Insights and findings from our daily work

Success with technology is not defined by its launch but by its longevity. The organisations that thrive are those that treat innovation as an ongoing capability, ensuring their systems continue to deliver value even as conditions change. 

Markets change quickly. Customer expectations evolve, regulations tighten, and competitors innovate. Without resilience built into their design, AI systems risk falling behind, leaving businesses exposed to poor outcomes and wasted investment. Building resilience into AI is about preparing systems not just for launch, but for long-term success. 

Artificial Intelligence continues to capture the attention of business leaders eager to transform how they operate. Many organisations begin with small proof-of-concept projects or pilots to explore the possibilities. These pilots can spark excitement and create momentum, but too often they do not progress into production-ready solutions that deliver measurable business value.

Artificial intelligence has quickly become one of the most powerful tools available to modern businesses. It can optimise operations, unlock insights, and even reshape customer experiences. Yet despite the hype and investment, many AI initiatives never deliver on their promise. The technology itself is rarely the issue. More often, the barrier is people. Culture and change management are the deciding factors that determine whether an AI project thrives or quietly fails.

IT has often been viewed through the lens of cost control, responsible for keeping the lights on, maintaining infrastructure, and ensuring employees have access to the tools they need. But that perception is rapidly evolving. With the rise of AI, IT has an opportunity to shift from a reactive support function to a proactive driver of business growth.

Artificial Intelligence is only as effective as the data that powers it. Businesses are investing heavily in AI to automate processes, enhance customer experience, and improve forecasting. But while attention often goes to the tools and models, the true value of any AI system depends on one thing: the quality of its data.

Legacy systems are often the backbone of a business—stable, deeply embedded, and mission-critical. But as the pace of digital transformation accelerates, these same systems can become roadblocks to growth, agility, and innovation. For many Australian organisations, the shift isn’t about whether to modernise, but how to do it without disrupting operations or losing control of costs. 

There is no shortage of hype around AI. Many organisations are investing heavily in it with the hope that AI will deliver game-changing efficiency, smarter decision-making, or competitive advantage. But without clear alignment to commercial outcomes, these investments can become science projects - interesting but ultimately ineffective. 

In the past, making IT investment decisions often came down to instinct. A trusted team member made a recommendation, or a shiny piece of software caught your attention. But in today’s AI-driven, cloud-native world, that approach no longer cuts it. Too many businesses start with the solution “We need AI,” “Let’s build a custom app,” or “Can you automate this?” before clearly defining the problem. The result? Misaligned tech, poor ROI, and projects that never quite hit the mark. 

 AI is no longer the future—it’s firmly in the present. Yet as more enterprises deploy AI, many are learning the hard way that success hinges not on capability, but on governance. The rush to innovate has left some organisations vulnerable to very public failures. Microsoft’s Azure AI stack is one of the most advanced in the market—but even there, missteps can happen when proper guardrails aren’t in place.