What AI Tools Say About Laura Lake — 32 Days Later
18/35 → 26/35. Same five queries. 32 days later.
18/35 → 26/35. Same five queries. 32 days later.
94% of buyers use AI to research vendors. Most organizations have their AI pointed in the wrong direction. Here’s what it costs.
Your pipeline doesn’t show it and your CRM can’t track it, but AI is already shaping how safe you look to cautious stakeholders. It defines the ghost objection and shows how to diagnose it inside your signal architecture.
You tightened the ICP. You cleaned up account selection. You layered in intent data. The team ran the playbook correctly. The number didn’t move. Here’s what precision targeting doesn’t fix.
AI answer engines have been forming your shortlist for at least two years. The CMO sees content, the CCO sees earned media, Sales sees the pipeline after the verdict is already in — but no one is accountable for reading what AI actually decided in the middle. This piece names the missing seat on the org chart and writes the job description for the AI Buyer Behavior Analyst: the role that reads what AI is saying about you and turns those signals into pipeline decisions.
Gartner just handed answer engine optimization to Communications. Their own numbers describe a buyer-behavior problem Marketing and Revenue haven’t staffed for yet.
Dark social isn’t a measurement gap — it’s the Silent Committee™ at work. Here’s the structural condition underneath it and what revenue teams can actually do about it.
The most credible audit is the one you run on yourself. This diagnostic applies the AI-Ready Buyer™ methodology to AI-Ready Buyer™ Research — five queries, three platforms, verbatim results, Trust Layer™ score, and the activation plan already in motion. The query set isn’t proprietary. What it surfaces about your signal environment is.
Sales enablement in the AI era starts before the first call — inside the 70% of the buyer journey your team never sees.