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Modernising Without the Mayhem: How to Plan a Legacy Software Overhaul

Matthew Labrum

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. 

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AI That Pays Off: How to Spot a High-ROI Use Case

Matthew Labrum

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. 

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From Gut Feel to Great Tech: Why Business Leaders Need to Flip the Decision-Making Process on AI and Software

Matthew Labrum

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. 

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Real-World AI Failures: Lessons from Microsoft Azure AI Deployments

Matthew Labrum

 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.  

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AI vs Automation: Getting the Decision Right for Your Business

Matthew Labrum

In today’s buzzword-laden tech landscape, it’s easy to assume that artificial intelligence (AI) is the solution to every business challenge. But the truth is more nuanced. Often, the right answer isn’t AI—it’s automation. And sometimes, it’s neither.

 

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Strategic IT Planning: Future-Proofing Your Tech Investments

Matthew Labrum

More and more businesses are under pressure to not just keep up, but to plan for the future. What happens if we have a cyber breach, what do we do in the event of a core system going down? These question provide a fork in the road for your business. Strategic IT planning isn’t just about upgrading your infrastructure or rolling out new software—it’s about aligning technology with your long-term business vision. As Australian IT spend hits $146 billion in 2025 and AI investment alone reaches $44 billion, future-proofing your tech stack is no longer optional. It’s a competitive necessity. 

 

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Overhype and Under-Delivery: When AI Promises More Than It Can Deliver

Matthew Labrum

 AI has been hailed as a transformative force, capable of driving efficiency, unlocking innovation, and reshaping industries. But while the promise of AI is enormous, the reality for many businesses is far less impressive. Too often, AI projects fail to deliver on their potential—not because the technology isn’t powerful, but because expectations are set too high, too soon, without a clear strategy for success.

 

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Why AI Is Never a Set-and-Forget Solution

Matthew Labrum

 AI is often sold as a game-changer, an advanced system that once trained, can deliver predictions, insights, and automation on autopilot. But that’s a dangerous misconception. The truth is, AI is never a set-and-forget solution. It’s a living, evolving system that requires constant care, monitoring, and maintenance.

 

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The Data Trap: Why AI Fails Without the Right Foundations

Matthew Labrum

 Artificial Intelligence is only as good as the data it’s built on. While AI promises smarter decision-making, automation, and predictive insights, the reality is that without high-quality, well-labelled data, even the most advanced AI models can fail—sometimes spectacularly. It’s a trap that many organisations fall into: investing in cutting-edge AI tools while overlooking the messy, unglamorous work of data management. 

 

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