AI hallucinations are frequently just a mirror of a company’s fragmented internal files. To stop the errors, teams need to move past the glitch narrative and fix the outdated data foundations feeding their models.
When an AI agent confidently quotes a 2022 price list in 2026, it is rarely a technical malfunction. It is usually just reading the only file it can find. We call these errors hallucinations, but they are often just a data hygiene problem.
The data crisis hiding in your stack
An Adverity study found that 45% of marketing data is inaccurate. In most offices, Salesforce records might contradict Microsoft SharePoint files, while the latest strategy lives exclusively in a Slack thread.
When an AI engine scans these sources, it lacks the context to know which PDF is the obsolete draft. As Tracy Rohan explains:
The AI is simply reflecting that mess back to you at scale… When your foundational data argues with itself, AI doesn’t know which version to believe. So it picks one.
Tracy Rohan, Founder & Chief Strategist EDGE
This creates several friction points:
- Three teams operating with three different definitions of an ideal customer profile
- Marketing and sales disagreeing on what constitutes a conversion
- AI picking a random version of truth when data argues with itself
Why clean data matters more than smart AI
Everyone wants the sexy moment where the agent finally works in a demo. But the real utility comes from the boring, foundational work of data discipline.
So often companies spend six figures on AI infrastructure while their product catalog still has duplicate entries from a 2021 migration.
- AI works exactly as designed; the problem is the input
- Systems cannot clean themselves yet
- Messy data results in amplified errors across every customer interaction
The real cost of bad data hygiene
When your data is inconsistent, mistakes are inevitable and expensive. This is happening every week in enterprises that have invested millions in AI transformation.
Common failure points include:
- Sales agents giving pricing that changed six months ago
- Content tools pulling brand messaging from 2020
- Lead scoring using ICP criteria that marketing and sales never agreed on
| Viewpoint | The Friction Point | The Solution |
| Tracy Rohan | AI is just a messenger for messy data | Ruthless audit of AI-accessible files |
| Adverity Research | 45% of data feeding AI is currently wrong | Establishing one source of truth |
| Enterprise Reality | AI scales the mess across all interactions | Assigning a dedicated data owner |
Five steps to fix your data foundation
You do not need a massive transformation initiative; you need discipline.
- Audit what your AI can see: Pull every document and spreadsheet. Be ruthless about what stays
- Create one source of truth: Pick one system for every definition that matters to your business
- Set expiration dates: Every asset should have a “valid until” date so it automatically disappears when irrelevant
- Test what your AI knows: Ask basic questions about current pricing or differentiators to find the gaps
- Assign ownership: One person must be responsible for the “Source of Truth” or the initiative will die
Foundation first, fix the mess
If you do not fix the mess, AI will scale the mess. Deploying powerful AI on top of chaotic data can actively damage your brand and customer relationships.
You can have the most sophisticated AI model in the world and the best prompts, but none of it matters if you are feeding it garbage. Remember, your AI isn’t hallucinating; it is telling you exactly what your data looks like.
Editorial Article by: Tracy Rohan, Chief Strategist & Founder of EDGE
