CDAO Germany schedule

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know.  Have fun! 

• Why data readiness, not model quality, is the real bottleneck to scaling AI 
• What AI-ready data actually means in federated European organisations 
• How geopolitical fragmentation increases the need for sovereign, execution-grade AI systems  
• Why most governance fails—and how to move from static control to execution enablement 
• What CDAOs must prioritise in 2026 to build resilient, reusable AI foundations 

• How AI maturity has evolved from experimentation to industrialisation
• Where GenAI is delivering measurable value today and where expectations are inflated
• What agentic AI changes for governance, accountability, and enterprise risk
• How organisations drive adoption by redesigning roles and workflows and upskilling the business
• What the next phase of enterprise AI will demand from CDAOs in org design, team integration, and platform/vendor readiness

Details coming soon!

• Where AI strategies most commonly break down between design and execution 

• Organisational, data, and governance challenges that are underestimated early on 

• Aligning ambition with real capabilities, timelines, and constraints 

• What companies wish they had known before launching their AI strategy 

Panellists: 

Maximilian Ehrlich – Director Data & Insights – Tonies 

Florian Leser, Executive Advisor/ Former Head of Data Analytics and AI – KKH 

Dr. Shivaji Dasgupta, Non-Executive Director (Technology, Data and AI) - STATE STREET 

• Why organisations still struggle to translate customer data into measurable revenue: Customer single source of truth (omni-channel), where is it, CDP, data warehouse?
• Aligning marketing, product, and data teams around shared commercial metrics, What are the ’shared commercial metrics’ - vs. Marketing ROI, product ROI
• Building data products that business teams actively use
• 4. Governance, consent, and trust as enablers of value creation, GDPR, Digital Omnibus, AI Act, GPC, where are we? How do we do the right thing to retain consumer trust and at the same time help achieve our commercials goals using data?

Do you trust the data, is it reliable?, How and where do you ‘activate’ the data to yield commercial results? How do you measure the success?

Back to the ’single source of truth’ - do you use the same tool to measure the success of common goals?

Moderator: Marie Fenner, Global Senior Vice President, Analytics - PIANO

Facilitators:

Alexandra Rahe, Team Lead Customer Analytics - LUFTHANSA

Pankaj Naryal, AI & Digitalisation Strategy Manager - HUHTAMAKI

Unlike visible failures such as plane crashes, AI risks are often invisible. With the EU AI Act and other emerging legislation, AI is increasingly required to meet minimum standards for quality and safety — making proactive risk management essential. Organizations operating in regulated environments must systematically identify, assess, and mitigate AI risks, leveraging standards, transparency, and education to build trustworthy, high-quality AI products.

This talk will include:

• How organizations can understand the emerging legal landscape and proactively integrate regulatory requirements into their AI strategy
• Best practices for governance, risk management, and quality management
• Building a healthy compliance culture
• Seeing compliance not as a constraint, but as a competitive advantage

Investing in AI governance and cybersecurity builds market trust, opens new opportunities, and demands a cultural shift — away from the "move fast" era toward interdisciplinary collaboration, resilience, and a governance mindset that coexists with innovation.

In this interactive session, attendees explore the real challenges of building data-driven organizations. Through guided discussion and a live poll, participants will tackle barriers to scaling data initiatives, benchmark their strategies, and walk away with actionable insights. 

Discussion Topics: 

• 
  Why is a reliable data foundation still so hard to achieve at scale?
  • What does “good enough” data really mean for decision-making and AI? 
  • How can traditional enterprises overcome legacy system inertia? 
  • What role do leadership commitment and organisational culture play in long-term success? 
  • Where do data initiatives most often fail — technology, people, or governance? 

Live Poll (5 mins) 

• 
  Which barrier is most critical — data, culture, or governance?
  • Where should focus and investment be prioritised in 2026? 
  • How confident are you that your data strategy will deliver measurable impact this year? 

Florian Leser, Executive Advisor/ Former Head of Data Analytics and AI - KKH 

Dr. Katharina Behme, Director Analytics & Insights – LUXEXPERIENCE 

• Why strong platforms still fail to drive adoption and impact 

• The critical success factors behind high-performing data teams 

• Building the right competencies across technical and business roles 

• Working with shared principles instead of rigid rules 

• How to shift mindsets, not just tools 

In this interactive discussion group, participants explore the data foundations required to scale AI and analytics in complex enterprises. Through guided peer discussion and live polling, attendees will unpack where data initiatives break down, compare approaches to data management and governance, and identify what truly enables trusted, reusable, AI-ready data at scale. 

Discussion Topics: 

• Why do data foundations still struggle to scale in large, federated organisations? 

• What does “fit-for-purpose” data mean in practice for analytics and AI use cases? 

• How can organisations balance decentralisation with consistency and control? 

• Where do data quality, integration, and governance most often fail to keep pace with AI ambition? 

• What foundations must be in place before scaling AI beyond pilots? 

