Why Attend?

Despite efforts to reform, central government still operates largely in departmental silos, limiting opportunities to deliver joined-up, efficient systems. With pressure mounting to do more with less, 2026 marks a shift toward converged operating models and cross-government collaboration. Yet barriers remain around data-sharing, supplier management and investment decisions. This session brings together leaders to explore how government can move from isolated initiatives to a system-wide approach. What practical steps would enable data sharing and align strategy across departments? 

• How can government overcome cultural and organisational barriers to effective data sharing?
• What does a realistic, joined-up operating model look like across departments?
• How can shared services reduce duplication while respecting departmental autonomy? 

AI adoption across central government has moved from pilot projects to an operational expectation. But beneath the surface, a harder question is emerging: are we truly transforming services, or simply layering AI onto systems that aren’t fit for purpose? This keynote panel challenges the narrative of progress and asks leaders to confront the gap between ambition and delivery. With growing pressure to demonstrate value under tight fiscal conditions, departments must move beyond proof-of-concept and into measurable, citizen-facing outcomes. This session will examine what is actually working, what is falling behind, and what must change to deliver AI at scale. From data quality and legacy constraints to governance, procurement, and accountability, speakers will explore the ways government is structurally ready for AI, and what it will take to get the rest of the way there.

• How are departments moving from pilots to scalable, outcome-driven AI adoption?
• What governance models enable safe, but agile, AI deployment?
• How can policy, procurement, and delivery functions align to avoid duplication?
• How are legacy systems being addressed to ensure AI tools are effective?

Trust has become the defining measure of success for digital government, but it cannot be designed in, it must be earned through outcomes, transparency, and data integrity. As AI-enabled services and digital identity programmes expand, central government faces growing scrutiny over how citizen data is used, shared, and protected while delivering results that lead to meaningful improvements in citizens’ lives. At the heart of this is data: its quality, its governance, and how confidently it can be used to drive fair, inclusive, and accountable decisions. This discussion will explore how departments can better align service design, data strategy, and delivery to create services that citizens not only use, but trust.

• How is poor data quality undermining citizen experience and confidence? Do citizens understand and trust how their data is used?
• Are digital-enabled transformation efforts delivering measurable improvements in outcomes, particularly for underserved groups?
• How can government use data to drive efficiency while maintaining accessibility, transparency, and public trust?