Responsible AI Summit Main Conference Day 1 - Monday 21 September

Deploying an AI system is not the finish line but the start of an ongoing obligation, one most organisations are not structured to meet. Effective oversight requires monitoring across two equally necessary surfaces: internal monitoring, which tracks cost, quality, drift, safety, and performance over time, and external monitoring, which captures vendor changes, public incidents, and evolving regulation. This session explores why AI monitoring is a genuinely hard problem and why most organizations only build for one side of it. It also examines why governing at the use-case level, rather than the model level, creates oversight that can withstand vendor swaps and mixed deployments.

• Implementing internal and external monitoring across two equally necessary surfaces.
• Governing use cases to maintain oversight through vendor and model changes.
• Evaluating cost, quality, drift, safety, and external risk signals.

Sponsored by: Trustible

---

Gerald Kierce

Co-Founder & CEO
Trustible

See Bio

AI regulation is accelerating, yet many organisations still struggle to translate legal obligations into effective, day to day governance. Under the EU AI Act and emerging standards frameworks, organisational functions including Legal and Governance are collectively responsible for risk classification, oversight, documentation, and organisational controls, but often approach these duties from different angles. This session explores how Legal and Governance teams can work together in practice: aligning responsibilities, streamlining AI use case triage, and building a shared governance model that stands up to regulatory scrutiny.

• Aligning legal and governance roles through shared ac-countability models

• Improving AI use case filtering to focus oversight where it matters most

• Strengthening collaboration by clarifying what Governance needs from Legal to operationalise compliance

Barbara Zapisetskaya

Principal Counsel
EBRD

See Bio

Kirsten Van der Zwan

Chief Privacy Officer and Global Head of Privacy, AI & Digital Compliance
Signify

See Bio

Gwen Morvan

Senior Legal Counsel
Synthesia

See Bio

Nick Wild

Senior AI Governance Lead
Swift

See Bio

AI driven innovation is accelerating, and with it, a surge of new tools, datasets, and experimental vendors vying for a place in the enterprise. Every promising partnership also carries potential privacy, security, and regulatory exposure. In this session, Lucia Batlova, Europe and META Data Protection & Privacy Lead at Lenovo, reveals how teams in a global organization cut through the noise: rapidly assessing high volume third party requests, screening risky AI initiatives without slowing momentum, and enabling safe experimentation at scale. With real world examples, she shows how legal can stay firmly positioned as an innovation accelerator, while keeping risk firmly in check (considering the fragmented global AI landscape & insufficiency of traditional privacy and security frameworks).

• Privacy and legal teams as innovation enablers: managing AI risk without unnecessarily slowing progress 

• Reasonable expectations of privacy, transparency, and trust in AI deployments 

• Third-party AI providers, vendor due diligence, and data-sharing considerations 

• Data use limitations, purpose assessment, and appropriate safeguards 

• Embedding privacy by design into AI and product development processes

Lucia Batlova

Senior Legal Counsel & EMEA Privacy Lead
Lenovo

See Bio

As the EU AI Act moves into its implementation phase, the focus is shifting from legislative ambition to operational delivery. Organisations must interpret risk classifications, conformity assessments and oversight duties, while aligning internal governance structures. This session offers a clear and practical update on timelines, enforcement trends and what regulators expect.

• Clarifying risk tiers and governance responsibilities.

• Aligning internal controls with supervisory scrutiny.

• Preparing for documentation, audit and enforcement readiness.

Pranav Gupte

Associate Director, AI Governance & Enablement
AstraZeneca

See Bio

Lara Nogueira

Head of Responsible AI & Data Compliance
Ericsson

See Bio

Gary Brown

Chief Privacy Officer
Westinghouse Electric Company

See Bio

Oriana Medlicott

Responsible AI- EU Lead
Admiral Group

See Bio

The challenge is no longer defining principles but embedding them into delivery. Organisations have moved beyond governance frameworks and are now embedding operational capabilities such as AI risk management, monitoring, model assurance and run time controls directly into technology delivery. This panel discussion will explore the controls that actually work, how to remain in control of agents, and what organisations need to do now to prepare for the next wave of AI adoption.

