The integration of AI into government organisations isn’t just a tech upgrade; it’s a profound shift with significant implications for workforce planning, job design, job satisfaction and operating models. As public sector agencies increasingly explore AI’s potential, strategic workforce planning must evolve to optimise productivity, nurture talent, and address the changing nature of work.

As small and medium-sized agencies align with the Whole of Government Data Strategy, the path to compliance can feel overwhelming.

Without governance, shadow AI can lead to data leakage, inconsistent outputs, and compliance failures. Worse, it can create a dependency on tools that may not be secure, auditable, or even available tomorrow. Just as agencies once had to audit and replace rogue Access databases, they’ll soon need to audit AI usage.

AI tools vary widely in complexity. Basic AI, like summarising emails or generating meeting notes, can be deployed with minimal preparation. These tools are often plug-and-play, requiring little more than user access and a browser. But as agencies move toward more sophisticated AI agents – those that make decisions, interact with systems, or handle sensitive data – the stakes rise dramatically.

The promise of artificial intelligence in government is compelling: faster services, smarter decisions, and lower costs. Unfortunately, a lot of these benefits are broadly distributed, and it’s pretty hard to harvest 13 minutes a week from each employee. To realise financial benefits agencies must deliberately harvest from specific use cases, and use these savings to fund broad AI adoption.

As AI agents become more capable and more embedded in government workflows, we need to rethink how we manage them. The best analogy? Treat AI teams like human teams.

We are pleased to share that ConceptSix has expanded our paid parental leave policy, offering 26 weeks of paid leave for primary caregivers and 16 weeks for secondary caregivers.

When people think about AI adoption, they often focus on the technology: the models, the data, the integrations. But in practice, the biggest barrier – and the biggest opportunity – is people. Change management and training aren’t just support functions, they’re core to AI success.