Rebeca Meyer, AI Strategist – KNAUF

Naveen Kanneganti, Global Lead Data & AI Enterprise Architect Technology & Engineering – EON

• Managing risk and quality while accelerating AI and analytics delivery
• Why traditional QA models struggle in data and AI environments
• Continuous assurance as an enabler — not a blocker — of innovation
• Aligning data, analytics, IT, and business teams around shared accountability
• What “confidence at scale” really looks like in complex enterprises

• When an AI decision causes financial or reputational damage — who is responsible?
• Why “human in the loop” often fails in practice
• The hidden gap between legal accountability and operational ownership
• What boards and regulators now expect from CDAOs
• Real cases where accountability broke down — and what changed after

Moderator: Krisztina Pinter, Head of Global Service Planning and Data Governance – SONY

Pankaj Naryal, AI & Digitalisation Strategy Manager – HUHTAMAKI

Veera Babu Manyam, Global Enterprise Architect – EON

Discussion Points:

• Why data teams fail without the right culture
• The human side of data transformation
• Internal branding: positioning the data team as a business partner
• From data experts to business influencers

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know.  Have fun! 

Capital Market is navigating a structural inflection point. Margin compression, regulatory fragmentation, geopolitical volatility and AI-driven technological disruption are no longer isolated pressures — they are converging forces reshaping capital markets.

• In this environment, data strategy is no longer a support function. It has become a central capability enabling firms to translate strategic intent into operational performance and sustainable advantage.
• Yet many organisations remain trapped in incremental fixes: siloed data domains, manual reconciliations, duplicated controls and fragmented ownership. The consequences are familiar — operational friction, slower product launches, data quality issues, AI initiatives stalled by data lineage gaps, etc.

To drive consistent business impact, data teams need more than just a modern stack — they need a clear, structured operating system. In this session, Pier Martin will explore how embedding human-centered principles and soft skills into your data culture can radically improve ROI. From real-world day-to-day team challenges to practical leadership strategies, he’ll offer insights and immediate actions for building stronger, more effective data functions. 

• Data Teams Need an Operating System—Not Just a Stack: Why focusing only on tools and technology misses the mark, and how process, mindset, and communication routines create consistency across data functions. 

• Soft Skills as a Multiplier for ROI: How skills like storytelling, empathy, and influence-building amplify technical output and help data teams embed more deeply into business strategy. 

• Prioritization as a Leadership Capability: Why leaving prioritization to tooling or backlog grooming is a mistake—and how data leaders can guide their teams by aligning decisions to business impact. 

• From Insight to Action: Common roadblocks data teams face and practical changes leaders can implement immediately to improve alignment, output, and morale. 

• Data should not lose its business context and semantics when it leaves the ERP context
• Fragmented, re-modelled data creates inconsistencies and breaks integrity
• Impact on AI: loss of business context limits AI effectiveness and scalability - AI models can't deliver value if they don’t have access to unified, context-rich data
• Integrated data platforms share data without loosing the business context and ideally support zero copy data sharing
• Business data delivery via (managed) data products guarantees consistency, reusability, and trust in data across the organization
• The role of SAP’s Business Data Cloud and other enabling technologies in that context
• Empowering business units to access and analyze data
• SAP's own "data for all" approach as an example
• Use cases for data architectures that allow scalable AI (predictive analytics, process automation, agentic AI)
• Leveraging SAP’s ecosystem (integration with Microsoft, Databricks, Snowflake, etc.)
• Real-world examples of increased productivity and efficienc

Join an interactive session with a seasoned leader at the intersection of strategy, finance, data, and cybersecurity.  With extensive experience driving growth and digital transformation across sectors—including digital business, Industry 4.0, the public sector, blue chips, and scale-ups—this expert will share practical guidance on leveraging data strategically, aligning with compliance requirements, and strengthening cyber-resilience. 

Key Focus Areas: Data-driven ROI, data strategy in regulated industries, cyber-resilience, digital transformation, AI-driven innovation, and the socio-economic impact of digital business models. 

• Identifyingwhich metrics and insights truly drive decisions 
  • Overcoming silos to enable cross-functional collaboration 
  • Aligning governance frameworks with business priorities 
  • Ensuring adoption and accountability for data-driven decisions 

 Wayne Cavanough, CTO & CIO – JAJA FINANCE 

Dmytro Pavlichenko, Manager Data Analytics – AUTODOC 

Abdul Maten Khan, Group Head of Data Analytics – DISH DIGITAL  

Agathe Chomel de Jarnieu, Director of Analytics Europe – AUTO1 GROUP 

Florian Hottner, Senior Director, Marketing Data, Analytics & Insights – PENGUIN RANDOM HOUSE 

Kavitha Chetana Didugu, Functional Lead (Applied AI Science) - ZOOPLUS 

• Monetising data while maintaining trust, quality, and compliance
• Turning data investments into measurable business outcomes
• Nothing comes for free – keeping an eye on the costs
• Aligning commercial ambition with operational and data reality

Moderator: Kolja Manuel Rodel, Head of Data and Analytics – RAMEDER

Pankaj Naryal, Scientific Researcher AI & Strategic Management - HUHTAMAKI

 Mariam Yakub, Master Data Manager – SWISSLOG  

• Challenges of working with complex, domain-specific data environments
• How AI agents can help democratise access to data and insights
• The importance of strong data foundations to enable agent use cases
• Practical learnings from a Bundesliga case study
• Key takeaways for data leaders looking to operationalise AI agents
  
  

• What really breaks once AI models are live — data drift, model decay, and operational blind spots 
• How organizations detect failure early before business impact escalates 
• Who owns remediation when models behave unexpectedly? 
• Balancing transparency, explainability, and speed when things go wrong 
• Lessons learned from AI incidents that forced course correction