Building workforce capability in the age of AI

What the session covered

A real-world case study: Creative Australia's capability rebuild

Creative Australia's People & Culture team operates with what they call a diamond-shaped structure: deep specialist expertise concentrated in a small team, rather than a traditional pyramid with layers of generalists supporting senior leaders. It's an efficient model when it holds, but it also means a single gap in specialist capability can slow an entire function to a crawl.

The organisation had grown quickly, hiring strong people into specialist roles without necessarily building the support structure around them, and reached a point where workforce capability needed deliberate attention rather than reactive fixes. The session walked through how that risk showed up in practice, what prompted the investment, and how the programme was sequenced in its first year, detail that's genuinely useful if you're weighing up a similar case internally.

Rethinking how skills gaps get identified

The conventional approach to skills gap analysis is a detailed matrix mapping every possible gap against every role. It looks thorough on paper, but in practice it tends to overwhelm the business and the people expected to act on it. Complexity, more than a lack of data, is often what stalls action once gaps are identified.

The panel discussed a lighter-weight alternative: how to group roles sensibly, where to pull workforce signal from beyond formal skills audits, and how to sequence a rollout so it builds momentum instead of stalling under its own weight. There's also a data point from recent research on what employees themselves now expect from workplace learning, which reframes the whole conversation around where development needs to sit day to day.

Making AI readiness practical, not overwhelming

For most mid-sized organisations, AI readiness isn't held back by a lack of ambition, it's held back by not knowing where to start with limited time and budget. The session set out a practical, low-resource sequence for building AI readiness across a business: what to assess first, what baseline literacy actually needs to cover, and how to choose a starting point that shows value quickly rather than trying to move the whole organisation at once. It's a deliberately unglamorous approach, but one built for organisations that don't have a dedicated AI transformation team to lean on.

Measuring whether AI is actually helping

It's easy to assume AI is working simply because things feel faster. The panel pushed back on that instinct, making the case that the real test is speed to outcome, not speed of activity, and walked through a concrete example of just how much manual effort a skills taxonomy project can involve without AI support, and how that changes with it.

They also touched on which skills remain in highest demand regardless of how much AI gets adopted, a useful check for anyone building a capability plan around the assumption that AI changes everything.

Building the case for leadership investment

Getting leadership buy-in for workforce capability work doesn't have to start with a large learning and development budget request, and for many HR teams, that's the biggest blocker to getting started at all.

The session explored a better way to present the request by focusing on organisational needs and employee benefits instead of the budget. It also provided a practical understanding of how long it takes to achieve measurable results, based on the foundations already in place.

It's a useful reference point if you're building a business case of your own.

Want to dive deeper? Watch the full recording above, or get a copy of our practical guide to workforce capability.