Is your Data Ready for AI? A Snowflake Agentic Showcase with Informatica: Why Data Foundations Matter More Than Models
AI adoption across enterprises is accelerating, but results are uneven. While organizations invest in models, copilots, and assistants, many struggle to move beyond experimentation. The root cause is consistent across industries: AI is being introduced before the data foundation is ready.
At aiDataWorks, what we are showing in this demo - and delivering for customers - is a very deliberate, execution-first approach to AI. It starts with data, not models.
The first step is establishing Snowflake as the unified cloud data foundation.
Enterprise data - structured and unstructured
- must be integrated, modeled, mastered, and governed so it can be trusted. Without this, AI produces inconsistent, unreliable answers, regardless of how advanced the model is.
Once Snowflake is functioning as a true system of record, we then layer AI agents directly on top of the data.
These are not dashboards
and not a generic chatbot interface. They are purpose-built agents that understand how to work with enterprise data. Each agent has a specific responsibility:
- Identifying where data lives in Snowflake
- Determining whether it is structured or unstructured
- Generating the correct queries
- Validating results
- Deciding how answers should be presented.
From the business user’s perspective, the experience is simple. A question is asked - revenue for a specific period, win rate by sales rep, and performance by product line. The agents handle the complexity behind the scenes.
The answer is returned as a chart, a table, or a downloadable dataset, depending on what makes sense for the question.
This capability only works because the data is clean, integrated, and governed first.
AI does not replace data management. It depends on it.
In fact, AI makes data issues more visible. Poor quality, inconsistent definitions, and weak governance surface immediately when agents are asked to operate across enterprise datasets.
That is why AI initiatives that skip the data foundation step often stall. They look promising in demos, but fail in real business use because the underlying data cannot support them.
What we are demonstrating here is how AI becomes operational when it is built on the right architecture. Snowflake provides a scalable, governed data platform. AI agents sit on top of it to make that data accessible, interactive, and usable by the business - without creating new analytics bottlenecks or dependencies on manual effort.
If your organization is investing in AI - or planning to - the most important question is not which model you plan to use.
Is your Snowflake data foundation ready to support AI agents?
Most teams have not pressure-tested this. They assume readiness without validating integration, governance, or data consistency across the platform.
We work with leadership, data, and architecture teams to:
- Assess whether Snowflake is functioning as a single, trusted data foundation
- Identify gaps in integration, data quality, and governance that will block AI outcomes
- Determine where AI agents can deliver real business value today - and where they cannot yet
- Prevent expensive AI initiatives from being built on unstable data
This is not a demo and not a theoretical discussion. It is a working session grounded in your actual environment.
If you have or are working toward having AI run over your cloud data warehouse–and want to understand whether your data can realistically support AI agents, this is the conversation to have before scaling AI further.
Start with the data.
Put AI on top of it.
Then scale.
Want to connect?
Email us at marketing@aidata.works
Or schedule a 15-minute session: aidata.works/meet