Lesson 10 of 10 · Buying and rollout
Making the case for an agent-readiness budget
Short answer
The data platform leader is best placed to make agents work on enterprise data. A strong case ties the budget to specific tasks, measured before and after, rather than to broad AI promises.
By The Lineage Desk · Best Data Catalogs · Published
Desk research from public vendor pages. Editorial assessment.
2 min read
Every company now has an AI budget, and most of it is not owned by the data platform team. Yet the reason agent pilots stall is usually the data platform: missing definitions, unclear lineage, governance that agents cannot read. The data platform leader who fixes that becomes the person who made AI work. The one who waits is asked why it did not.
Why do broad promises fail?
Finance and leadership have heard many claims that AI will transform everything. A case built on that language competes with every other AI request. A case built on one measured task competes with almost nothing.
What does a scoped case look like?
Pick two or three tasks your team or your analysts do today, with a known cost in time. Euno publishes one example from AlphaSense: a 287-field legacy reporting table feeding 12 dashboards, 41 views and 303 users was rebuilt as a 13-model Kimball star schema in about 15 prompts in one session, against a manual estimate of 3 to 5 weeks, and a billing model redesign proposal took about two hours against an estimate of a day and a half. The shape is what to copy: a named task, a baseline estimate and a measured result.
Which costs belong in the case?
Include the platform price at the size you will reach, not only today's size (lesson 9). Include the people: an analytics engineer to write benchmark questions, a governance owner to turn rules into machine-readable form. Include what you avoid: migration projects you will not need if the platform reads the stack you have, and the cost of wrong answers reaching decisions.
How do you measure it?
Use the testing loop from lesson 7. Record accuracy and time-to-answer for your benchmark questions before the platform and after. Report those two numbers, plus the scoped tasks, every month for the first quarter.
What about the platform you already pay for?
Expect the question. If your data, definitions and agents stay inside Snowflake or Databricks, their native catalogs may cover much of the need (see platform-native options). If agent questions cross warehouses, transformation tools and BI tools owned by different teams, a third-party layer earns its place. Make that argument with your own stack diagram, not a vendor's.
What should the first ninety days deliver?
One working agent use case in production, a benchmark with a measured improvement, and a list of the next three use cases ranked by value. That is enough to fund the next stage. Present it as the data platform team's result, with the numbers your own benchmark produced, and let the next request build on it.
Key points
- Tie the budget to named tasks with a baseline and a measured result.
- Price the platform at the size you will reach and include the people.
- Answer the platform-native question with your own stack diagram.
Platforms in this lesson
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