Every business in Australia is racing towards AI, and most are looking to platforms like Databricks to get there faster. But the organisations that get the most value will not win because of flashy models. They will win because of the unglamorous work they did before AI was even on the agenda.

What's keeping tech leaders up at night

Two things dominate the conversation. The first is proving AI spend to the board. If a business is spending a few hundred thousand dollars on Copilot licences, what does that actually mean for the bottom line? Most leaders do not have a clean answer yet, and the pressure to produce one is only growing.

The second is data, though not the way it is usually framed. Enterprise companies do not lack data. They lack access to it. It sits in silos across systems that do not talk to each other, which leads to the underlying question every CIO ends up asking: how do we make sure the systems AI touches are secured and governed, particularly across financial services and government, where that question carries regulatory weight as well as commercial risk.

What Databricks sees in the winners

Companies making real progress right now took the hard pill. They spent years on the foundational work before anyone shipped an agent: getting the data access model right, designing governance from day one, earning leader buy-in early.

The payoff shows up later. Once the foundation is there, new AI capabilities can be brought online in weeks. Databricks, the data intelligence platform underpinning a large share of enterprise AI infrastructure in Australia, is built around exactly that idea: a governed layer that lets analytics, machine learning and AI workloads sit on the same foundation instead of bolting AI on top of a fragmented one. That is the pattern Databricks keeps seeing across its own customer base, and it is the same pattern the organisations that skipped the foundation work are now paying to retrofit, at higher cost.

The vendor question most leaders aren't asking

Sitting across from a vendor, the temptation is to focus on the technology. What does it do? What does it cost? Which models does it use?

The more useful question is: what change is this going to bring to the company? What processes will need to shift, and who reorganises around it? Most vendors have thought less about that than about their feature list, which tells you something on its own.

Technology is the easiest part

"Technology is always the easiest part," says Vivian Vo, Enterprise Account Executive at Databricks. In her role, Vo spends most weeks in conversation with tech and data leaders across Australia, watching what actually holds up as enterprises move from AI experiments into production on the Databricks platform and elsewhere. Her read: a business can have the best AI agent in the world, but if the people and processes around it are not ready, it does not go anywhere.

AI adoption that holds up looks pretty boring from the outside. Slow foundation work, careful buy-in, governance in place before anything gets built. It is the version that lets a demo eventually become production.

Not sure whether your foundations can carry the AI you're planning to build

The Omnia Collective works with enterprise, financial services and government teams as a certified Databricks partner in Australia, building the governed data foundation that AI actually needs before the interesting use cases can run on top of it. See our data engineering services or get in touch.

This post is adapted from Episode 5 of our Nap Stack podcast on AI, data, and building a business. Listen on Spotify or Apple Podcasts.