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

• How can governance be embedded into platforms rather than enforced after the fact? 

• What does “good governance” look like in production AI systems? 

• How should German organisations balance compliance, innovation, and accountability? 

• Where are teams over-engineering controls and where are risks underestimated? 

• How do you govern AI when use cases evolve faster than regulation? 

• 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 

• What had to change in processes, not just technology, to deploy AI in production? 

• How were risk, compliance, and legal teams brought along the journey? 

• Where did expectations clash with reality? 

• What trade-offs were made between accuracy, speed, and explainability? 

• What would the organisation never do again? 

• 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

Discussion topics:

• Why data product strategies stall after early momentum
• Ownership: who is responsible for performance, adoption, and ROI?
• Balancing experimentation with governance and brand risk
• Embedding data into customer journeys, not just dashboards
• When central platforms help — and when they slow execution

• What operating models work in German enterprises? 

• How to balance autonomy with alignment across business units 

• The hidden costs of getting the model wrong 

• How operating models must evolve as AI matures 

• Signs it’s time to change your current setup 

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? 

Live Poll (5 mins): 

• What is the biggest blocker to scalable AI today — data quality, integration, governance, or ownership? 

• Where should enterprises focus first to unlock AI value in 2026? 

• How confident are you in your organisation’s current data foundations? 

• 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 AI informs or automates decisions, who is ultimately accountable? 

• How do organisations prevent “decision drift” between business, data, and IT? 

• What governance models clarify ownership instead of adding friction? 

• How should CDAOs work with legal, compliance, and business leaders on accountability? 

• Where have organisations already learned these lessons the hard way? 

• How AI maturity has evolved from experimentation to industrialisation 

• Where GenAI is delivering value today and where expectations are inflated 

• What agentic AI changes for governance, accountability, and risk 

• How leaders should assess readiness before scaling 

• What the next phase of enterprise AI will demand from CDAOs 

• Why do organisations have more data than ever — yet struggle to act confidently? 

• Where do dashboards help decision-making, and where do they actively slow it down? 

• Are OKRs and KPIs being misused as reporting tools rather than decision tools? 

• How can organisations design metrics that support trade-offs, not just monitoring? 

• What should leaders stop measuring altogether? 

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! 

• How data teams are evolving to integrate AI, analytics, and business strategy 
• Building agility without compromising compliance in German enterprises 
• Aligning data team structures with evolving corporate strategy 
• Lessons from organisations that successfully scaled data-driven operations

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. 

In this interactive session, attendees work in small groups on real-world AI investment scenarios, tackling trade-offs between speed, risk, and ROI. 

Live Poll (5 mins) 
• Which AI initiative should be prioritized: efficiency, innovation, or risk mitigation? 
• Centralized approval or decentralized decision-making? 
• Short-term vs long-term impact — which is critical in 2026? 

Group Hack (20 mins) 
• Define non-negotiable objectives for AI initiatives 
• Identify acceptable trade-offs between speed, risk, and scalability 
• Prepare a concise justification for executive leadership 

Debate & Compare (15 mins) 
• Groups share outcomes 
• Discuss assumptions and differences in decision-making 
• Highlight what leadership would challenge first 

Key Takeaways (5 mins) 
• Common patterns and pitfalls 
• Practical frameworks for prioritizing AI investments 

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 

Panellists: 

Panellists:

Francesco Di Costanzo, CEO – 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

• 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

Kolja Manuel Rodel, Head of Data and Analytics – RAMEDER

• 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

• What are the core principles of effective data management? 

• How can robust data management practices speed up digital transformation initiatives? 

• In what ways can data management enhance decision-making and drive business innovation? 

• What strategies can overcome common challenges in data management?