When AI Becomes the First Consumer of Your Data
- Dave Findlay
- Aug 12
- 2 min read
For decades, we’ve built data for humans.
We’ve modelled it for clarity, aggregated it for speed, and visualized it for impact. Dashboards, charts, and reports were all designed to help people make decisions.
But we’re entering a new era where your first consumer might not be human at all.
LLMs and AI agents aren’t waiting for dashboards. They’re querying your warehouse or lakehouse directly, interpreting the results, and taking action.
Often, this action occurs before a person even knows there’s a decision to be made.
And that changes everything.
Your “audience” has changed
When humans consume data, we can explain the context, caveats, and nuances in a meeting or an email.
When AI consumes data, there’s no small talk. It takes what’s there at face value.
That means:
Your schema design matters more than ever. Consistent naming, clear relationships, and strong metadata aren’t nice-to-haves, they’re essential.
Ambiguity is your enemy. If your data model relies on tribal knowledge, an AI agent won’t pick it up.
Quality problems move faster now
When an AI system is surfacing insights in real time, the risk of bad data is amplified.
A human analyst might notice a number that “feels off” and investigate.
An AI won’t.
It will deliver the number confidently, on time, and to the wrong people if your pipelines aren’t monitored.
That means you need:
Automated quality checks embedded in your pipelines.
Real-time monitoring and alerts that trigger before an AI delivers faulty insights.
Clear data contracts so upstream changes don’t silently break downstream consumers.
Governance isn’t optional
If your first consumer is an AI, the stakes go up for governance.
You need to know:
Who (or what) has access to what data
How transformations are applied
What business logic is embedded along the way
This isn’t about slowing things down, it’s about creating safety nets that allow automated systems to operate without constantly tripping over errors.
A new measure of success
Historically, we’ve measured data success by human adoption:
• Are people logging into the dashboard?
• Are they using it to make decisions?
In an AI-first world, the metric changes.
Success is about whether the automated consumers of your data are making correct, relevant, and timely recommendations and whether those recommendations are trusted by the humans who act on them.
The takeaway
If your data team is still designing only for human eyes, you’re already behind.
The upcoming wave of data work will focus on developing systems that machines can utilize with equal or greater reliability than humans.
Because soon, the first “user” of your data won’t be a person looking at a dashboard.
It’ll be an AI making a decision.
At Fuse, we believe a great data strategy only matters if it leads to action.
If you’re ready to move from planning to execution — and build solutions your team will actually use — let’s talk.
