The AI Data Agent Is Here. Your Business Definitions Are the Moat.
The next wave of AI products will not win because they can query a database.
That capability is becoming infrastructure.
OpenAI made the shift clear on September 10 when it introduced the Data agent in ChatGPT Work. The agent connects to approved company data, investigates business questions, and creates interactive dashboards through plain-language conversation.
But the most important detail was not the connector list or the dashboard builder.
OpenAI said its own data team made broad internal adoption possible by creating shared business definitions, access rules, and safeguards for sensitive data.
That is the founder lesson.
The model can write the query. Your product still has to know what the business means.
A database connection is not a product
Connecting an agent to Snowflake, BigQuery, Databricks, MongoDB, or a folder of company documents is useful. It is not enough.
Every established business has terms that look simple until you ask two departments to define them:
- Is “revenue” booked, billed, collected, or recognized?
- Is an “active customer” someone who paid, logged in, or completed the core workflow?
- Does “churn” include downgrades, pauses, and failed payments?
- Which date determines a conversion: form submission, sales acceptance, contract signature, or first payment?
- Which accounts should a renewal report exclude?
A general model cannot safely guess those answers.
Snowflake’s guidance for semantic models says descriptions are the most important element for accuracy because language models cannot reliably infer proprietary terms, abbreviations, or business logic. It recommends explicit descriptions, predefined metrics, reusable filters, and verified queries for common questions and edge cases.
In other words, the valuable layer sits between the raw tables and the chat box.
It is the company’s operating knowledge made precise enough for software to use.
Your domain expertise becomes the semantic layer
This creates a strong opening for experienced professionals who understand a workflow better than a general software team.
A revenue-operations leader knows why pipeline coverage differs by segment. A healthcare operator knows which appointment statuses count as completed care. A logistics manager knows when an exception is late enough to threaten a service-level agreement. A construction executive knows that “project margin” changes meaning when change orders are still pending.
That knowledge usually lives in scattered places:
- spreadsheet formulas;
- dashboard filters;
- onboarding documents;
- Slack explanations;
- analyst memory;
- the corrections a leader makes during a weekly review.
Your first product opportunity may be to capture one of those decision systems.
Do not begin with “an AI analyst for the entire industry.” Start with one recurring question where the wrong definition creates wasted work or a bad decision.
Examples:
- Which customer accounts need intervention before renewal?
- Which jobs are likely to miss their promised completion date?
- Which claims are missing the evidence required for the next review?
- Which sales opportunities are real enough to include in a forecast?
The agent is only the interface. The definitions, relationships, exclusions, and escalation rules are the product.
Build a definition set before you build a dashboard
A practical first version does not need hundreds of metrics.
Start with a minimum viable semantic layer for one workflow:
- Name the decision. What will the user decide after seeing the answer?
- Define five to ten terms. Write each definition in plain business language, including exclusions.
- Map the evidence. Identify the tables, documents, fields, and dates behind each term.
- Specify permissions. Decide which roles can access which sources, rows, and sensitive fields.
- Create verified questions. Collect real questions and the answers an expert considers correct.
- Add uncertainty. Require the product to show missing data, conflicting definitions, or low-confidence conclusions.
- Keep a human checkpoint. Do not let an early agent automatically change forecasts, contact customers, or update a system of record.
This is a better prototype than a beautiful dashboard filled with synthetic data.
You can test it manually. Give the same business question to the product and to a trusted expert. Compare the result, the evidence used, and the next action recommended. When they disagree, update the definitions or the workflow before blaming the model.
That evaluation set becomes an asset competitors cannot download from a model provider.
The moat is governed judgment
OpenAI’s Data agent enforces the connected account’s existing table, row, and column permissions. It also uses business terms, metric definitions, custom calculations, and data relationships from trusted company sources.
Those details point to the product category forming underneath the launch.
Customers will not merely buy “chat with your data.” They will buy dependable answers inside a specific operating context.
Dependability requires more than intelligence. It requires:
- a shared definition of the metric;
- permission to see the underlying evidence;
- a traceable path from question to answer;
- rules for ambiguous or incomplete data;
- a person who owns the definition when the business changes.
Models will improve. Connectors will multiply. Natural-language dashboards will become common.
The durable company will own the narrow decision system that makes those tools trustworthy for a particular customer.
Start with the metric people argue about
If you know an industry well, listen for recurring arguments about the numbers.
“Why does finance show something different?”
“Which report is correct?”
“Why did this account get flagged?”
“What counts as complete?”
That friction is not a data-cleaning footnote. It may be the product wedge.
Choose one decision. Define the business language. Connect only the evidence required. Test against real cases. Then wrap the validated method in an AI experience that makes the answer faster, clearer, and easier to act on.
That is how domain expertise becomes more than content inside a prompt. It becomes product infrastructure.
If you want help turning a workflow you understand into a focused AI product and a 12-week launch plan, book a strategy call. That is the work we do inside AI Product Accelerator.