AI Workflows Need More Than Data Access. They Need Business Context.

In the previous article, I wrote about Trusted Metrics and why KPI drift breaks confidence in data platforms.

Now we move to the part many organizations are most excited about:

AI workflows, agents, and natural language analytics.

This is where the value of governed context becomes very visible.

Because giving AI access to data does not automatically give it business understanding.

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The problem with “just ask your data”

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Natural language interfaces are a big step forward for analytics.

A business user can ask a question without writing SQL, or an analyst can explore faster, or an agent can reason through a task and call the right tools, or a workflow can move from question to action with less manual effort.

That is powerful.

But it also creates a new risk.

If the AI does not understand the approved business definition of revenue, customer, churn, margin, pipeline, or active user, it may still return an answer.

The answer may look confident.

It may even be technically correct.

But it can still be wrong in business terms.

That is why AI workflows should not start from raw tables alone. They need a governed layer of business meaning between the user’s question and the data platform. What is more, that meaning should be consistent across teams.

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Why semantic context changes the AI experience?

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For AI-assisted analytics, semantic context is what helps connect business language to technical data structures.

Instead of forcing AI to infer everything from table names, column names, joins, and examples, the platform can expose approved entities, dimensions, facts, relationships, and metrics.

In Snowflake, this is where Semantic Views, Cortex Analyst, and Horizon Context become connected pieces of the same story. Semantic Views provides the business-friendly model over physical data, Cortex Analyst uses semantic views to support natural language questions over structured data, and Horizon Context extends the idea of governed semantic context across AI, BI, and applications. (Snowflake Documentation)

The practical impact is simple:

- AI should be pointed to the approved metric definition every time it runs a query.

- The semantic layer makes that routing decision on the model’s behalf.

That does not remove the need for validation, testing, or human oversight. But it reduces the risk that every AI interaction becomes a fresh interpretation of the business. And the more AI will work on curated and trusted semantic definitions, the better effecs in the long run.

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From chatbot to governed workflow

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The real opportunity is not only “chat with data.”

It is governed workflows where AI can:

Select the right semantic view or metric

Trigger the next step in a controlled way

This is where the difference between a demo and a production-ready AI workflow becomes clear.

A demo can work with a few curated examples.

A production workflow needs consistency, permissions, lineage, ownership, monitoring, and clear context.

Snowflake positions Cortex Agents as a managed agentic platform where agents can reason over requests, plan work, call tools, execute code, and generate responses inside Snowflake’s governed environment. CoWork also connects agents to semantic views, semantic models, Cortex Search services, and tools, so questions can be routed through the right governed context instead of relying only on free-form interpretation. (Snowflake Documentation)

That direction depends on two things:

agents pointed at governed semantic definitions, and someone accountable for keeping those definitions current as the platform evolves.

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Measurable targets for AI-ready context

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AI readiness should not be described only in abstract terms.

It should be measurable.

For AI-powered analytics and agentic workflows, I would look at targets like:

100% of business-critical AI questions routed through governed semantic definitions

If the answer influences reporting, operations, finance, customers, or executive decisions, it should not rely on raw schema interpretation.

Named ownership for every AI-exposed metric or semantic view

If AI can use it, someone should own its business meaning and technical correctness.

These are not universal benchmarks.

They are practical guardrails that help organizations avoid treating AI as a separate layer disconnected from data governance.

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What to watch out for?

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The biggest risk is assuming that AI will clean up semantic ambiguity by itself. It will not, more likely it will make matters even worse – in short “Garbage in, garbage out”.

Take a metric like revenue that already has five competing definitions across teams: AI does not resolve that disagreement, it just answers faster from whichever definition happens to be closest at hand, and unclear ownership means nobody is positioned to correct it.

This is why governed context needs to come before broad AI adoption in analytics.

A good AI workflow should be able to answer more than just:

- “What is the number?”

- “Which definition was used?”

- “Who owns this metric?”

The thesis I would put forward is this:

Many teams expect AI to smooth over messy metric definitions on its own. In practice the opposite happens: gaps in semantic governance surface faster, and at greater scale, once an agent is the one asking the questions.

That is why AI-ready data platforms need more clean data and strong access controls.

They need governed business context.

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My Perspective

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AI is here. It is real. And it needs a little help to get going properly.

Up to this point, the premise of “garbage in, garbage out” was mostly associated with working on unclean data. That premise still holds, but it has now expanded to the ontology and semantic layer. Because of that, not only does the original principle become stronger, it also becomes much harder to ignore.

AI is a tool. Period.

Like every tool, it needs to be operated properly and in the right context. For AI and data, the right context means being clear about three things:

• Where the data sits
• Whether the data is clean and reliable
• How AI should operate within approved business definitions

Setting up these three principles early saves a lot of headaches later down the line.

This does not mean building state-of-the-art governance from day one. It means creating a sensible first version at the beginning of the journey and developing it in an agile way as the platform grows.

Think about it this way: if you run a few simple iterations with business and data teams early on, you start building the foundation for more complex AI workflows later.

Proper governance is Robin to AI’s Batman.Without it, AI can still roam Gotham – just without anyone making sure it’s chasing the right leads.

In the next article, I will look at the emerging consumption layer around modern data platforms: how business users, developers, applications, CoWork, CoCo, and AI agents can work from the same governed foundation.

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