Case Study · HP IT & Consulting | GTM Advisory Project

Used AI to improve pipeline predictability

Compared closed-won and closed-lost opportunities, identified four buyer signals absent from lost deals, and redesigned opportunity stages around customer behavior.

Pipeline predictability improved 8%Buyer-signal stage definitionsAI-powered reporting workflow

Context

An advisory engagement with HP IT & Consulting focused on a question every revenue organization faces: why does pipeline that looks healthy fail to convert? My role was GTM advisor, bringing an AI-assisted analytical approach to pipeline design.

Business Challenge

Opportunity stages were defined by seller activity rather than buyer behavior, so the forecast systematically overstated late-stage pipeline health.

What I Owned

The analytical methodology and the redesign of stage definitions — comparing closed-won and closed-lost opportunities with AI-assisted analysis and turning the findings into operational stage criteria and reporting.

What I Built

A buyer-signal framework: AI-assisted comparison of won and lost deals surfaced four buyer signals consistently absent from losses; opportunity stages were redesigned around those behaviors, with an AI-powered reporting workflow to keep the definitions honest.

Measurable Results

8%
Improvement in pipeline predictability
4
Buyer signals identified as absent from lost deals
AI
Powered reporting workflow installed

Leadership Scope

Advisory scope: partnered directly with GTM leadership; the framework informed the ABX strategy built for HP IT & Consulting during the same engagement.

Proof

This methodology is the subject of part three of the Predictable Pipeline Revenue series on this site, presented at CRO Exchange, San Francisco 2026.