Main Conference Day 1, Wednesday, 4 December 2025

• Overview of the current state of AI integration in shared

service centers, highlighting practical use cases and pilot

projects.

• Exploration of key challenges faced during AI

implementation, including ROI measurement, change

management, and technology adoption hurdles.

• Insights into future trends and strategic priorities for scaling

AI-driven automation across global business services.

Marc Sauty

Transformation Director
Sodexo

See Bio

• Use of orchestration frameworks to chain LLM calls with tools,

APIs, and memory for complex multi-step workflows.

• Case studies from document processing, procurement, and

service resolution with fully or semi-autonomous agents.

• Alignment with platform strategy, change management, and

IT compliance for controlled scaling of agentic systems.

Slot reserved for one of our business partners

• Exploration of practical use cases of generative and agentic

AI transforming back-office and global business services

operations.

• Discussion of key application areas including finance, HR,

procurement, and compliance within GBS environments.

• Vision for the future role of autonomous AI workflows in

driving efficiency, scalability, and strategic innovation across

back-office functions.

Marc Sauty

Transformation Director
Sodexo

See Bio

Wioletta Strączek

GBS Finance Transformation Director
Jacobs

See Bio

• Exploration of AI-driven change management techniques to enhance operational excellence.

• Use of AI for strategic decision-making, including predictine and managing change impacts.

• Automation of routine processes to increase productivity and • streamline organizational transformation.

Wioletta Strączek

GBS Finance Transformation Director
Jacobs

See Bio

• DT position on the EU AI Act: GPAI models and harmonized

standards

• How to implement it in DT

• Extension to Green AI

Joachim Stegmann

Chapter Team Lead Artificial Intelligence & Data Science
Deutsche Telekom AG, Innovation Hub

See Bio

• Showcase of real-world agentic AI applications across

enterprise functions such as customer service, supply chain,

and finance.

• Overview of deployment approaches, including integration

with existing IT systems and overcoming operational challenges.

• Best practices for scaling agentic AI solutions, focusing

on governance, performance monitoring, and continuous

improvement.

Slot reserved for one of our business partners

• Explains how companies can turn the AI Act into practical rules, responsibilities, and risk

checks for real-world AI systems.

• Shows how legal categories and risk levels from the regulation become actual tools,

access rules, and workflows inside organizations.

• Highlights why training, culture, and two-way communication are key to getting

thousands of people to follow responsible AI practices at scale.

Dr. Djalel Benbouzid

Trustworthy AI Expert
AIAAIC

See Bio

• A practical blueprint for decentralized AI with strong governance

• How org–architecture fit accelerates platform adoption and reuse

• Field-tested best practices for ML/LLMOps, security, data, and change management

Ömer Adıgüzel

Technical Lead Generative AI
Lufthansa Group

See Bio

• Proof-of-concept using a multi-agent architecture to replicate the internal decision

dynamics of a marketing department

• Agents work together using signals from traditional ML models (e.g. churn prediction,

personalization) to coordinate budgets, campaign strategies, and actions

• A hybrid system where humans remain in the loop to validate and approve outcomes,

reducing planning cycles from days to hours

Sabato Leo

Director of AI, Data Science and Research and Development
Tipico

See Bio

• Personal AI generally refers to AI systems that are designed specifically for an individual, learning from their unique data and behaviors

• How enterprises are combining traditional ML-based personalization signals with GenAI agents to orchestrate dynamic, goal-driven marketing and customer engagement workflows

• Practical challenges in deploying agentic systems—from data integration and model oversight to aligning agent actions with brand, compliance, and campaign strategy

• The evolving role of AI teams, marketing leaders, and human reviewers in co-piloting agentic workflows that accelerate planning, content creation, and decision cycles

Sabato Leo

Director of AI, Data Science and Research and Development
Tipico

See Bio

Aidan McLaughlin

Global Director, Esg Marketing
Indeed.com

See Bio

• Showcasing AI agents that deeply understand candidate skills, life stages, and values beyond resumes to deliver personalized job and talent matching. • Exploring the dual-agent system serving both job seekers and employers in real time.

• Discussing the strategic impact of agentic AI on evolving recruitment marketplaces an internal adoption challenges.

Aidan McLaughlin

Global Director, Esg Marketing
Indeed.com

See Bio

• How AI agents transform complex insurance claims handling, from triage to resolution

• Moving beyond pilots to scalable, industrial-grade solutions that meet regulatory and customer requirements

• Success factors for integrating AI into legacy systems, enabling human–AI collaboration, and measuring impact across the claims value chain

Hugo Hamad

Head of AI Factory
AXA France

See Bio

• Exploring the deployment of agentic AI and robotics to automate critical operational tasks in transportation.

• Discuss the necessity for high resilience in complex, dynamic environments, and the enablers required.

