In early March, data from the Australian Bureau of Statistics showed something unexpected. After years of stagnation, Australia’s productivity is finally ticking up again. The surprising part isn’t the increase. It’s what’s driving it. Productivity or “gross domestic product per hour worked” was up one per cent over the year to December.
Why was this a surprise? Well, it was the first time since before the COVID pandemic that Australian productivity had risen at this rate. And what was driving this change? Research from the economists at CBA suggests that the increased use of AI could help grow the economy by three per cent annually by lifting Australia’s productivity.
The increasing benefits of AI on productivity at the macro-economic level are being realised only through thousands of AI initiatives across the breadth and depth of Australia’s economy. From small businesses starting to use GenAI across their operations to large organisations embarking on multi-million dollar legacy modernisation projects using agentic coding platforms.
Arguably, every industry has an opportunity to find a shortcut to enhanced productivity. However, the benefits must also be balanced against the pitfalls.
Take Australia’s $305 billion professional services sector, for example. This sector, which, ironically, is playing a major role in driving AI adoption, also faces massive waves of disruption as the nature of work is changing.
For professional services, the upcoming shift will not be due to AI alone, but due to inherent constraints within some of the sector’s existing business models. Time-based billing has traditionally aligned effort with value delivered. As AI becomes more embedded in delivery, that relationship may begin to evolve. Similarly, career pathways built on incremental experience and linear progression are likely to be influenced by these shifts. The entire sector is being reshaped, with questions around previous fundamentals such as time-based billing and career pathways.
Across all sectors, similar dynamics, albeit in various forms, will play out. AI will not always be responsible for the shift, but it will be the catalyst for this evolution. When it comes to “survival of the fittest” in productivity evolution, the winners will be those who orient themselves around the customer.
It sounds basic, but it is effective. At Synechron (now incorporating Chamonix IT and Exposé), we’ve seen this first-hand in our own work, particularly where organisations focus less on the solution they want to build and more on the outcomes their customers are trying to achieve.
Early in my consulting career in the City of London, I saw many talented data engineers build impressive solutions that clients never really used. I saw a pattern: a central team would initiate a project, gather requirements, and deliver something technically sound. But the most-used feature in almost every solution was invariably the “export to Excel” button.
Data engineers, no matter how skilled, are not accountants. When you start with “here’s the platform I want to build, tell me your requirements” rather than sitting with people and understanding how they actually work, you end up solving the wrong problem.
Capturing customer intent means you can deliver on what your end-user wants. It means you can solve the problems that matter and focus on the big picture of what a customer needs, not the minutiae of delivering smaller projects.
This concept extends broadly. In recent weeks, the “SAAS-pocalypse” (or rapid selling of software-as-a-service stocks by investors due to fears that AI will replace software) has missed a fundamental truth about how companies work. The major software vendors are integral to their clients’ operations. They’ve been on board with their clients for many years, they have established a high level of trust, and they’re constantly ensuring their clients are driving the outcomes they want.
There are lessons for all industries in this for retaining clients, as AI changes how work is delivered. Like the software vendors that are deeply embedded within their clients’ workflows, it’s about putting the customer story at the centre of everything you do. It’s making sure you do the right thing by your customer, not the right thing for you. It’s about being in it for the long haul together, and it’s about asking the right questions to understand their needs.
Companies across all sectors must differentiate themselves through their ethos around customer engagement. Your customers need to like dealing with you, and enshrining this is a cultural imperative that needs to span your entire organisation.
What changes in the AI-driven economy is the stakes and the speed. The export to Excel problem I saw in London wasn’t a technology failure, it was a signal that solutions were being built for imagined users rather than real ones. AI doesn’t fix that instinct. It amplifies it.
The differentiator won’t be who has the most sophisticated AI models. It will be who understands their customers well enough to apply AI in ways that genuinely improve outcomes. Organisations that get this right will see real productivity gains. Those that don’t will simply scale inefficiency, faster.Ends