Responsible AI Summit Main Conference Day 1 - Tuesday 23 September

As organizations navigate the complex landscape of AI implementation, many struggle with moving beyond theoretical frameworks to practical governance solutions—especially in highly regulated industries like energy. Ania Kaci, Global Leader for Responsible AI at Schneider Electric, brings frontline expertise from building and scaling a cross-functional Responsible AI program across a multinational energy management and industrial automation leader.

Drawing from Schneider Electric's journey developing high-risk AI systems for energy management, predictive maintenance, and industrial automation, Ania will provide actionable insights on:

·       Translating EU AI Act requirements into practical governance frameworks that align with sectoral regulations and business objectives

·       Building effective AI risk assessment methodologies specifically designed for energy and industrial automation applications

·       Creating a thriving Responsible AI community that extends beyond legal and technical teams to drive organization-wide AI literacy

·       Balancing innovation with compliance across global operations and regulatory landscapes

·       Leveraging AI for sustainability goals while addressing AI's own environmental impact

Ania Kaci

Global Leader for Responsible AI
Schneider Electric

See Bio

When scaling AI governance across global organizations, leaders face a key decision: centralized or federated governance? This session will examine the advantages and challenges of both approaches, providing strategies to determine the optimal model for your organization’s size, complexity, and regulatory environment.

·       Comparing centralized and federated governance models for global AI deployment.

·       Building flexible frameworks that scale with organizational needs and regulations.

·       Implementing governance structures that ensure consistency while allowing for local autonomy.

Anastasia Zygmantovich

Global Data Science and Data Visualisation Director
Reckitt

See Bio

Penny Jones

Responsible AI Lead
Zurich Insurance UK

See Bio

Martin Woodward

Global Responsible AI Officer
Randstad

See Bio

Cécile Prinsen

Expert Associate Partner
McKinsey & Company

See Bio

Session Details to be Announced

Matthias Holweg

American Standard Companies Professor of Operations Management
Saïd Business School, University of Oxford

See Bio

Uthman Ali

Global Responsible AI Officer
bp

See Bio

How do LLMs behave across different languages and cultural contexts — and what does this mean for fairness in real-world deployments? Clara, Responsible AI lead at BBVA, is translating her applied research on multilingual LLM bias into concrete methodologies to evaluate bias in LLM-powered applications. Her work focuses on identifying performance disparities across demographics and vulnerable groups that could lead to discriminatory outcomes.

• Using control and adversarial testing, as well as cultural probing, to surface hidden biases in LLM-powered applications
• Adapting open-domain bias metrics to enterprise-specific use cases and decision contexts
• Integrating fairness assessments into scalable pipelines with sociotechnical oversight
• Embedding bias evaluations into product workflows to support accountable, trustworthy, human-centered AI

Clara Higuera

Responsible AI Lead
BBVA

See Bio

As AI adoption accelerates across every discipline, it’s critical to keep all colleagues informed and alert to the risks, limitations and biases inherent in AI systems. In this visual and insightful session, Richard Boorman will share how he has successfully engaged teams across a complex global enterprise - using compelling AI tests and memorable AI fails, alongside external research findings, to spark curiosity, drive understanding and promote responsible AI practices.

Mastercard is recognized for operating one of the most advanced AI Governance programs in the industry. Established in 2019, the program rigorously evaluates all internally developed and externally sourced AI systems to ensure they meet high standards of ethical, responsible and human-centric design and deployment. 

• Strategies for education and enablement as core pillars of AI Governance.
• Creative approaches to communicating AI flaws, bias, and complexity.
• Ideas for impactful and repeatable AI demonstrations.

Richard Boorman

AI Governance
Mastercard

See Bio

Monitoring AI systems post-deployment is key to ensuring their continued efficacy and safety. This session will focus on technical frameworks for tracking AI performance, detecting anomalies, and ensuring compliance with established governance practices. Learn about the tools that enable real-time monitoring and proactive management of deployed AI models.

·       Learning about frameworks for continuous monitoring of AI system performance.

·       Exploring anomaly detection tools to ensure AI systems perform as expected.

·       Understanding compliance and governance monitoring for deployed AI.

Andrea Cosentini

Head of Data Science & Responsible AI
Intesa Sanpaolo Bank

See Bio

Martin Koder

AI Governance Lead
Swift

See Bio

Nestlé is the world’s largest food and beverage company, with 300,000 employees, a digital ecosystem spanning 185 countries, and an AI portfolio powering everything from demand forecasting to food safety. But how does a non-tech-native organisation govern AI responsibly on a global scale? In this session, Laura will share how Nestlé’s hybrid governance model—balancing global oversight with local flexibility—enables responsible, enterprise-wide AI adoption without losing sight of cultural nuance, operational complexity, or ethical risk. This talk will delve into: 

• Building governance on existing digital foundations: culture, quality, legal alignment, and values
• Applying a hybrid model: global policy with local adaptation across 185 countries
• Embedding people, process, and tool-based controls into every stage of AI deployment

Laura Perea Virgili

AI Risk Management in the Responsible AI office
PepsiCo

See Bio

As AI adoption accelerates, governance in large enterprises is under strain, caught between regulatory pressure, organizational complexity, and the need for speed. This session distills lessons from some of the world’s largest organizations on how to navigate that tension and make governance a foundation for both trust and productivity.

• The cost of fragmented processes, manual reviews, and wasted time
• Why visibility and risk awareness remain elusive at enterprise scale
• The cultural gap between lofty principles and everyday practice
• What it takes to embed governance without slowing innovation

Ahmet Gyger

Senior Director of Product Management
Domino.ai

See Bio

In this panel discussion, we will share and dissect real-world case studies of AI governance in action.

Together, we will explore key themes such as decision-making structures, roles and responsibilities, and the practicalities of operationalizing governance models at scale.

·       Sharing and critiquing case studies of AI governance frameworks.

·       Discussing challenges and lessons learned from real-world implementations.

·       Identifying best practices for scaling and ensuring accountability across AI initiatives.

Hellena Crompton

Data Protection Officer UK&I
dentsu international

See Bio

Ronnie Chung

Group Head of Responsible AI
Centrica

See Bio

Shazia Khan

Senior Privacy Counsel, Axiom Law and Co-Founder
Women in AI Governance (WiAIG)

See Bio

As AI becomes embedded in core business and operational functions, safeguarding these systems requires deep collaboration between AI and cybersecurity teams. At Philip Morris International, Ray, Head of AI Security, is leading efforts to ensure security is not an afterthought—but a foundational principle across AI development. This session will share how PMI is building secure-by-design AI practices in a complex, global environment, while aligning with compliance and risk expectations.

• Building integrated workflows between AI and cybersecurity to address risks early and effectively
• Operationalising security protocols tailored to AI-specific threats across global infrastructure
• Aligning security, compliance, and innovation priorities to support responsible AI at scale

Ray Ellis

Head of AI Security
Philip Morris International

See Bio

Slot Reserved for Sponsor Partner

Establishing guardrails for responsible AI deployment is essential for minimizing risk and ensuring ethical outcomes. This session will cover how to design and implement content filtering mechanisms and establish safeguard protocols that prevent harmful AI behaviour, especially in sensitive or regulated environments.

·       Developing technical guardrails and content filtering for ethical AI deployment.

·       Understanding the importance of regulatory compliance in AI safety design.

·       Implementing mechanisms for proactive monitoring and control of AI outputs.

Ray Ellis

Head of AI Security
Philip Morris International

See Bio

Stephanie Cairns

Senior Data Scientist, AI Risk
OVO Energy

See Bio

Mo Sadek

Technical Marketing Director
ActiveFence

See Bio