CDAO UK 2027 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! 

• How is the CDAO mandate changing as data, analytics and AI become inseparable from enterprise strategy?
• Where should data leaders focus when boards want faster AI results, but the foundations still need investment?
• What capabilities does a CDAO need to become a business leader who happens to lead data, rather than simply a leader of the data function?

• Scaling AI beyond pilots and individual use cases
• Connecting data foundations, AI adoption and business value
• Embedding AI into the way the enterprise operates

 Keynote speaker: Thomas Geerinck, Groupwide Retail Chief Data Officer & Head of Data & AI, ING

• Which changes to data, technology, governance and operating models matter most as AI moves into core workflows?
• Where are organisations discovering that legacy processes, rather than models, are the real barrier to scale?
• How should responsibilities be divided between the CDAO, CAIO, CIO, risk leaders and the business?
• What have leaders learned from moving from experimentation to production?

• Turning AI and data investment into scalable, profitable growth
• Finding the use cases capable of delivering material enterprise value
• Scaling AI without scaling cost, complexity and risk
• Converting productivity gains into better customer and financial outcomes

Speakers:

Ari Cohen, Chief Data & AI Officer, EMEA and Americas, Macquarie Bank UK

Christoffer Kanstrup, Chief Data Officer & Head of Data & Analytics, Danske Bank

Jen Courant, Chief Data Officer, DWS Asset Management

• Creating trusted data at source rather than continually repairing it downstream
• What does end to end lineage need to capture as data feeds analytics and AI?
• Sharing ownership between producers, consumers, data teams and the business
• Can observability and automation make data quality more proactive?

• Moving from data management activity to measurable business impact
• Quantifying efficiency gains from better managed data
• Building a credible ROI case for foundational data investment
• What does the business actually value?

Speaker:

Geoffrey Van Ijzendoorn-Joshi, Head of Data, Møller Mobility Group

• Turning regulation, privacy and responsible AI principles into practical controls
• What should be governed centrally and where should teams have freedom to innovate?
• Using automation and evidence trails to reduce governance friction
• Staying ready for changing AI requirements without freezing innovation

Speakers:

Edmund Towers, Head of AI Product Delivery, Financial Conduct Authority

• Why do promising AI proofs of concept so often stall before reaching enterprise scale?
• Building portfolios of AI initiatives rather than disconnected experiments
• Moving AI out of the innovation team and embedding it into everyday business processes
• Lessons from failed deployments every AI leader should know

• Deciding what capability genuinely needs to be built in house
• What an effective AI vendor evaluation process should test
• Governing vendors as products, models and capabilities continue to evolve
• Matching the partner strategy to organisational maturity, scale and existing technology

• Central AI function, data, technology or the business: where should ownership sit?
• Drawing the line between centralised capability and federated delivery
• Avoiding duplicated platforms, fragmented teams and competing AI strategies
• Creating clear accountability without rebuilding a central bottleneck

Speakers:

Aditya Sharma, Head of Engineering - Data Platform and Analytics, Tide

• Will the boundaries between banks and financial providers disappear?
• Could banking become a marketplace rather than a single institution?
• How will AI and technology reshape financial products and delivery?
• What will customers expect from the next generation of banks?
• Which parts of today’s banking model will no longer exist?

Moderator: Vladimir Bendikow, Chief Data Officer, First Bank

• Modernising legacy data without destabilising business critical processes
• Which workloads should be migrated, rebuilt, wrapped or retired?
• Reducing technical debt while improving AI readiness and interoperability
• Lessons from large scale cloud, platform and core system transformations

• Moving analytics from explaining what happened to recommending what happens next
• Where can decision intelligence have the greatest impact on business performance?
• Combining domain expertise and business context with model driven recommendations
• Building trust, explainability and accountability into AI supported decisions

Speaker: Elena Konovalova, Head of Data and Analytics, Paysend

• Connecting fragmented customer data while respecting consent, privacy and purpose
• The role of MDM, identity resolution and real time data in creating a usable customer view
• Personalisation without crossing the line into intrusive or untrusted use
• Where is better customer intelligence creating measurable value?

Speaker: Tom Puffitt, Head of Data, Audley Travel

• Where AI is augmenting, fragmenting or replacing knowledge work first
• Redesigning roles and workflows around human and machine strengths
• New expectations for managers leading human AI teams
• Preserving judgement, expertise and accountability as automation increases

Speakers:

Pedro Liaño, Chief Data & AI Officer – Fractional, Beyond Inc.

