As energy systems digitise and GenAI adoption accelerates, critical infrastructure operators face new regulatory scrutiny, cyber threats, and resilience risks. At Centrica, building a Responsible AI Framework has been key to scaling innovation while safeguarding operations, customers, and society. Attend this talk to understand how Ronnie and his team are:

·       Embedding AI governance across GenAI, ML, and critical energy infrastructure systems

·       Aligning risk tiering with regulatory, cyber, and environmental resilience expectations

·       Translating responsible AI principles into action across complex, distributed operations

Ronnie Chung

Group Head of Responsible AI
Centrica

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As one of the UK’s leading energy providers, OVO Energy is focused on enabling a greener future—and that includes how it uses AI. In this session, Myrna Macgregor, AI Risk Lead, explores the dual challenge of using data and AI to support decarbonisation, while also addressing the hidden environmental costs of AI itself. From energy optimisation to model accountability, discover how a responsible approach to AI can serve both innovation and sustainability goals.

• Applying AI and data-driven insights to accelerate net-zero initiatives across energy systems
• Tracking, measuring, and managing the carbon impact of enterprise AI development and deployment
• Implementing practical strategies to reduce AI’s environmental footprint without slowing progress

Myrna Macgregor

AI Risk Lead
OVO Energy

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As AI adoption accelerates across industries, ensuring responsible, scalable, and consistent deployment is more critical than ever—especially in highly regulated sectors like pharma. At Novo Nordisk, responsible AI is not a siloed initiative but combines collective effort across the enterprise with area and domain specific requirements. From aligning with evolving legislation like the EU AI Act to making it easier for practitioners to comply, this session explores how practical tooling, a trustworthy AI council, and other strong AI governance can turn frameworks into action.

• Establishing tools that embed compliance into AI workflows and decisions
• Building governance structures that scale with enterprise-wide AI adoption
• Treating AI as collaborative work, not standalone machine decision

Per Rådberg Nagbøl

Senior Data & AI Governance Professional
Novo Nordisk

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As AI adoption grows, so does the need for machines—and humans—to understand data in context. In the Scottish Government, Masood and the Digital Directorate’s technical team are architecting the data foundations that make responsible, explainable AI possible. This session explores the hands-on work behind enabling AI systems to interact with public sector data through intelligent tagging, synonym resolution, and semantic modelling. Learn how knowledge graphs, metadata enrichment, and NLP interfaces are unlocking scalable, governed access to complex datasets—without compromising on accountability.

·       Architecting semantic layers using knowledge graphs to power explainability and data lineage

·       Automating tagging, classification, and synonym resolution across siloed, federated data sources

·       Enabling NLP-driven “talk to data” interfaces to widen access without exposing risk

Masood Alam

Chief Data Architect
The Scottish Government

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Ensuring the safety of agentic AI systems requires thorough testing and verification methods. This session will dive into the best practices for testing autonomous AI, focusing on verification protocols that ensure the systems behave predictably and ethically under all conditions.

·       Exploring testing methodologies for ensuring the safety of autonomous AI systems.

·       Implementing verification processes that guarantee compliance with governance standards.

·       Addressing the challenges of testing agentic AI and its ethical implications.

Paul Dongha

Head of Responsible AI & AI Strategy
NatWest Group

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Dara L. Sosulski

Managing Director, Head of Artificial Intelligence and Model Management
HSBC

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As generative AI rapidly integrates into banking workflows—from customer service chatbots to advisory copilots—the risks of hallucination, toxicity, privacy violations, and regulatory non-compliance are rising. Intesa Sanpaolo, one of Europe’s largest banking groups, is tackling this head-on. In this session, Alessandro, Head of Responsible AI, shares how his team is designing and implementing technical guardrails around generative AI models, with a sharp focus on risk mapping, prompt injection protection, and fundamental rights impact assessments.

• Building proactive defences against prompt injection and out-of-context content generation
• Implementing toxicity filters and privacy safeguards in systems leveraging external LLMs
• Operationalizing EU AI Act principles through guardrails and continuous risk monitoring

Alessandro Castelnovo, Head of Responsible AI, Intesa Sanpaolo

Alessandro Castelnovo

Head of Responsible AI – Data Science & Responsible AI
Intesa Sanpaolo

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In an industry where content is the product, AI governance and decision making involves carefully maintaining a balance between risk to assets and potential value. Andi, Head of Data and AI Governance at the Financial Times, shares how a 135-year-old premium news organisation effectively governs a wide programme of AI-enabled solutions and experiments that enable experimentation without undermining their intellectual property or journalistic integrity

·       Creating flexible, multi-tiered governance tools that serve different stakeholder needs—from quick checklists to comprehensive consultations

·       Positioning AI governance as an innovation partner rather than a gatekeeper through approachable, frictionless processes

·       Developing practical methods to evaluate AI use cases against ethical frameworks while maintaining competitive advantage

·       Navigating the tension between exploring new AI-driven business models and safeguarding premium content value

Andi McAleer

Head of Data & AI Governance
Financial Times

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As AI continues to evolve, so must the skills of the workforce. This session will examine best practices for developing training and upskilling programs that enable employees to understand and responsibly engage with AI technologies. From foundational education to specialized workshops, we’ll cover how to structure learning paths that meet the needs of both technical and non-technical teams.

·       Design training programs tailored to both technical and non-technical employees.

·       Create learning paths that support responsible AI integration and adoption.

·       Foster continuous AI education to stay ahead in an evolving landscape.

Danielle Langford

Responsible AI Specialist
Zurich Insurance

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Georgiana Marsic

Former Principal AI Manager
Jaguar Land Rover

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Oriana Medlicott

Responsible AI Lead
Admiral Group

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As AI transforms financial services, the Financial Conduct Authority is playing a dual role: setting expectations for responsible innovation across the sector while embedding responsible AI practices within its own organisation. In this session, Fatima, Principal Advisor for Responsible AI and Data, offers a rare window into both sides of that journey. From aligning internal frameworks with data privacy, cyber and legal requirements, to collaborating with DSIT and Ofcom on national policy, Fatima explores what responsible AI means in practice—for regulators and the regulated alike.

• Applying principles internally to support safe and responsible use of AI
• Shaping future-facing regulation through collaboration, research, and open engagement
• Building internal capability through data strategy, literacy, and AI-specific governance structures and Data and AI Ethics Frameworks.

Fatima Abukar

Principal Advisor, Responsible AI and Data
FCA

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AI’s increasing role in workplace decisions, from hiring to performance management, raises important ethical concerns. This session will explore the ethical implications of AI-driven decisions, focusing on transparency, fairness, and accountability. We will address the role of governance in ensuring AI systems are used responsibly and in ways that uphold organizational values and employee rights.

·       Examining the ethical impact of AI-assisted decisions on the workforce.

·       Discussing strategies for ensuring fairness, transparency, and accountability in AI algorithms.

·       Managing the ethical challenges of algorithmic management in the workplace.

Philippa Penfold

Responsible AI & Data Science Manager
Elsevier

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Sarah Matthews

Group Responsible AI Manager
The Adecco Group

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Vijay Mitra

Generative AI & Responsible AI Risk Lead
Nationwide Building Society

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