AI agents can reason. They can plan, summarize, and even write SQL.
But they can’t fix messy data.
That gap between reasoning and reality is where most AI projects stall, not because the models don’t work, but because the data underneath them is too fragmented to think with.
The hidden bottleneck
Every company wants to give their business users an AI assistant that can answer questions like:
"Which campaigns drove the most revenue this quarter?"
Or even more ambitious:
"If we increase ad spend by 10% across platforms, what happens to conversion cost?"
The problem isn’t the prompt.
It’s that the model has to reason over conflicting definitions of “campaign,” multiple “revenue” columns, and data scattered across CRMs, ad tools, and spreadsheets.
The result is confident answers that are wrong.
Large language models can generate logic, but they can’t repair the structure that logic depends on.
They don’t know which system is right when numbers disagree, and even if you’re able to give them guidance, it’s forgotten the moment you start a fresh conversation.
Reasoning requires ground truth
Think of an LLM as a sharp analyst dropped into a disorganized office.
They can ask smart questions, make connections, and even propose insights, but if every folder has its own version of “customer_data_final_v2,” they’ll still get lost.
LLMs need a consistent, unified layer underneath them to reason safely.
That layer provides:
- Entity resolution (so “user,” “lead,” and “customer” point to the same person)
- Normalization (so “spend” always means the same thing)
- Proof and lineage (so you can see why the AI answered the way it did)
Without it, the reasoning is just pattern-matching on chaos.
Example: Why AI struggles with marketing data
Consider a simple question:
"Which campaigns had the highest ROI this month?"
The data for that question usually lives across multiple tools:
- LinkedIn Ads: campaign performance data such as impressions, clicks, conversions, and spend
- Reddit Ads: campaign performance data that captures similar metrics but uses different naming conventions, attribution logic, and data structures
- Google Sheets: manually tracked pipeline and revenue data that connects campaign activity to actual business outcomes
At first glance, this looks easy to join. Both ad platforms are reporting the same kind of metrics.
In practice, the schema, field names, and even metric logic differ enough that an LLM can’t cleanly reconcile them.
For example:
- Reddit might call a column “Total Spent (USD)” while LinkedIn calls it “Amount Spent.”
- Reddit’s conversions might be attributed by “View” and “Click,” while LinkedIn uses “Post Click” and “Post View.”
- The underlying campaign IDs differ across both systems, and neither directly ties back to the deal IDs tracked in Sheets.
So the problem isn’t that the data types are different.
It’s that the definitions, identifiers, and relationships are inconsistent across tools, and no AI model can reliably resolve that structure automatically.
What that inconsistency looks like in practice
Problem Example:
- Mismatched identifiers: LinkedIn uses “Campaign ID,” Reddit uses “Campaign Name,” and the Sheet uses free-text entries like “Spring Promo.” The AI can’t reliably join them.
- Inconsistent schemas: LinkedIn and Reddit measure engagement differently. One uses CTR, the other uses CPE. The model treats them as comparable.
- Misaligned time frames: Reddit reports in UTC, LinkedIn in local time, and the Sheet updates once a week. The AI merges mismatched periods.
- Different metric logic: The Sheet’s “revenue” column includes both “won” and “lost” deals, while ad platforms count all spend as active.
- No persistent schema: Even if the AI fixes these issues once, it doesn’t remember them for the next query.
The output looks intelligent. It returns numbers and charts, but the logic underneath is incoherent.
What looks like a small naming issue becomes a silent failure mode when these systems feed an LLM.
The model isn’t lying; it’s improvising with incomplete context.
To answer that question correctly, several structural steps need to happen first:
- Entity resolution: connect “Spring Promo” across Sheets, LinkedIn, and Reddit even when IDs or names differ.
- Normalization: unify currencies, time zones, and date formats.
- Semantic alignment: define what “revenue” and “ROI” actually mean across all systems.
- Proof and lineage: show exactly how those numbers were derived.
None of this can be done reliably by an LLM alone.
It requires a persistent data layer that understands relationships, definitions, and meaning.
Once that layer exists, the AI can reason instead of guess.
The same prompt, “Which campaigns had the highest ROI this month?”, now produces consistent, explainable results across every source.
In AstroBee, you can literally see every system mapped to the same entity definitions so “customer,” “lead,” and “user” finally mean the same thing.
And this story repeats everywhere
Marketing is only one example.
The same pattern plays out across every function in a business:
- Product managers want to connect feature usage data with revenue outcomes, but product analytics and billing rarely share entity definitions.
- Sales teams track leads in CRMs that don’t match marketing definitions of “qualified,” making pipeline reports unreliable.
- Operations and finance rely on metrics from systems that calculate costs differently across regions or business units.
Each role faces the same challenge: they know what questions to ask, but not how to unify the context to get trustworthy answers.
Without a data layer to standardize meaning, every query becomes a one-off cleanup job instead of a reusable insight.
Business users can’t build this infrastructure themselves
Before AstroBee, the only people who could create this kind of foundation were data engineers and analytics teams.
That meant every marketing manager, product lead, or ops analyst had to file tickets and wait weeks just to get clean data to work with.
It’s the same bottleneck data scientists faced years ago.
They could train models, but couldn’t deploy them without infra engineers.
Now business users face the same wall.
They understand the business context better than anyone, but don’t have the tools to access or unify their data effectively.
They don’t need another dashboard or prompt template.
They need a system that builds the data foundation for them, so AI can actually reason with the business context they know best.
The foundation matters more than the model
A great model sitting on messy data is like a Ferrari on gravel.
No matter how powerful it is, it’s not going anywhere fast.
No matter how powerful the LLM is, if the data foundation is incomplete, automated reasoning will fail.
The real innovation isn’t another smarter LLM.
It’s the layer that makes your company’s data understandable, traceable, and ready for reasoning.
That’s what AstroBee focuses on, creating a unified data layer that lets business users query, explore, and automate decisions confidently.
Because the future of AI in business isn’t about bigger models.
It’s about giving every user solid ground to stand on.
Closing thought
AI doesn’t fail because it’s not smart enough.
It fails because it doesn’t understand your business.
The fix isn’t a better chatbot.
It’s a better foundation.
If this challenge sounds familiar, we’re always open to comparing notes.
Feel free to reach out at tc@astrobee.ai to talk through what a cleaner data foundation could look like.
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