Recently I’ve been lucky enough to host several Data & AI breakfast forums in Sydney, Melbourne, Newcastle, and Brisbane, all centred on how to take AI from concept to reality.

Along the way, we’ve spoken to customers across industries, from finance and legal to retail and healthcare. Every participant has been generous with their time and fulsome with their opinions and insights.

And their while every organisation is at a different stage in their AI journey, common challenges and opportunities kept surfacing.

Here are some of the big takeaways – and what they could mean for businesses looking to walk, run, and race with AI.

1. Generative AI is just the tip of the iceberg

Right now, Generative AI – think Copilot and ChatGPT – is gobbling up all the attention. It’s been positioned as a major productivity enabler, and we’ve seen some compelling use cases, particularly in the legal industry, where the use of Copilot to summarise contracts, run conflict of interest checks, and draft documents can deliver real efficiency gains.

However, many CFOs told us they’re struggling to justify investments in Generative AI, simply because the ROI isn’t clear. Productivity gains are notoriously hard to measure. 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.

In our view, the real opportunity lies in AI that’s embedded directly into business applications and processes – where it can drive tangible outcomes like faster customer service, more accurate compliance checks, or better operational decision-making. This kind of AI may not grab headlines, but it’s where we’re seeing the most immediate potential for value creation.

2. Data is the foundation – and the biggest bottleneck

We all know that AI is only as good as the data that powers it. But for many organisations, fragmented, siloed data remains a major barrier to scaling AI initiatives.

Without a secure, scalable, and well-governed data architecture using proven solutions like Microsoft Azure, even the most promising AI projects are unlikely to deliver real value. We spoke to one customer who’s doing advanced work with AI, but even they admitted they’re struggling to scale because the underlying data infrastructure just isn’t where it needs to be.

The challenge is that while the opportunity is clear, data is the secret sauce that unlocks AI’s full potential. Building that foundation requires investment, and in today’s “do more with less” environment, that’s easier said than done.

3. Navigating the hype: What’s real, what’s not, what’s next

Another theme that came up repeatedly was the sheer amount of hype around AI – and the confusion it’s creating for decision-makers.

“With new tools, features, and capabilities being released almost weekly, some businesses are hesitant to commit to a particular path, fearing it might become obsolete before they see an ROI.”

There’s also concern about the risks associated with AI. From privacy and compliance issues to the phenomenon known as AI “hallucinations” (when models confidently make up incorrect answers), the potential downsides are real – and top of mind for many of the leaders we spoke to.

One customer, for example, had temporarily turned off access to generative AI tools across their organisation due to fears around brand damage and regulatory risk. They’d seen employees unknowingly inputting sensitive information into public AI tools, raising serious data governance and security concerns.

But here’s the key point. While it’s important to be aware of these risks, it’s equally important not to let fear stop you from moving forward. AI can deliver huge benefits – if you focus on the right use cases and put the right guardrails in place.

4. Think small to win big: Why Micro Innovation matters

When it comes to AI, you don’t need to bet the farm. In fact, one of the most popular ideas we discussed was the concept of Micro Innovation – starting small, rapidly ideating, prototyping, and testing AI use cases to prove value quickly, without overcommitting resources upfront.

This approach (coupled with low-code tools like Microsoft Power Platform) can help you cut through the hype, reduce risk, and build momentum by focusing on quick wins that demonstrate real business outcomes. Once you’ve proven the value at a small scale, you can start thinking about how to scale up.

5. AI won’t take your job – but someone who knows how to use AI might

One final topic that sparked a lot of discussion was the future of work. There’s no question that AI will change jobs – but it won’t necessarily eliminate them. Instead, we’re likely to see many roles evolve, with AI taking over repetitive, manual tasks and freeing people up to focus on higher-value, more strategic work.

The bigger risk, in our view, isn’t that AI will take your job – it’s that someone who knows how to use AI might. That’s why building AI literacy across your organisation is so important. By empowering your people to understand and use AI tools in their everyday work, you can ensure that they’re part of the AI revolution, not left behind by it.

We covered a lot of ground in these sessions, and it was great to see so much engagement and openness to share experiences and insights.