Snowflake Cortex AI makes it easier to build AI using the data you already have in Snowflake. Getting a pilot working is usually the easy part. Many Australian teams can build a working pilot within a few weeks, but moving it into production is more difficult.

The main challenge is not the AI model. It is making sure the data is trusted, the right governance is in place and there is clear ownership.

Get the business definitions right first

Cortex AI cannot fix unclear business definitions. It will answer based on the metric definition it is given. For example, ‘revenue’ could mean before refunds, after refunds or after settlement. These can all give different results. Semantic views help keep metrics, dimensions and business rules in one governed place.

A simple readiness check is to ask two different teams to define your top three metrics. If their answers are different, agree on the definitions before using Cortex AI.

What Snowflake Cortex AI gives you, and what’s still on you

Snowflake provides a genuine platform: text-to-SQL, retrieval, AI functions, semantic views and role-based access. You don’t need to build your own model-serving layer or vector database.

What it doesn’t give you: agreed business definitions, approved metric owners, user training, or a plan for who handles it when an answer’s wrong. A team can build a working feature fast and then spend far longer on the governance around it.

Accuracy is a business-risk question, not a single number

There’s no universal accuracy threshold for ‘production-ready.’ A small error in a general document search might be a minor inconvenience. A wrong answer feeding into financial or regulatory reporting is a different order of risk entirely.

Build your test set from real business questions, including ones the system should reject outright. A high average accuracy score can hide a small number of failures in exactly the questions that matter most, so test those separately.

Give the solution a clear owner

Every production Cortex AI solution should have a business owner and a technical owner.

The business owner should be responsible for:

• Metric definitions
• Expected answers
• Acceptable risk
• How the output is used

The technical owner should be responsible for:

• Access and security
• Testing and monitoring
• Cost
• Incidents and support
• Technical changes
• Data models and the semantic layer

Australia’s Guidance for AI Adoption, released by the National AI Centre, also focuses on accountability, risk management, testing, monitoring and human oversight.

When ownership is unclear, incorrect answers can be left between the data, engineering, risk and business teams, with no one responsible for resolving them.

Final thoughts

Snowflake Cortex AI can reduce the technical work needed to build AI solutions using trusted organisational data. But moving into production requires more than switching on an AI feature.

Australian organisations need clear business definitions, strong access controls, realistic testing, privacy reviews, cost monitoring and clear ownership.

The organisations that get the most value from Cortex AI in 2026 won’t just focus on launching quickly. They’ll focus on building something people can understand, manage and trust.