Keen to build your generative AI skills but not sure where to begin – or how to help your team do the same? I’ve just updated my AI Skills Learning Journey with a clear, flexible roadmap that covers both free and paid courses, from quick intros to full specialisations.

It’s based on 40+ courses I’ve taken over the last three years, and it maps out a smart route depending on how much time you’ve got and which AI tools you're using. Includes new government-funded options too – worth a look if you want to stay ahead.

Culture around risk and innovation varies not just by industry, but by geography as well. A particularly striking comparison is between Australian companies and their U.S. counterparts. Australian business leaders are often characterised (even by themselves) as more risk-averse and cautious in embracing change, whereas Silicon Valley lore celebrates “fail fast, fail often” as a path to success. These differences in risk appetite can significantly influence AI adoption on either side of the Pacific.

One of the biggest challenges in organisational change today is the mismatch between how fast technology evolves and how fast organisations adapt. We’re living in an age of exponential tech advances – generative AI being a prime example – but organisations (and humans in general) adapt on a more linear, incremental curve. As New York Times columnist Thomas Friedman observed, our ability to adapt has been surpassed by the rate of technological change​. In other words, the tech curve is shooting up like a rocket, while the human organisational curve lags behind. This gap can leave companies perpetually playing catch-up, and it’s a dangerous place to be.

No matter the industry, one universal truth remains: AI adoption is as much about managing change with people as it is about deploying technology. A shiny new AI tool won’t deliver value if employees don’t use it, or worse, actively resist it. That’s where change management comes in.