Are you creating “A Single Source of Lies?”
Author
Rob Manne
| Published: August 11, 2026
A client recently asked their AI assistant to identify poor-fit prospects in their CRM.
The recommendation seemed logical.
But here’s what actually happened.
For years, RevOps nirvana has been to get all the company’s data in one place.
It’s called “The Single Source of Truth.”
Traditionally, companies struggled with separate, disconnected systems with unstructured and fragmented data.
Consolidating it was the goal. Either in one platform like a CRM, or through a series of inter-connected platforms.
Once the data is centralized and available, leaders then have the power to make decisions based on what’s actually happening in their business.
But here’s the thing: the Single Source of Truth relies on the data being correct.
As companies rush to connect LLMs and AI agents to their CRM data, I’m worried we’re creating something else:
A Single Source of Lies.
Is the AI lying?
Not quite. It’s just that AI is incredibly good at repeating whatever it’s given.
If your CRM contains:
- Duplicate contacts or companies
- Outdated lifecycle stages
- Missing attribution
- Incorrect owner assignments
- Stale company data
- Incomplete activity history
AI doesn’t know the context, outliers, or rationale.
It sees everything as facts.
And the LLMs will confidently generate insights, recommendations, forecasts, outreach, and executive summaries based on information that may be completely wrong.
As an example, you LLM may tell you that one prospect in your CRM is a poor fit. There are several deals that have been open a long time, with not many contacts attached.
But the reality is that these deals have actually been closed but not updated. The accounts team had started working directly on these projects.
This activity isn’t syncing with the CRM, so it appears that they didn’t move ahead. Accepting this as fact can have big consequences.
Hallucinations compound the problem
On top of that, there are the hallucinations. As good as your data may be, gen-AI has a tendency to bend the truth.
We’ve come across many examples where whole data points were made up. And there was no verification system in place to catch and correct the errors.
GPTZero recently reported that KPMG had to withdraw an AI report after researchers found that the majority of its citations were fabricated, distorted, or misleading.
The report included invented examples of AI adoption and inaccurate references that appeared legitimate.
If a global consulting firm can’t catch errors before basing decisions on them, what hope do SMBs and scaling companies have?
How to Keep Your Single Source of Truth Truthful
Don’t worry, we got you.
Follow these steps to ensure your Single Source of Truth stays on the straight and narrow:
1. Treat CRM Governance as AI Governance
Every property, workflow, lifecycle stage, and integration now influences AI outputs.
Data governance is a mandatory exercise now.
Here’s some good news: also thanks to AI, data hygiene and cleanup is easier to do than ever before.
2. Implement Data Validation & Monitoring
If you have unlimited Superadmins, it’s hard to keep your data on track.
Ensure you have worked out access levels based on role and expertise.
Building processes to catch bad data is important.
As an example, we build an Abnormal Data Dashboard for all our client’s HubSpot instances.
3. Build Confidence Scores
Not every CRM record deserves equal trust.
Imagine if AI could see:
- High Confidence
- Medium Confidence
- Low Confidence
based on completeness, recency, enrichment, and validation.
The quality of AI outputs would improve immediately.
4. Keep Humans in the Loop
AI should recommend, and humans should approve.
Especially when the output impacts:
- Forecasting
- Territory planning
- Customer segmentation
- Executive reporting
- Revenue projections
Build in a step of human verification – perhaps with some AI support – prior to accepting the data as correct.
AI amplifies CRM data issues and presents them with confidence
The old computing adage was ‘Garbage In, Garbage Out.’
But as AI becomes embedded in CRM systems, we’ve entered the era of ‘Garbage In, Gospel Out.’
In other words, these are bad outputs that people trust.
As you work to build a Single Source of Truth, you also have a new responsibility:
Making sure AI never amplifies bad data to turn it into a Single Source of Lies.
