Conference Day 1, Thursday, 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

Consultant
Independent

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• How LHH applies agentic AI to automate candidate screening and enrich candidate data through conversational agents

• Lessons from standardizing job descriptions, CV summaries, and matching logic using AI across multiple markets

• The impact on consultant efficiency, candidate experience, and organizational scalability

Robin Klein

Director CRM & Digital
LHH

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• 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

Consultant
Independent

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Wioletta Strączek

GBS Finance Transformation Director
Jacobs

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Eduard Serrahima de Cambra

Director Data Science
Novartis

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Armand Angeli

Vice President Digital Transformation & AI
DFCG - IAFEI

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• 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

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

standards

• How to implement it in DT

• Extension to Green AI

Dr. Joachim Stegmann

Team Lead, Chapter Artificial Intelligence & Data Science
Deutsche Telekom

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• 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

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• 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.

Djalel Benbouzid

AI Senior Manager and Trustworthy AI Expert
Volkswagen Group

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• Demonstrating how agentic AI supports compliance in medical devices by assessing both QMS processes and product records.

• Showing how limited expert capacity can be scaled through agentbased evaluations, with human reviewers validating AI findings.

• Sharing lessons learned from Philips’ journey in applying AI to streamline compliance across multiple product lines.

Edward Arunraj

Director, Agile Methodologist, Enterprise Agile Transformation and Capability Ex
Xpedite B.V.

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• How Opella’s global AMP framework advances marketing-mix modelling and econometrics for evidence-based decisionmaking

• Using simulation and optimization to test marketing scenarios before real-world budget commitment

• The next step: exploring agent-based approaches for simulating multi-channel marketing interactions and future personalization workflows

• A hybrid system where humans remain in the loop to validate and approve outcomes, reducing planning cycles from days to hours

Cristina Gomez

Global Lead Data Scientist, consumer Healthcare
Sanofi Spain

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• 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

Cristina Gomez

Global Lead Data Scientist, consumer Healthcare
Sanofi Spain

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Lydianne Ardry

Director CRM
Crypto.com

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Lydianne Ardry

Director CRM
Crypto.com

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Zeke Zhiqian Wu

Global Customer Relationship Management Program Manager
Nissan Motor (GB) Ltd

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• How predictive analytics and generative AI are being applied to customer engagement, lead scoring, and forecasting

• Lessons from scaling personalization initiatives across web, offline journeys, and omnichannel communication

• Opportunities and challenges for moving from ML-based personalization to agentic orchestration in the automotive sector

Zeke Zhiqian Wu

Global Customer Relationship Management Program Manager
Nissan Motor (GB) Ltd

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• Delve into the often-overlooked

challenge of preparing enterprise

data for LLM-based applications —

from curating SharePoint archives

to structuring domain-specific

knowledge bases.

• Share practical approaches to

separating outdated, conflicting,

or role-inappropriate content

and aligning data pipelines with

retrieval-augmented generation

(RAG) architectures.

• Debate ownership models, the

role of decentralized teams, and

emerging practices like data

contracts and SLAs in the age of

GenAI democratization

Maximus Ndaboka

Global Head of Master Data
Lindt & Sprüngli

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• Preparing and formatting internal content for AI systems

• Challenges with hallucinations and prompt tuning

• Demonstrating ROI and ongoing optimization of GenAI tools

Dimitri Metzger

Digital, Data & IT Business Partner for Development & Medical Units & AI
Merck

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• How finance and operations teams are moving from analytics pilots to scalable agentic workflows that support forecasting, demand planning, and supply chain decision-making
• Exploring real-world use cases in regulated industries—balancing automation, security, and confidentiality in AI-driven financial processes
• Lessons learned in governance, accuracy, and human-in-the-loop oversight, ensuring AI enhances strategic decision-making across the value chain

Eduard Serrahima de Cambra

Director Data Science
Novartis

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• Use of agentic systems to search, retrieve, and synthesize scientific literature in response to medical information queries or research hypotheses.

• Deployment of a modular platform that integrates licensed databases and external tools to validate or disprove scientific assumptions.

• Application of autonomous agents to orchestrate search, ranking, summarization, and evidence tracking in regulated research workflows.

Dimitri Metzger

Digital, Data & IT Business Partner for Development & Medical Units & AI
Merck

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• 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

Dr. Neda Naghshbandi

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

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• How AstraZeneca’s AI Development Agent (AIDA) integrates data products, mathematical models, and agentic reasoning to accelerate drug formulation and process design

• Step-by-step illustration of human-in-the-loop collaboration across modeling, simulation, and validation phases

• Lessons from scaling an agentic architecture built on MCP and A2A protocols — toward reducing development time by up to 50 percent

• Outlines the transition from manual capital market researchto AI-supported decisionmaking using Deep Research tools.

• Highlights regulatory, cultural, and data licensing barriers to deploying agentic AI in financial institutions.

• Explores the vision of AI-integrated team collaboration and real-time decision support through conversational interfaces.

Dieter Konrad

Head of Capital Markets Data Science
Union Investment Institutional GmbH

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• 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

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• Showcases real-world applications of agentic AI to optimize pharma and biopharma manufacturing processes.

• Explores autonomous agents for predictive maintenance, quality control, and supply chain coordination.

• Highlights challenges and solutions in deploying agentic systems within highly regulated manufacturing environments.

Ogsen Gabrielyan

Head of Data Science in the Global E&T
Boehringer Ingelheim Biopharmaceuticals

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• 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

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  ·      Quality of life is a complex, multidimensional concept that is difficult to continuously measure using conventional methods.

·       Vocal biomarkers offer a promising alternative because voice production is highly complex and sensitive to changes in physical and mental health.

·       A machine learning model was developed to analyze vocal characteristics and objectively assess quality of life.

·       Due to the complex data structure, a quantum-based machine learning model was also investigated to capture deeper patterns.

Michael Stivaktakis

Quantum & AI Researcher
Merck

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• 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

Product Owner Conversational AI (Voice & Chatbot)
Telefonica

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• Obtaining automated insights based on secondary data

• Challenges in a regulated industry

• Multi-agent approaches to optimize results

Carlos Martin

Head of Data Science
Boehringer Ingelheim

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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

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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

Jose Ramon Reboredo Mera

Head Data Analytics & AI
Grupo Pinero

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