Part 1: How Often It Happens
The rate each outcome occurs at, drawn from SHRM, Forrester, Ebsta × Pavilion, SaleSo, ICONIQ Growth and LinkedIn Talent Trends — not a single company's anecdote.
Before any dollar figure is worth trusting, there is a more basic question underneath it: how often does each of these three outcomes actually happen? Not in a worst-case scenario — under average hiring circumstances, the same conditions most AI tech companies operate under every day.
This is the first of five reports on what an enterprise sales hire actually costs. Each one answers a single question across all three outcomes — the slow-hire, the low-hire, and the mis-hire — using the same published research. This one starts with frequency, because a cost model is only as credible as the rate it's built on.
Are These Edge Cases, or the Baseline?
The rate each outcome occurs at, drawn from SHRM, Forrester, Ebsta × Pavilion, SaleSo, ICONIQ Growth and LinkedIn Global Talent Insights — not a single company's anecdote.
100% of hires
Modeled at 100% of hires — a certainty with a variable magnitude, determined entirely by how long the search runs beyond what it should. Average time-to-fill for mid-level roles is 42 days; AI tech searches routinely run 60–90.
~60% miss the number
78% of sellers missed quota in 2025 — up from 69% in 2024 — and only 24.3% exceed their yearly quota outright. Just 40% of AI tech enterprise AEs hit or exceed — and 14% of sellers generate 80% of all revenue.
30–35% within 18 months
Leadership IQ's study of 20,000+ new hires found 46% fail within 18 months — 89% of the time for attitude and cultural fit, not lack of skill. This analysis models the enterprise sales-specific rate conservatively at 30–35% of that broader pattern. Separately, 75% of employers report making at least one bad hire, and skipping a standardized interview process makes one roughly five times more likely.
One Is Certain. One Is Likely. One Is a Planning Assumption.
These are the probability weights this entire series applies to every dollar figure that follows — the share of hires each outcome is expected to affect.
These are the probability weights the cost model applies throughout — full methodology.
Under average hiring circumstances, these outcomes are not exceptions. They are the statistical baseline.
Demand Is Growing More Than Three Times Faster Than the Talent Pool
The reason enterprise AI searches routinely run past baseline isn't process. It's supply — the qualified pool is not growing at the rate the roles are.
The supply–demand imbalance behind AI tech search timelines — LinkedIn Global Talent Insights.
None of these are catastrophic scenario numbers. They are the data-driven output of documented vacancy rates, published quota attainment distributions, and industry fit failure rates — the same inputs, applied honestly, that this series uses to build every dollar figure that follows.
Part 2 takes these same three rates and asks the next question: once one of these outcomes is underway, how long does it actually run before anyone acts on it?
Vacancy drag: 100% probability weight — the magnitude scales with days above baseline. Quota miss: ~60%, from SHRM, Forrester, Ebsta × Pavilion, SaleSo and ICONIQ Growth quota-attainment research. Fit failure: 30–35% within 18 months — this analysis's conservative narrowing of Leadership IQ's broader 46% new-hire-failure finding to the enterprise sales-specific culture/fit case.
Research compiled May 2026 · Sources: SHRM, RepVue, Bridge Group, Ebsta × Pavilion, BLS, McKinsey, Robert Half, LHH, Gartner, LinkedIn Talent Trends, SaleSo, Seattle Corporate Search
These are planning numbers. The hire is the variable.
Twenty minutes, no pitch — what the role really needs, and whether this is a search worth running.