Professor Jon Whittle joins 'Future of Work: Australia' to reveal why AI success depends on strategy, culture and bold leadership (not clever prompts).
Luke Bebbington
Industry Advisor, Financial Services
Workday APAC
Audio also available on Apple Podcasts and Spotify.
Long before ChatGPT became the fastest adopted technology in history, my guest on 'Future of Work: Australia', Professor Jon Whittle, was at CSIRO using AI to help preserve the Great Barrier Reef and predict the path of bushfires in real time.
The author of AI for Business says that, for decades, “There was a lot of AI everywhere. But it was for very specific applications where you had good quality data – and good quality expertise within your organisation to actually build these systems.”
Then Generative AI changed the game. We went from AI being used by scientists in niche applications to AI being used by everyone, everywhere in your organisation. Professor Whittle believes it's presented a new challenge, as he asks: “How do you get proper ROI from your use of AI?”
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Professor Whittle says the term ‘artificial intelligence’ conjours up images of human-like intelligence, but, “It's really not. I think it's important to understand that underneath it all – it's statistics.” He likes the way psychologist and philosopher, Alison Gopnik, frames AI as ‘a large-scale statistical pattern matching engine’.
That’s not to say he’s downplaying the capabilities of generative AI. “They’re absolutely phenomenal. When I started 30 years ago, I could never have imagined that we would get to where we are at now. But it is a machine at the end of the day, and I think we should always remember that.”
I like the way he illustrates this idea in an article with Stefan Hajkowicz for The Conversation. If you drop advanced AI into a dumb organisation, it won't make it smart. It'll just help the organisation do dumb stuff more efficiently.
Professor Whittle believes those obsessed with AI as an efficiency tool are missing the point.
"People think, 'We've got some inefficient processes, let's use AI to make those processes faster.' And you can absolutely do that," he says. "But you're not really going to fundamentally change your business because everyone's doing that. So you won't get any competitive advantage."
Instead, he encourages leaders to start somewhere else entirely: their business strategy. “Can AI enable us to do things that we weren't able to do before? Things that will lead to competitive advantage? That will lead to growth and new revenue streams?
This is how AI stops being a productivity initiative and starts becoming a growth strategy.
"The key point is that you need to be deliberate about where you are now and where you want to be."
Building on this idea, Professor Whittle cautions leaders to resist the temptation to jump straight into AI use cases. "It amazes me how many organisations skip a step," he says. "They go straight into, 'Let's develop some AI use cases.'"
The result is hundreds of disconnected ideas competing for attention, with no clear way to decide which ones matter most. Instead, he argues organisations should first answer a much simpler question: What is our AI ambition?
Professor Whittle describes three broad approaches:
Importantly, he doesn't argue that every organisation should aspire to be an AI disruptor. "The key point is that you need to be deliberate about where you are now and where you want to be."
That clarity is critical because it shapes every decision that follows. Without it, different parts of the organisation will pursue different objectives. Some will push for transformation, others for incremental improvement.
Without a commonly agreed and understood ambition, progress quickly stalls.
From his studies of large-scale enterprise AI deployments around the world, Professor Whittle has distilled the findings into an AI diagnostic that measures organisations across 14 success factors.
I was fascinated to hear that most have nothing to do with technology.
The principles of managing risk, assigning accountability and maintaining effective oversight remain the same.
"The vast majority of those factors are organisational change management, leadership and culture," he says. "In many ways that's a good thing because all that stuff's not new. We're used to that."
I found that reassuring. Amid all the excitement around rapidly evolving AI models, it's easy to assume success depends on mastering the latest technology.
But Professor Whittle argues that the organisations getting the most from AI are the ones that excel at already established skills like leading people through change or executing good governance.
As he says, the principles of managing risk, assigning accountability and maintaining effective oversight remain the same. What's changed is the pace.
AI systems evolve rapidly, models change, and governance needs to keep up. So organisations will need to review risks more frequently and respond much faster than they have in the past.
Professor Whittle also challenged the way many organisations are approaching AI training.
Too much of it, he believes, focuses on teaching people how to use today's tools. How to write prompts or get better outputs. While those skills have their place, they're not where the biggest opportunity lies.
His practical advice is to experiment. Rather than waiting until you've mastered the latest prompt techniques, start using AI in your day-to-day work. Test ideas. Learn what works. Adapt as the technology evolves.
"Training is not the thing," Professor Whittle says. "It's experimentation that you need to do. Just try things out, see what works, what doesn't, and you'll learn much quicker than if you do a one-day training course."
He says this is important for everyone in the organisation, but especially for leaders.
“Nowadays, in a world of AI, every leader is an AI leader," he says. "This is not something you just give to the IT department and let them deal with. You've got to lean in on this – 100%.”
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