If you're leading AI adoption, workforce capability or digital transformation, this roundtable explores the gap between executive confidence and frontline readiness, and how organisations can build, measure and demonstrate workforce AI capability.

Sally-Anne Lyster
Chief People Officer
NSW Treasury 

Ben Satchwell
Head of Capabilities
Acorn  

• Gain clarity on emerging approaches to AI capability: Understand how other departments and agencies are defining AI fluency and what they're trialling.

• Benchmark workforce readiness perspectives: Explore how executive, manager and frontline views of AI readiness differ and what this means for implementation.

• Share practical challenges and lessons: Discuss with peers what's delivering results, what’s not, and where capability gaps are most visible in real-world settings.

Public sector investment in AI continues to grow, and the rules for governing it are established. Whether that investment pays off depends on the workforce: AI is moving into everyday roles used by people whose jobs weren't originally designed around it, and whether they're using it well is largely unmeasured. Completed training is still treated as proof that they can use it well. 

Acorn's 2026 State of Learning for AI Fluency Report shows the pattern: executives are confident, the frontline far less so, and managers are widely expected to lead AI adoption being equipped to. This roundtable brings senior leaders together on what comes after rollout: moving from counting completions to evidencing what people can demonstrably do with AI, and safely tying usage and capability to performance and service outcomes. 

Key discussion points:

• How organisations are defining AI fluency at role level: What approaches are emerging to describe and understand AI capability in a way that reflects real work requirements, including how roles themselves are being redesigned around human–AI collaboration.

• The role of managers in enabling AI adoption: How managers are supporting (or constraining) the translation of AI expectations into day-to-day practice.

• Where measurement of capability is currently falling short: Why traditional indicators such as training completion don’t provide the full picture of AI applied in practice, and what better evidence looks like.

• Linking AI adoption to operational and business outcomes: How agencies are linking AI capability to service delivery, workforce performance, and productivity.