Sponsored by: Capgemini

---

In this technical case study, Alessandro Castelnovo, Head of Responsible AI at Intesa Sanpaolo, details how the bank designed and operationalised Guardian Agents to govern emerging multi-agent AI ecosystems. He presents a formal taxonomy that combines three operational roles (Reviewers, Monitors, and Protectors) with five structured risk domains: Data Security & Protection; Performance & Reliability; Quality & Compliance; Explainability & Transparency; and Ethical Coordination & Decisioning. The framework clarifies responsibilities, embeds automated safeguards, enables continuous oversight, and supports accountable, resilient orchestration across complex agent networks.

• Formalising operational roles define structured AI oversight mechanisms.

• Creating five risk domains that align governance with concrete control layers.

• Embedding safeguards to enable resilient, accountable multi-agent orchestration.

Alessandro Castelnovo

Head of Responsible AI
Intesa Sanpaolo

See Bio

• Olympus organisational model for AI governance and cybersecurity.
• Key collaboration challenges between governance and security teams.
• Embedding security controls into enterprise AI governance processes.

Robin Schoss

Director, AI Governance and Head of Information Security EMEA
Olympus

See Bio

As enterprises delegate decisions to autonomous systems, they must confront a deeper question: what does it mean to transfer authority without severing responsibility? Agentic AI challenges traditional notions of control, oversight, and accountability, forcing organisations to redefine human agency in operational terms. This panel explores the philosophical and practical dimensions of delegation, and the skills required to remain meaningfully responsible for systems that act on our behalf.

• Examining delegation without severing human responsibility.

• Redefining agency in autonomous enterprise systems.

• Building skills for accountable human oversight.

Detlef Nauck

Head of AI & Data Science Research
BT

See Bio

Olu Akinyede

Data Privacy, Data Governance and AI Ethics
Aviva

See Bio

Ramin Mobasseri

Head of Agentic AI Delivery
Wells Fargo

See Bio

Rozemarijn Jens

Senior AI Innovation Lead
Shell

See Bio

In highly regulated sectors, such as life sciences, regulatory changes create a hidden risk: compliance drift, beyond the usual data drift. Hence, there is a need for practical strategies to keep AI aligned with evolving regulations and operational realities. This session will present the AI governance setup in the Research and Development area of Novo Nordisk. It will cover ideas, challenges, and practical solutions for automating AI governance without correspondingly scaling human labour. 

• moving from regulation to automatable requirements and processes that enable the use of rule-, chatbot-, and agent-based automation.

• preventing compliance drift.

• making automation complement human labour.

• creating oversight of AI systems and visualise AI system interdependencies.

Per Rådberg Nagbøl

Senior Data & AI Governance Professional
Novo Nordisk

See Bio

Explainability is often treated as a compliance afterthought; at Wiley, it is a system design requirement. At the scale of one of the world's largest scholarly publishers, that means AI outputs must be traceable, transparent about what models are trained on, clear on where human intervention sits in the workflow, and explicit about how updates and corrections are managed over time. In this session, Pascal Hetzscholdt, Director of AI Strategy and Content Integrity, provides a candid, technical look at how enabling students and researchers to prompt directly against curated, licensed content materially reduces hallucinations.

• Addressing cost pressures that drive unseen model changes.

• Securing meaningful quick wins while protecting long- term information integrity.

• Strengthening institutional situational awareness.

Pascal Hetzscholdt

Senior Director of AI Strategy and Content Integrity
Wiley

See Bio

As AI systems scale and grow in complexity, the lines between engineering responsibility and governance oversight can blur. This panel explores where frictions emerge, how technical and governance teams can collaborate effectively, and whether current role definitions are fit for purpose. Through real-world examples, the discussion will probe whether enforcing policy-as-code and defining accountability truly resolves tension.

• Assigning clear roles to reduce friction and evolving with system complexity.

• Embedding governance into practice through technical pipelines.

• Integrating monitoring and checks to enforce accountability in real time.

Disha Mukherjee

Lead Data Engineer
Ford Credit

See Bio

Graham Ross

Head of Responsible AI
Centrica

See Bio

Stephanie Cairns

Senior Data Scientist, AI Risk
OVO Energy

See Bio