For years, the success of a data platform was measured by one thing:
How easily people could build dashboards.
Today, I think that's changing.
More and more enterprise data is being consumed by AI agents instead of analysts. That changes what a "good" data platform looks like.
The problem is that most data platforms were never designed for AI.
Dashboards Hide Complexity
When a human opens a dashboard, they already know a lot.
They know which KPIs Finance trusts.
They know which report leadership uses every Monday.
They know that two customer tables exist, but only one should be used.
Most of that knowledge never appears in the database.
Humans simply carry it with them.
AI doesn't.
AI Doesn't Want Charts
AI isn't looking at your dashboard.
It's looking at the underlying data.
If that data contains three revenue definitions, duplicated customer IDs, or five possible join paths, the AI has no way to know which one represents the business truth.
The model can generate SQL.
That doesn't mean it understands your business.
What Should a Modern Data Platform Provide?
Instead of focusing only on BI performance, I've started thinking about a different checklist.
Can the platform answer questions like these?
- Which relationships between tables are actually trusted?
- Which metric definition is the official one?
- Which tables are deprecated?
- Which joins are safe to reuse?
- Which business terms mean the same thing?
These questions aren't about analytics.
They're about context.
A Small Example
Imagine asking an AI agent:
Show quarterly revenue by customer.
Finding the sales table is easy.
The difficult part is everything that comes next.
- Which customer table?
- Gross revenue or net revenue?
- Calendar quarter or fiscal quarter?
- Should internal transactions be excluded?
- Which join path has already been validated?
Those decisions are usually made by experienced analysts.
AI needs that knowledge too.
This Changes Platform Design
I don't think data platforms will disappear.
But I do think their priorities will change.
Traditional platforms optimized for:
- dashboards
- reports
- SQL performance
- human exploration
AI-native platforms will also need to optimize for:
- trusted relationships
- shared business definitions
- governed metrics
- reusable organizational knowledge
The data hasn't changed.
The consumer has.
My Take
Every major shift in software has introduced a new primary user.
Web browsers changed frontend development.
Mobile phones changed application design.
Cloud changed infrastructure.
I think AI agents are going to change enterprise data architecture in the same way.
If we're still building data platforms only for analysts, we're solving yesterday's problem.
The next generation of data platforms won't just help people understand data.
They'll help AI understand it too.
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