If your AI agent is confidently giving wrong answers, the issue probably isn’t the AI.
It’s your data.
We’ve watched leadership teams roll out AI tools in and connected to HubSpot, ask what should be simple questions about pipeline, pricing, ICP or positioning — and get an answer that’s outdated, contradictory or flat-out wrong. The reaction is usually some version of “Huh, must be a hallucination.”
That explanation is convenient, but it’s missing an important truth.
AI hallucinations absolutely happen. But more often than not, what looks like “the AI making things up” is more often the AI surfacing conflicts, gaps and inconsistencies that already exist inside your systems. The AI isn’t inventing the mess. It’s reflecting it faster and more visibly than any human ever could.
What’s actually happening when AI gets it wrong
AI doesn’t “think” like humans. It reconciles inputs. When your systems contain multiple versions of the truth (e.g. different definitions, outdated records, half-enforced processes etc.) AI has no way to know which one is correct. So, it picks one.
Sometimes it picks the right one, and sometimes it doesn’t.
This is why AI answers feel confident and wrong at the same time. The model is actually doing exactly what it was designed to do with the information it has.
If humans were answering the same questions, they would come up with compensations:
- “Ignore that field, it’s never accurate.”
- “We don’t really use that stage anymore.”
- “That number is technically right, but…”
AI doesn’t have that context. It assumes the system is telling the truth.
“Hallucinations” are a Data Trust failure
This isn’t just a data cleanliness problem, because it goes deeper than that. It’s what we call a Data Trust problem.
If you have Data Trust, that means you can rely on your systems to:
- reflect reality
- use shared definitions
- enforce agreed-upon processes
- stay accurate as the business changes
Most companies don’t have this. They have data that’s “good enough” for dashboards and quarterly reviews, held together by human judgment and workarounds. This isn’t just anecdotal; according to research by Adverity, “45% of the data marketers use to make business decisions is incomplete, inaccurate, or out of date. And 43% of CMOs believe less than half of their marketing data can be trusted.”
And for some companies, maybe that’s worked in the past… until AI entered the picture.
AI removes the human buffer. It treats your systems as authoritative. And when those systems disagree with themselves, AI exposes it immediately.
Why this shows up first in your CRM
CRM is where AI failures become obvious because it’s where your strategy meets daily execution.
This is where things often break down:
- Sales and marketing use the same fields differently.
- Required fields aren’t actually required.
- Lifecycle stages mean different things in different regions.
- Deals are updated to satisfy reporting, not reality.
Humans learn how to work around this, but AI doesn’t. Even powerful tools like HubSpot’s AI features are only as good as the data it has access to.
Practically speaking, this means when an AI agent pulls from your CRM, whether for forecasting, enablement or customer communication, it inherits every shortcut, inconsistency and outdated assumption baked into the system. Not great.
If you don’t trust your CRM data today, AI will make that distrust impossible to ignore.
AI “hallucinations” are a lagging indicator
By the time AI is visibly wrong, the problem is already mature. The real failure happened earlier, when definitions weren’t enforced, processes were documented but not followed and exceptions became the norm
These issues already existed, and AI scaled them.
What used to be a quiet internal inconvenience becomes a customer-facing risk. Wrong recommendations. Misaligned outreach. Faulty forecasts.
And while it’s easier than addressing the underlying issues, blaming the AI misses the point.
What CEOs get wrong about “AI readiness”
A lot of AI readiness conversations I see tend to focus on tools, models and vendors – maybe a mention of data hygiene.
AI readiness is much more about operational discipline than it is about the technology. We all have access to the same LLMs. If your organization can’t consistently agree on and enforce what fields are critical, what correct looks like and who owns it, no AI initiative will be reliable.
The fix is boring but necessary
You don’t fix this by simply cleaning all your data – that’s a Band-Aid.
You fix it by:
- Identifying the small set of data that must be right
- Enforcing how that data is created and maintained
- Making drift visible instead of discovering it through failure
AI doesn’t need perfect data, but it does need stable rules and trusted sources. When those are in place, AI outputs become boring — and correct. That’s when it starts compounding value instead of exposing flaws.
AI readiness starts with Data Trust
The companies getting real value from AI aren’t the ones with the flashiest demos. They’re the ones that did the unglamorous work first: enforcing process, owning definitions and treating data as infrastructure, not exhaust.
If your AI is hallucinating, it’s confirmation: you have a Data Trust problem.
Interested in learning how we help clients establish Data Trust? Get in touch.
