CDAO Government UK schedule

• How is the UK public sector moving from AI pilots to scaled services and productivity gains?
• What data foundations are required to make public sector AI reliable and reusable?
• How should central strategy translate into practical delivery across departments and local government?
• Where can data and AI create the greatest public value over the next three years?

Keynote Speaker: Feryal Clark, Former Parliamentary Under-Secretary of State for AI and Digital Government, Member of Parliament for Enfield North

• Why the AI revolution has made trusted data infrastructure an urgent priority
• Moving beyond access and permissions to establish rights, accountability and provenance
• Creating independent verification and audit through a statutory data asset register
• Moving data due diligence from a reactive exercise towards a proactive, trusted framework

• Giving citizens meaningful visibility into how their data is used, shared and reused
• Strengthening individual rights over digital identity, access and control
• Balancing data sharing and AI innovation with sovereignty, transparency and public trust
• Can citizens become participants in data governance rather than simply subjects of it?

Ott Velsberg, Client Engagement & Delivery Lead, Data, Tony Blair Institute for Global Change | Former Government Chief Data Officer, Estonia

Inga Rós Gunnarsdóttir, Chief Data Officer, The City of Reykjavík

Aga Strandskov, Head of Data Strategy, Digital Isle of Man

• Prioritising AI where it can deliver meaningful citizen and productivity outcomes
• Moving proven use cases from isolated success to public-service scale
• Scaling responsibly across diverse services, communities and user needs
• Measuring public value beyond financial ROI

• What does AI ready government data actually look like in practice?
• How can metadata, quality, standards and APIs make public data usable by both people and AI?
• How should structured and unstructured information be connected to give AI reliable context?
• Where can automation accelerate classification, cleansing, enrichment and stewardship?

Speaker: Raphael von Thiessen, Program Lead AI, Office for Economy, Canton of Zurich

• Democratising data and analytics by giving non-technical employees the skills and tools to build their own solutions
• Moving beyond traditional data literacy training towards practical, hands-on capability building
• Empowering employees to solve everyday operational challenges
• Scaling citizen analytics across the organisation while maintaining appropriate governance and support

Speaker: Dominique Kwiatkowska, Head of Data & Analytics, NHS SBS

• Moving beyond AI experimentation to deliver tangible improvements in public services and citizens' lives
• Using data and AI to address societal challenges, improve accessibility and reduce inequalities
• Balancing innovation with public trust, transparency, accountability and responsible AI
• Measuring public value: how do we know whether data and AI initiatives are genuinely making a difference?
• Sharing lessons across governments, public institutions and mission-driven organisations

Speakers:

Flo Albu, Director, Technology for Impact, and Chief of Staff to the Chief Impact Officer, CIFF

• What evidence should be required before an AI system is deployed into a public service?
• How should testing and assurance differ according to risk and citizen impact?
• What transparency, documentation and audit rights should government require from AI suppliers?
• How should public bodies monitor third party AI after deployment as models and systems change?

• Where can AI remove administrative work rather than simply make existing processes faster?
• Which repetitive, document heavy and rules-based workflows are the strongest candidates for automation?
• How can AI help the government simplify processes rather than automate unnecessary complexity?
• Where must expert judgement remain central?

• Which public sector decisions should never become fully autonomous?
• When does mandatory human review provide meaningful protection and when does it simply create another approval step?
• How should exception handling and escalation work as AI becomes more autonomous?
• What happens to accountability when a human technically approves a recommendation they cannot realistically interrogate?

• Which skills do civil servants and public-service leaders need as AI becomes embedded in work?
• How can the government develop internal capability rather than relying entirely on suppliers?
• What does practical AI literacy look like for non-technical teams?
• How should leaders address workforce concern while still accelerating adoption?

Speakers:

Jeremy Gould, Deputy Director, Public Sector AI Delivery, Government Digital Service

• How should the government procure AI technology that may fundamentally change during the life of the contract?
• What should public bodies demand around data access, interoperability, IP, transparency and exit?
• How can government work with innovative SMEs and emerging AI providers without increasing operational or supplier risk?
• How can procurement encourage innovation and competition while avoiding long term dependency on individual platforms or providers?

• 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