What is identity drift in AI search

Short answer

Identity drift is when AI systems pull old, conflicting or misaligned signals over time, causing the model to describe a business in a way that no longer matches reality. This happens slowly and is caused by leftover records and mixed data across the web.

What identity drift looks like

• The model mentions services you no longer offer.
• You are placed in the wrong industry or category.
• Your business location or coverage area is incorrect.
• The description feels half right and half outdated.

It is not a full error — it is a slow shift away from accuracy.

How identity drift happens

Drift rarely comes from one mistake. It builds from leftover information that was never corrected or cleaned.

• Past pages and listings remain indexed and visible.
• Old offers remain active even after being discontinued.
• New language drifts from the original service scope.
• Profiles expand without controlled structure.

Small shifts compound, and the model begins to rewrite the business identity based on the mixed picture it sees.

Why identity drift is dangerous for AI visibility

A business with identity drift receives fewer clear recommendations. The model hesitates because the story no longer lines up cleanly. You become visible but not precise, known but not trusted.

Drift means the system cannot confidently place you — so it names someone else.

Business signs of identity drift

• You get visibility but the description is wrong or incomplete.
• Your business is used as an example but with outdated context.
• Search tools know your brand but not your current scope.
• AI overlaps your business with a similar competitor.

You are seen — but as the wrong version of yourself.

How Business Visibility Group corrects identity drift

Business Visibility Group identifies where the drift started, isolates outdated signals and restores one correct version of the business across the sources AI systems read from.

1. We locate the sources that contain the outdated identity.
2. We compare them to the current business reality.
3. We define the correct version using plain language.
4. We repair records and close or overwrite conflicting ones.
5. We reinforce the corrected identity with reference pages.

Once drift is removed, AI no longer has two versions to choose from.