How Vertex Industrial Partners used Value Creation AI to conduct a comprehensive sales analysis — uncovering $3.1M in recoverable revenue, exposing critical pipeline gaps, and rebuilding their entire go-to-market strategy around what the data actually showed, not what management assumed.
Vertex Industrial Partners had spent the previous 18 months scaling their sales team from 12 to 24 reps. Headcount doubled. Revenue grew only 18%. Win rates had dropped from 34% to 24%. Pipeline was bloated with deals that hadn't moved in 60+ days, and the monthly forecast calls had become a ritual of disappointment — the number always came in 30–40% below what was called.
The VP of Sales was coaching based on intuition — sitting in on 3–4 calls per week, reviewing whichever deals appeared most interesting. No one had a systematic view of why deals were being lost, which reps were following the playbook, or where in the funnel Vertex was most broken. They were flying blind at 180 miles an hour.
Value Creation AI conducted a complete sales analysis — 8 dimensions, 18 months of deal history, every call, every rep, every lost deal — and delivered not just the diagnosis but a ranked action plan with projected revenue impact for each recommendation.
"We thought we had a pricing problem. AI showed us we had a discovery problem. That single reframe saved us from an entirely wrong strategy."
— VP of Sales, Vertex Industrial PartnersValue Creation AI's full sales analysis covers every dimension of sales performance — from individual rep behavior to systemic pipeline health to ICP accuracy. Each dimension cross-referenced against Vertex's 18 months of deal history, call recordings, CRM data, and competitive intelligence.
On paper, Vertex had a 3.4× pipeline coverage ratio — comfortable for their quarterly targets. But the AI pipeline audit found that 31% of the deals in CRM had zero activity in 60+ days, had been moved forward manually by reps to "look good," or were in stages that didn't match the actual conversation history. Real, active pipeline was 2.1× — dangerously below the 3.5× minimum needed.
| Stage | Reported $ | AI-Verified $ | Phantom $ | Health |
|---|---|---|---|---|
| Prospecting | $2.8M | $2.1M | $700K | Soft |
| Discovery | $3.4M | $2.2M | $1.2M | Poor |
| Demo / Evaluation | $4.1M | $3.0M | $1.1M | Soft |
| Proposal | $3.2M | $2.9M | $300K | Healthy |
| Negotiation | $1.8M | $1.6M | $200K | Healthy |
| Total Pipeline | $15.3M | $11.8M real | $3.5M phantom | 2.1× real |
AI analyzed every lost deal across 18 months — the stage where it was lost, why it was logged as lost, what the call recordings actually showed, and which competitors were mentioned. The patterns were unmistakable once the data was synthesized across hundreds of deals.
For the first time, Vertex's leadership had an objective, consistent view of all 24 reps — not based on anecdote or recency bias, but on every behavioral metric across every call. Four specific behaviors explained 82% of the win-rate variance across the team.
"The analysis told us we were training the wrong skill, targeting the wrong companies, and managing the wrong metric. Fixing all three in parallel is why the results compounded so fast."