• Building capabilities through pilot projects that integrate human expertise and AI for scalable enterprise solutions

Neda Naghshbandi

Technical Director- AI & Robotics Strategy, Solutions, and Delivery
AtkinsRéalis

See Bio

• Use of GenAI agents to automate routine accounting tasks such as invoice matching, variance detection, and journal entry suggestions across multiple ledgers.

• Coordination of agentic workflows that integrate with ERP systems, handle exception resolution, and collaborate with human approvers in shared service centers.

• Governance, auditability, and risk controls to ensure transparency and compliance in AI-driven financial operations.

• Examining how generative and agentic AI are reshaping enterprise architecture, security, and operational governance.

• Sharing practical insights from Nestlé’s approach to AI committee oversight, risk mitigation, and compliance with evolving regulatory frameworks like the AI Act.

• Discussing the continuous evolution of governance practices to address hallucination control, legal and functional requirements, and future-proofing AI deployments.

Fernando Blanco

Senior Enterprise Architect
Nestlé

See Bio

• Connecting LLM-powered agents to internal systems through

APIs to automate tasks across domains such as customer

service, compliance, and operations.

• Introduction of middleware strategies, API wrappers, and

lightweight orchestration layers to ensure robust and secure

AI-to-system interactions.

• Governance and team enablement challenges in exposing

enterprise functionality to GenAI tools while maintaining control

and compliance.

Avinash Jha

Senior Director | Head of API & Integrations
Volvo Group

See Bio

• Navigating the shift from analytics-led to data-first thinking

in enabling sustainable AI transformation.

• Building a data quality framework that supports compliance,

scalability, and model accuracy across a global enterprise.

• Lessons from Ericsson’s AI adoption journey: organizational

change, governance, and aligning architecture with business

needs.

Gabor Harsanyi

Head of M&CR Data Office
Ericsson

See Bio

• Deploying autonomous data quality agents to cut preparation time from weeks to hours, accelerating insights for project bids and financial reporting.

• Preventing downstream errors through continuous AI-driven monitoring and correction of data streams.

• Shifting expert teams from manual maintenance to predictive analytics by automating validation, integration, and reporting.

Rafael Socorro

Head of Data & Analytics
ACCIONA

See Bio

• Exploring Telefónica’s use of generative AI to enhance customer-facing virtual assistant

• Aura for more natural, flexible dialogues.

• Addressing challenges of hallucination, data privacy, and latency in deploying generative AI in customer service.

• Highlighting ongoing efforts to advance from deterministic bots to agentic AI systems capable of dynamic reasoning and personalized responses.

Tina Rahman

Chief Product Owner Conversational AI (Voice and Chatbots)
Telefonica

See Bio

• Implementation of GenAI-driven personalization engines to dynamically tailor content, promotions, and product recommendations across customer touchpoints.

• Integration of real-time behavioral data with customer profiles to trigger individualized experiences in web, mobile, and email channels.

• Approach to safely scaling personalization in retail environments, balancing privacy, performance, and brand control.

• Aligning operational and commercial strategies to enable agile, scalable personalisation that delivers measurable ROI within stringent regulatory frameworks.

• Leveraging first-party data responsibly with privacyby- design approaches to power compliant and effective personalisation initiatives.

• Building future-proof technical architectures that combine advanced analytics, AI, and machine learning to deliver secure, adaptive, and scalable personalised customer experiences.

In this immersive 2.5-hour workshop, participants will learn how to shift from traditional UI/ UX paradigms to a conversation-first design approach for AI-powered assistants. Using realworld case studies and guided collaboration, attendees will explore how to structure user-agent interactions that are natural, scalable, and production-ready.

• Understand the foundations of conversation design and why it is essential for building effective AI-driven customer experiences.

• Practice mapping realistic conversation flows to surface business requirements and define necessary backend functions. • Prototype assistant behavior using fake data and prompt engineering before involving development teams.

• Explore how to design multilingual, dynamic, and accessible AI agents that adapt across contexts and customer needs.

Emily Stretton

Growth Executive
Holiday Extras

See Bio

Sarah Page

Innovation & AI Lead
Holiday Extras

See Bio

Personalization promises high-impact engagement, but most initiatives collapse under fragmented data, weak governance, and models that don’t scale. This masterclass explores how to design the data foundations and AI strategies that make hyperpersonalization sustainable — moving beyond pilots to enterprise-wide execution.

• Why most personalization initiatives stall without solid data foundations, and how to avoid common pitfalls

• Architecting scalable personalization with clean, connected, and governed data for AI-driven workflows

• Practical strategies to align data quality, orchestration, and AI models to deliver real-time, personalized experiences at scale

• Data Foundations for an agentic workflow orchestration

Sabato Leo

Director of AI, Data Science and Research and Development
Tipico

See Bio