The surge of artificial intelligence into the public consciousness, with applications like ChatGPT and Copilot, has signalled an opportunity to rethink whole industries and unlock the economic potential of these powerful technologies, AI experts say.

For Dr Tim Fountaine, who established McKinsey’s QuantumBlack AI consultancy in Asia, a vastly transformed future is something Australian businesses need to be prepared for, as he sees gaps widen between digital leaders and laggards.

“As you think about where this all might go, you have to contemplate futures that are very, very different from today,” says Fountaine, whose qualifications include a PhD in neuroscience and a medical degree. “That change is going to be far faster than anyone is expecting.”

Fountaine was speaking as a special guest at NAB’s recent Transaction Banking customer event series for 2025, as part of an agenda exploring the latest payments innovations and advances in treasury services – areas where AI is already playing a role.

He says the past decade has seen remarkable progress in AI capabilities relative to human performance across a range of metrics – from language comprehension and generation to solving milestone maths problems and for advanced predictive reasoning.

Unlocking potential

The rapid rate of progress has surprised even the experts until recently, Fountaine says, and predictions now say generative AI will accelerate the automation potential to unlock trillions of dollars over the global economy[i].

An update on a McKinsey Global Institute analysis of the future automation of work has the midpoint of predictions now showing more than half of all time spent on work activities done today could be automated by AI as soon as 2045, accelerated by the latest developments[ii].

Fountaine caveats this acceleration of a highly automated future is one of several possible outcomes with many variables, but says it underscores this material effect AI will have during the careers of most Australians working today.

His main point for business is that AI is set to amplify the gaps already seen through technology adoption, and organisations need to restructure processes, workflows and even entire product offerings for success in this economic future.

“Pick one or two areas of the company to focus on that are absolutely vital,” Fountaine says. “Maybe it’s a bottleneck, or what’s most crucial to improving your customer outcomes, or your strategy versus your competitors.

“Concentrate your efforts on these areas and really think how would you re-engineer what’s there. It’s not just about sticking AI on top.”

Playbook for change

To deliver this, organisations firstly need to have a positive environment for technology and talent – including upskilling and retraining for staff – and an operating model where digital, business and product can all work together.

Harnessing the data used in the applications is by far the slowest part of most AI projects, Fountaine says, and so making this information reusable is vital: “If you can produce data in a way that allows multiple uses, it just saves so much time and money.”

The final pillar is around adoption and scaling, he says, noting McKinsey surveys suggest companies spend about half their AI project budgets on the change management phase – from process redesign to communications and training.

Survey data also shows a clear relationship between tech savvy executive teams and business success. He calls transformation “the ultimate corporate team sport” taking in the C-suite to department heads, finance, human resources, technology as well as legal, risk and compliance.

“The more of the executive team that understands this technology, the better companies tend to do. We all need to get smart on this, we can’t just leave it to the technology people or the digital people.”

Future skills

As far as successfully working with AI as a tool, he says the next generation needs to have a solid grounding in core skills of maths, writing, critical thinking and coding.

“Our job will become much more about defining objectives and questions. How do you break a problem down into its constituent parts so that you can then have AI work on each piece?”

He says understanding coding processes and the logical thinking this encourages remains key to ensuring it is the human driving the AI and not the other way round.

“AI is not right all the time. Kids need to be able to understand that and think for themselves. The first thing we need is really learning to love learning. To be able to go out and find information, put it together and come up with your own perspective. It’s important to understand that.”

[i] the-economic-potential-of-generative-ai-the-next-productivity-frontier.pdf and The State of AI: Global survey | McKinsey

[ii] McKinsey Global Institute analysis