What Our Own Data Journey Taught Us About AI Readiness
Every business wants to move faster with AI. But most hit the same wall. The data isn’t ready.
At Macquarie, we made a deliberate decision to fix that first. Over the past few years, we’ve rebuilt our data foundations to make AI practical across the business. It’s been a journey of discovery, leadership, and focus – and it’s changed how we think about data entirely.
Here’s what we learned along the way.
Vision, leadership and Microsoft Fabric: Setting the direction.
It doesn’t matter how keen you are, your data program will only succeed if you start with the right vision and the right person driving it. For us, that was James Mystakidis, our Group Executive. From day one he owned the vision of having trusted insights as the foundation for AI, and made sure it was treated as a business priority.
On an episode of our Cloud Reset podcast, James explained why you can’t do an AI project without a data project. That principle guided every step of our journey.
Following the vision, was the discovery stage. It’s tempting to jump straight into new tools, but if you skip the groundwork you just carry old problems into a new system. We mapped every dashboard, every data source, every definition. We did a data maturity assessment to get a clear, honest baseline, providing a way to measure progress and keep the business engaged.
The outcomes of that assessment signalled that we needed a solution that addressed people, process and technology, guiding our move to Microsoft Fabric. This provided a single platform to connect, clean and prepare data for ready for AI. We’re using the medallion model (bronze for raw data, silver for structured, gold for governed and AI-ready) as a way to track and communicate our progress. Our journey is proof of why this progression matters for building real AI outcomes. We share more detail on it in our Data & AI report.
Microsoft Fabric provides the foundation. It doesn’t just rebuild old dashboards in a new tool, it provides a single source of truth for our data, that provides a consistent, trusted data layer that can power reporting, analytics, and future AI projects. That matters because AI models can’t just point at raw enterprise data. It needs clean, structured, well-governed data that is ready for AI use. Fabric gives us a way to do that systematically.
The impact: cleaner data, clearer insights, more room for innovation.
Strong data foundations don’t take years to show results. The impact is felt almost immediately.
When data quality improves and the business trusts the data, everything else accelerates. The discovery phase exposed gaps and inconsistencies. Cleaning them lifted accuracy and trust across the business.
With Fabric unifying the flow of data, reporting became effortless and insight more immediate. That clarity created space for what really matters: innovation. Early AI pilots showed us what was possible, but they also made it clear how hard it is to scale when the data underneath isn’t consistent or easily accessible. With Fabric in place, those same AI pilots can scale faster. Stronger data foundations turn AI experimentation into a commercial reality.
To hear more about some of our successful AI projects we’ve run internally, download our Data & AI report.
Eight lessons for building stronger data foundations.
Here are the lessons that have shaped our own data journey and that we now share with customers starting theirs.
- Face your data reality. Start with a data maturity assessment. This truth will shape the vision for what came next. The answers to your AI strategy are often in the data itself.
- Develop your vision that lives beyond IT. A data program will only stick if it’s owned outside IT. Engage the business early, share the problem statement openly, even the skeletons in the closet. Trust builds buy-in.
- Focus on real use cases. Tackle the pain points that matter most. For us, it started with executive monthly management reports because that’s where the impact was felt.
- Prepare the BI/Data Teams for change. Fabric is a new technology, and it requires upskilling and shifting the capability from report writing to data engineering. So be prepared that your team also needs to upskill.
- Find your AI champions. Look for early AI projects or champions across the business. Their needs can help shape a data model that works in practice.
- Measure progress. A data maturity assessment keeps you honest and shows the business the gains along the way.
- Stay outcome driven. Technology is the enabler. Success should be measured in business results.
- Find quick wins to show value quickly. It’s easy to get overwhelmed moving to a new platform – try find quick wins during the Fabric deployment. Run a POC on a specific data set and show how clean, AI-friendly data can be enabled. This helps with buy in and get business excited for what is to come.
Our data journey, your starting point
Most businesses want to be AI-ready. What we’ve learned is that AI readiness isn’t a technology milestone – it’s a cultural one. You need leaders who treat data as a shared asset, not a departmental resource.
Getting our data foundations right was the most valuable investment we could have made. It’s also why we built our Managed Microsoft Fabric offering – to help other organisations accelerate the same journey.
It brings together our own expertise and lived experience on governance, security, visibility and Fabric expertise, and makes it available to anyone who wants to accelerate their path to AI-ready data long-term.
Whether you’re just starting your data and AI journey or looking to move faster, now’s the time to act. Talk to one of our Data and AI experts to see how stronger data foundations can make AI practical in your organisation.