• Starting with the business process rather than the AI capability
• Redesigning roles, decisions and handoffs around AI
• The operational changes required before the technology could deliver value
• What the organisation learned once employees actually started using it

• Identifying workflows where agents can genuinely outperform simpler automation
• Moving from assistance to execution without jumping straight to full autonomy
• Designing approval, supervision and exception handling around different levels of risk
• What production grade agent orchestration looks like beyond the demo

• Why do AI systems struggle when business knowledge is scattered across documents, systems and people's heads?
• How can semantic layers, ontologies and knowledge graphs make enterprise context machine-readable?
• What role should unstructured data play in the enterprise AI strategy?
• Who should own and govern the context that agents use to make decisions?

Speaker: Mikhael Mazu, VP, Head of Metadata Management, Deutsche Bank  

• Moving from project by project delivery to reusable, governed data products
• Who owns the product, service level and business outcome?
• Using data contracts and metadata to improve reliability
• Avoiding hundreds of "data products" that nobody actually uses

• Which skills become more valuable as AI changes data and analytics work?
• Balancing central expertise with domain led delivery
• Building data and AI literacy beyond the data function
• Preserving expertise and institutional knowledge through rapid technological change

• Where should CDAOs place their biggest bets over the next 12–24 months?
• What should remain firmly owned by the CDAO as Data and AI responsibilities converge?
• What are the hardest decisions CDOs are facing around value, governance, architecture, talent and AI?
• How does the CDAO mandate need to evolve as responsibility for data and AI spreads across the enterprise?

• When models developed in one market meet different customers, cultures and behaviours
• Separating genuine predictive signals from historical processes and interventions
• Using local context without encoding stereotypes or unsupported assumptions
• What global organisations need to understand before models travel

Speaker: Dr Justine Dattani, Chief of Strategy & CTO, RM1

• How do cultural and market differences affect model performance and risk?
• What happens when historical behaviours become tomorrow’s training data?
• Why successful models fail when transferred across markets or organisations
• How can you assure third party models and data built in unfamiliar contexts?
• When is a model technically sound but wrong for the environment?

Moderator: Dr Justine Dattani, Chief of Strategy & CTO, RM1

• Proprietary data, distribution, workflow integration or organisational speed: where advantage is actually emerging
• Capabilities becoming table stakes versus those that remain genuinely differentiating
• The threat from AI native challengers unconstrained by legacy technology and operating models
• What should organisations be building now rather than trying to predict for 2030?

Speakers:

Karol Tajduś, Chief AI Officer, Bank Pekao

• The developments reshaping the CDAIO agenda after another year of rapid change
• Where does enterprise AI deliver real value and where have expectations fallen short?
• The capabilities becoming essential as data and AI move deeper into the enterprise
• Priorities for leaders preparing for the next wave of change

• Which decisions and processes are moving from human-led to machine-executed?
• Deciding where autonomy is appropriate and where it is not
• What new operating, risk and governance models emerge as AI becomes a digital workforce?
• How can leaders preserve accountability when decisions happen across complex agent chains?

• Leading with clarity through rapid technological and organisational change
• Understanding how different generations respond to change, autonomy and leadership
• Bringing diverse generations, disciplines and perspectives together around a common goal
• Lessons from high-performance environments that data and AI leaders can apply to their own organisations

• As AI accelerates decisions, how valuable will trusted data become for the enterprise?
• How can organisations measure trust across quality, provenance, security and explainability?
• What happens commercially when confidence in AI-driven decisions is lost?
• Can trust become a source of competitive advantage rather than simply a compliance requirement?

• How can governance help teams use data and AI faster rather than act as gatekeepers?
• Which controls should be embedded directly into platforms and workflows?
• How can policy-as-code and automation reduce manual governance effort?
• What does proportionate governance look like across different levels of risk?

Executive Panel | How do you create trusted data ecosystems without losing control?

• How can organisations share data across functions, partners and sectors without losing visibility or control?
• What role do consent, contracts, privacy-enhancing technologies and secure environments play?
• How should provenance and usage rights travel with data?
• Where can trusted data sharing create new services, competition and commercial value?

