. Drawing on real-world leadership experience, Lucas will share insights into the technical and strategic challenges of designing intelligent, compliant, and scalable personalisation systems in a complex regulatory environment.

• Building end-to-end ML pipelines for personalisation in banking and finance: A look into the foundations of personalised systems - from data and feature engineering to model development, deployment, monitoring, and feedback loops - within a highly regulated, data-rich environment.
• Evolving to Generative AI: Architecting multi-agent LLM systems for personalisation: Designing agent-based LLM architectures to support personalisation at scale across financial services.
• Unlocking customer intelligence with GenAI: Using generative AI to power customer understanding and enable hyper-personalised user journeys.
• Applying quantum-inspired algorithms for enhanced recommendations: Exploring how quantum-inspired techniques can optimise recommendation efficiency and handle complex challenges.
• Immersive personalisation: Extended Reality (XR) in next-generation customer experience: Examining the potential of augmented and virtual reality to reshape customer interaction models and deepen personal engagement in digital finance.

Lucas Vinh Tran

Director of AI/ML & Engineering
JPMorganChase

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To deliver real transformational change, enterprise leaders must understand how they can effectively scale to meet billions of constantly changing data items, thousands of queries per second, with minimal latencies. During this session we explore how you can use big data, RAG, vector search, machine learning and LLMs to deliver fast, precise decisions that drive real business success.

• Query, organize and make inferences in vectors, tensors, text and structured data at enterprise level
• Building data-driven application that deliver unbeatable performance and infinite automated scalability
• Efficiently retrieving and processing vast amounts of information with RAG architecture

Highly regulated sectors - from life science to financial services - demand zero tolerance for failure when deploying AI. In these environments, “close enough” is never good enough. During this session, Bryan Carmichael, Director of Digital Innovation & Data Science explores how ThermoFisher are building robust AI infrastructures that adhere to regulatory issues, ensure auditability,

and guarantee repeatability.

• Designing AI systems where precision, not probability, is the standard
• Choosing tools and data pipelines that ensure regulatory compliance
• Integrating human-in-the-loop mechanisms for validation and control
• Collaborating effectively with regulators, auditors, and legal advisors

Bryan Carmichael

Director, Digital Innovation & Data Science
Thermo Fisher Scientific

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Deploying AI in a clean, greenfield stack is easy. Doing it in the real world - across legacy systems, siloed tools, and tightly coupled processes - is not. Apoorv Kashyap joins us to share how financial services are integrating AI across a complex mesh of technology and regulatory requirements. Sharing lessons learned - from user adoption to LLM cost management to system integration challenges, and what it really takes to make AI work in production.

• How to navigate AI deployment in legacy-heavy tech ecosystems
• Integration and process alignment challenges—and how to overcome them
• Cost and benefit tracking strategies for enterprise scale
• Lessons from a successful enterprise AI transformation

Apoorv Kashyap

Head of Artificial Intelligence
Santander

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AI investment is accelerating but proving a tangible near-term business value remains a challenge. This panel brings together enterprise AI leaders to unpack how to garner continuous internal buying, collaborate across non-technical functions, and position AI as a core business enabler - not just a technical experiment.

• Defining and measuring the real impact of AI investments: cost, productivity, revenue, and beyond
• Securing executive support and funding for AI products in competitive prioritisation environments
• Communicating value to non-technical stakeholders and navigating organisational AI illiteracy
• Embedding AI thinking across product, operations, and business strategy
• Evolving your role from technical lead to enterprise AI change agent

Jie Zheng

Technology Team Lead - Machine Learning Lab
TUI

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Deepak Paramanand

Director Of Artificial Intelligence
JPMorgan Chase & Co.

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Ahmed Elahi

Senior Director of Data
SkyShowtime

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Anton Hinel

VP, Machine Learning Research
American Express

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