The three of us — Shirlyn, Prishilla, and Jack — spent a day at Snowflake SPN Connect Sydney last month. This is an exclusive event hosted for partners within the Snowflake partner ecosystem.

Here’s what stood out to us. 

The headline: AI is compressing the gap between a business question and a data-backed answer

The clearest throughline across every session was this: AI is reshaping how people interact with data, and Snowflake is leaning into that hard. Tools like Snowflake Intelligence are putting self-serve analytics directly in the hands of business users — no data team required for the day-to-day stuff. That’s not a threat to data teams. It’s actually a relief. Less fielding ad hoc requests, more building data products that people can actually use.

The announcement worth knowing about: Cortex Code

Snowflake’s Cortex Code (nicknamed CoCo internally) was the product announcement that got people talking. Snowflake’s claim is that it outperforms Claude Code for Snowflake-native development. We haven’t stress-tested that ourselves yet, but if it holds up, it makes the platform meaningfully easier to work in — especially for teams spending real time writing and debugging Snowflake-specific code.

AI at two layers — and why that distinction matters

Organisations are finding value in AI at two distinct points in their data platform:

On the development side, AI tooling is catching issues earlier — query optimisation, pipeline code review, the kind of subtle bugs that historically surface at 2am when a critical ingestion job fails. Less firefighting, more building.

On the consumption side, business users can now interrogate data through natural language instead of raising a ticket with the data team or navigating five dashboards to find one number. Getting the right information to the right person at the right time has been the stated goal of data teams for years. It’s starting to actually happen.

The hard conversation: AI won’t fix a broken foundation

This came up more than once, and it needed to. The most common mistake organisations are making right now is investing in AI before their data foundations are ready for it. AI doesn’t fix poor data quality or inconsistent schemas — it amplifies them. Organisations that layer AI onto an immature data platform end up with unreliable outputs, eroded trust, and more complexity than value.

The version of this we hear most often: teams building infrastructure in anticipation of hypothetical future needs, disconnected from any specific business outcome. At scale, that becomes very hard to unwind.

Cost and observability are the other side of this. Cloud-native architectures are flexible, but complexity grows faster than most teams expect. Without observability across the platform, hidden costs accumulate — inefficient queries running unnoticed, unused datasets sitting idle, poorly designed workloads burning compute. Once a platform reaches a certain size, you can’t manage it by intuition alone.

Practical takeaways

A few things we’re taking back to client work:

• Before adopting any new Snowflake-native AI capability, evaluate whether to build within Snowflake or use an external tool. The answer isn’t always Snowflake-native — it depends on your architecture.
• Monitor credit consumption during the build phase, not just post-deployment. Surprises are avoidable.
• Get data governance in place before rolling out AI tools. Classification, tagging, masking policies, row-level security — these aren’t optional steps to defer.

Want to see what we’ve built on Snowflake?

From data platform builds to AI-ready architectures, we’ve delivered 250+ projects across Australia. If you’re working through a Snowflake implementation or wondering whether your data foundation is ready for AI — we’re happy to have that conversation. Lets talk!

This blog was written by Shirlyn, Prishilla, and Jack from the EdgeRed team.