Speakers:

Sally Bashuan, Executive Director and Head of Global Data Governance, Federated Hermes

Discussion Group | Data products at scale: Transitioning ownership from the data team to the enterprise

• How do you make domain and business leaders genuinely accountable for data products?
• What service levels, contracts and measures should sit around reusable data assets?
• How should central teams support standards without taking ownership back?
• How can organisations measure whether data products are actually being reused and creating value?

Speakers:

Aditya Sharma, Head of Engineering - Data Platform and Analytics, Tide

• Moving from AI that generates to AI that plans, decides and acts
• Which GenAI foundations survive the transition to agentic systems?
• Sequencing adoption without chasing every new capability
• Preparing architecture, governance and people for greater autonomy

What does the AI first organization actually mean and how to go about it? Hear a practitioner perspective:
•    What does the AI strategy look like?
•    Doing the heavy lifting – the data layer
•    Practical strategies to drive cultural change
JP Bhamu, Director of Data & AI, NHS BUSINESS SERVICES AUTHORITY 

• Accountability when agents execute multi step tasks across enterprise systems
• Permissions, limits and escalation paths for increasingly autonomous AI
• Building audit trails that reconstruct what happened and why
• Decisions that should remain human regardless of model capability

Executive Interview | Beyond the data function: What does it take to lead as a CDAO today?

• How has the role of the data leader evolved from leading technical teams to influencing enterprise strategy, growth and transformation?
• What capabilities do today’s CDAOs need beyond traditional data and technology expertise?
• How do you identify where data and AI can create genuine business value rather than simply pursuing the next innovation?
• How should leaders balance innovation, investment and risk when priorities and technologies are changing so quickly?

Interviewee: Murtz Daud, Chief Data Officer, Director, Data and AI, British Gas

• How do organisations move beyond training courses to change everyday decision-making behaviour?
• What should leaders expect every employee to understand about data and AI?
• How can incentives, workflows and leadership behaviours reinforce data literacy?
• How do you measure whether literacy is translating into better decisions?

• Why does data ownership remain unclear in so many mature organisations?
• What authority and incentives do data owners need to succeed?
• How can ownership be embedded into business processes rather than governance committees?
• Where should accountability sit when data is reused by AI in ways the original producer never anticipated?

• The failures that only appear at enterprise scale
• Monitoring quality, drift, latency, cost and unexpected behaviour
• Managing model and platform changes without rebuilding everything
• Designing incident response when AI becomes part of critical workflows

Speakers:

Dr. Tony Wang, Lead Data Scientist, HSBC

• How will employees consume analytics when they can ask questions in natural language?
• Can agents move from answering questions to investigating and acting autonomously?
• What happens to semantic models, metrics layers and BI governance?
• How should analytics teams evolve as routine analysis becomes automated?

As AI moves from generating answers to taking action, where should organisations draw the line between machine intelligence and human judgement?

This debate will explore:

• Where should AI recommend, and where should it be trusted to act?
• Is human-in-the-loop a safeguard or a bottleneck?
• What shouldn't we automate? Identifying the decisions where context, empathy, accountability or experience still matter
• How do organisations gain the speed and scale of AI without creating a workforce that simply accepts what the machine tells them?

Speakers:

Tom Puffitt, Head of Data, Audley Travel

Tomáš Trnka, Chief Data Officer and AI Team Lead, EAG

• Moving governance out of policy documents and into the systems, workflows and decisions
• Connecting metadata, lineage, quality and ownership to create a usable control layer across the enterprise
• Automating controls and evidence without increasing bureaucracy
• Proving where data came from, who owns it, how it has changed and whether it can be trusted

• Is the CDAO becoming a business transformation leader rather than a functional data leader?
• How do leaders identify and prioritise the opportunities capable of creating genuine enterprise value?
• How do you balance innovation, investment and risk in an uncertain environment?
• What outcomes give a CDAO genuine influence with the CEO and board?

• A growing attack surface across models, prompts, agents, tools and enterprise data
• Protecting against prompt injection, data leakage, manipulation and agent misuse
• Monitoring autonomous systems without blocking legitimate adoption
• Preparing for failure when AI becomes embedded in critical processes

• Which capabilities disappear into everyday enterprise infrastructure?
• What changes when using AI is no longer an innovation initiative?
• Where does competitive advantage remain once everyone has access to similar models?
• The capabilities organisations need when AI itself stops being the differentiator