Overview
AI-mediated search changes where product discovery and evaluation occur, making generated answers and conversations part of a journey that conventional analytics only partly sees. The series connects eye-tracking evidence on AI Overviews with passive-log evidence on observed LLM exposure, separating attention shifts and visible channel-mix changes from claims about trust, effort, speed, or causation. It moves from interface attention to buying-journey structure, measurement architecture, bounded empirical results, and dashboard guardrails. The practical conclusion is that teams should update the measurement boundary before reallocating credit or budget, because a shorter visible path may reflect relocated evaluation rather than a faster decision.
Learning outcomes
- AI-mediated search can move evaluation into generated answers or conversations, making the external journey look shorter without proving less decision effort.
- In the tested lab setting, AI Overviews attracted more early visual attention while ranked results retained rank-dependent visibility.
- Observed LLM exposure is associated with a changed visible pre-intent channel mix; adjusted evidence does not establish general acceleration or causation.
- A defensible dashboard separates exposure from journey augmentation and treats shorter visible timing or direct transitions as observability alarms before reallocating credit.
Keywords
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PlansFAQ
Does AI-mediated search make buying journeys faster?
Not on the available evidence: adjusted passive-log estimates do not establish a general acceleration effect. A shorter visible path may reflect comparison moving into an unobserved conversation.
How do AI Overviews affect traditional search results?
They attract more early visual attention but do not eliminate ranked results or rank-dependent engagement. Traditional pages may remain useful for detail or verification.
What is the difference between LLM exposure and journey augmentation?
Exposure is a user-level state showing any detected LLM touchpoint in the observation window; augmentation is a journey-level state requiring a canonical LLM touchpoint before observed intent.
What should an attribution dashboard measure when AI enters product discovery?
It should keep LLM separate and report exposure, journey augmentation, visible channel mix, timing, transitions, and label quality distinctly before credit or budget is changed.