How Australian Businesses Can Overcome The Struggle to Move AI from Hype to Reality

As much as AI has captured the imagination of Australian business leaders, there is a sobering reality: most organisations are struggling to translate AI enthusiasm into tangible business outcomes.

The numbers tell a stark story. According to research presented by Brennan, less than 5% of AI initiatives are making it into production. This failure rate isn’t due to technology readiness. Rather, it's fundamentally about the inability to demonstrate clear return on investment.

Perhaps most damning was the revelation that 60% of Chief Financial Officers (CFOs) don’t believe businesses could build an effective AI use case. This level of scepticism from the people controlling purse strings represents a significant barrier to AI adoption across Australian enterprises.

Is it a good idea to abandon AI ambitions? Not at all. But there does need to be a better understanding of how to execute on AI to prevent organisations getting caught between genuine excitement and practical paralysis.

While products like Microsoft Copilot and ChatGPT have generated significant buzz, particularly in the legal sector where they're being used for contract summarisation and document drafting, CFOs are pushing back on business cases that simply don't stack up.

"Productivity gains are notoriously hard to measure," Steve Anderton, Director of Digital Solutions at Brennan, said. "Unless you're directly reducing headcount – which few organisations want to do – it's tough to turn 'giving people time back' into a concrete business case."

While generative AI continues to dominate headlines and boardroom discussions, industry experts suggest the real opportunity lies elsewhere. The most immediate value creation is happening with AI that's embedded directly into business applications and processes. It’s the kind that drives faster customer service, more accurate compliance checks, or better operational decision-making.

This shift in focus from flashy consumer-facing tools to practical business applications represents a maturation in thinking. It's less about the technology that grabs headlines and more about solutions that solve real business problems with measurable outcomes.

It Starts With Data

Data quality is the foundation for AI success and , as Brennan’s Anderton explains, its biggest bottleneck. Many organisations have, in recent months, discovered that their fragmented, siloed data architecture simply isn't ready to support AI initiatives at scale.

Anderton highlighted one customer example that showed this perfectly: despite doing advanced AI work, they admitted to struggling with scaling because their underlying data infrastructure wasn't fit for purpose. Without secure, scalable, and well-governed data architecture, even the most promising AI projects are unlikely to deliver real value.

This infrastructure gap is creating a catch-22 situation. Building proper data foundations requires significant investment, but in today's "do more with less" environment, securing that investment without proven AI returns is increasingly difficult.

Another unexpected challenge that surfaced repeatedly was the emergence of "shadow AI" – employees independently signing up for AI tools and incorporating them into their work without IT oversight. This phenomenon mirrors the shadow IT problems organisations faced with cloud adoption, but with potentially more serious consequences.

One organisation had temporarily shut down access to generative AI tools entirely after discovering employees were unknowingly inputting sensitive information into public AI platforms, raising serious data governance and security concerns. The fear of brand damage and regulatory risk was driving some leaders to adopt a "shut it down until we understand it" approach.

The Micro Innovation Approach

To address these challenges, a new approach is gaining traction: micro innovation. Rather than betting big on AI transformation, this methodology focuses on starting small, rapidly prototyping, and testing AI use cases to prove value quickly without overcommitting resources upfront.

The approach emphasises bringing cross-functional teams together, including business stakeholders, technologists, and decision-makers, to rapidly ideate, prioritise, and prototype solutions. The goal is to move from concept to proof of value in weeks, not months.

"You don't need to eat the elephant all at once," Anderton said. "Find something of high visibility with the lowest investment and biggest impact. Once you can prove that, you can scale from there."

Despite the challenges, one message emerged clearly from the forums: the risk isn't that AI will take jobs, but that someone who knows how to use AI might take yours. This reality is particularly acute given that new graduates are entering the workforce with AI tools already integrated into their skill sets.

Building AI literacy across organisations has become essential. Rather than viewing AI as a threat, successful organisations are empowering their people to understand and use AI tools in their everyday work, ensuring they're part of the AI revolution rather than left behind by it.

The path from AI concept to reality isn't straightforward, but Australian businesses that focus on clear use cases, solid data foundations, and measured innovation approaches are beginning to unlock genuine value. By doing this, those organisations will have moved beyond the hype and can then focus on reaping the benefits of their investment into technology and innovation.