CXO Insights – The next move on Data and AI: Observations from Australia with Gaurika Wijeratne

Things are moving very fast on the technology front, but translating that into tangible business value is where most organizations are still struggling. What I have observed is that customers who have invested in strong, well-structured data platforms are seeing the most success; they can build on those foundations and effectively leverage multiple AI models and platforms. However, for most businesses, while AI adoption has moved beyond initial experimentation, only a small minority have truly scaled it to deliver enterprise-level value or measurable ROI. The majority are still stuck in pilot mode, with limited production rollout and inconsistent outcomes. The core challenge isn’t the AI technology itself, but rather weak data foundations, gaps in governance, and overall readiness. As a result, most organizations are currently using AI for incremental productivity gains, such as copilots and automation, rather than achieving meaningful, business-wide transformation.

Yes, very much so. The same challenges kept coming up consistently across different customer conversations, and the interesting part is that these aren’t unique to Australia or New Zealand; it’s largely the same story globally. Global studies show that even though AI adoption is widespread, most organizations are still struggling to scale and embed it into core business processes. What differs slightly by region is the context. Australia, in my experience, tends to show a wider gap between strong AI ambition and actual execution at scale, but fundamentally, the underlying challenges are very consistent. What we are seeing in ANZ is not an outlier; it’s a reflection of where most global organizations are in their Data & AI journeys.

I saw firsthand how powerful it is when organizations get their data foundation right and connect it directly to real business outcomes. I saw customers that have moved beyond just the initial data platform we developed or AI use cases we implemented and actually extend and embed the solution into their day-to-day decision-making: whether it’s optimizing operations, improving customer experience, or enabling faster and more confident decisions.

What stood out wasn’t the sophistication of the AI itself, but the clarity of purpose; they were very deliberate about aligning data initiatives to specific business problems. It really reinforced the idea that when data is treated as a strategic asset and not just a technical capability, the value becomes very tangible. It’s stories like this that more organizations need to hear, because it shifts the conversation from “doing AI” to actually “running the business differently with data”.

Microsoft came up very consistently across most conversations; in many cases, it’s almost considered the default starting point, especially for organizations already invested in the ecosystem. What’s making it so relevant right now is that Microsoft has managed to bring together what most companies are struggling with: data, AI, and productivity, into one integrated stack. Platforms like Microsoft Fabric unify data engineering, warehousing, analytics, and BI into a single environment, significantly reducing complexity and accelerating time to value. The integration with Azure AI and Copilot takes it further; AI is no longer a separate layer but embedded directly into workflows, allowing organizations to move from data to insight to action much faster.

But I think the real reason it resonates with business leaders is that it directly addresses the challenges we discussed earlier – unified data foundations, built-in governance, and enterprise-grade security – within a familiar, end-to-end ecosystem spanning infrastructure (Azure), data (Fabric), and user experience (Copilot). In a space where many companies are overwhelmed by fragmented tools, that’s a compelling proposition.

If a business leader wants to get serious about Data & AI, I’d tell them to focus on five things. First, get the data foundation right. Clean, well-governed, well-integrated data is the single biggest success factor. Second, start with clear business outcomes, not technology, and anchor every initiative to measurable value. Third, prioritize a few high-impact use cases and scale them properly, rather than spreading effort across too many pilots. Fourth, invest in governance, security, and operating models early, because scaling AI without control creates long-term risk. And finally, focus on people and change, building skills, adoption, and a data-driven culture, because ultimately, AI only delivers value when the business changes how it works.