AI Recruiting Pilot Evaluation Worksheet
Pilot programs for AI recruiting tools routinely fail to produce actionable data — either because success was never defined upfront, or because the evaluation team spent the pilot period on configuration rather than measurement. This worksheet structures the pilot into three phases and defines the specific data points to collect at each stage. The goal is to exit the pilot with a documented, defensible scale decision.
Why this matters
Vendors know that pilots with vague success criteria tend to roll into contracts. Define your success criteria before the vendor has any involvement in the evaluation — and make those criteria specific, measurable, and independent of the vendor's own analytics.
How to use this tool
Complete the pre-pilot setup section before the pilot starts. Assign each measurement to a specific owner and a specific data source. Conduct weekly check-ins during the pilot to document progress against each metric. At pilot conclusion, fill in the decision criteria section and present the results to stakeholders with a clear scale / stop / extend recommendation.
The Tool
4 sectionsPre-Pilot Setup (Complete Before Day 1)
Every field in this section should be completed before the pilot begins — not during.
Pilot scope
Which roles, teams, and locations are included in the pilot? What is excluded? Be explicit about scope boundaries.
Baseline metrics
What is the current time-to-first-screen, time-to-hire, and manual screening hours per filled role for the roles included in the pilot? Pull this from your ATS before the pilot starts.
Primary success metric
Choose one. Options: reduction in time-to-first-screen, reduction in manual screening hours per filled role, improvement in quality-of-hire proxy metric, or improvement in candidate completion rate versus current process.
Secondary metrics
Up to three. Completion rate, ATS data quality (are scores landing in the right fields?), recruiter adoption rate (percentage of eligible candidates sent through AI screen), and hiring manager satisfaction score.
Scale threshold
Define the specific outcome that will trigger a scale recommendation. Example: 'Time-to-first-screen reduces by 30% or more for included roles, with recruiter adoption above 80%.' This threshold must be documented before results are known.
Stop threshold
Define the specific outcome that will trigger a stop recommendation. Example: 'Completion rate below 40%, or ATS write-back failure rate above 10% of interviews completed.'
Data owners
Who is responsible for pulling each metric at each checkpoint? Name specific individuals — not teams.
Phase 1: Configuration and Baseline (Days 1–14)
This phase is for setup, not measurement. Do not use Phase 1 data in the final evaluation — the system is not in steady state.
ATS integration configuration complete
Date: ___ / Owner: ___
Interview scripts drafted and approved for each pilot role type
Date: ___ / Owner: ___
Test candidates run (minimum 5 per role type, reviewed for ATS write-back accuracy)
Date: ___ / Owner: ___
Recruiter training completed (all recruiters handling pilot roles)
Date: ___ / Owner: ___
Baseline metrics confirmed and documented (pull from ATS pre-pilot data)
Date: ___ / Owner: ___
First real candidate invited through platform
Date: ___
Phase 2: Active Measurement (Days 15–45)
Run weekly check-ins. Document the numbers — do not rely on memory at the final review.
Week 3 check-in
Invitations sent: ___ / Completions: ___ / Completion rate: ___% / ATS write-back errors: ___
Week 4 check-in
Invitations sent: ___ / Completions: ___ / Completion rate: ___% / ATS write-back errors: ___
Week 5 check-in
Invitations sent: ___ / Completions: ___ / Completion rate: ___% / ATS write-back errors: ___
Week 6 check-in
Invitations sent: ___ / Completions: ___ / Completion rate: ___% / ATS write-back errors: ___
Recruiter adoption rate (week 6)
% of eligible candidates sent through AI screen: __%
Recruiter feedback (week 6 survey)
Average score on: interview quality, ATS data usefulness, workflow disruption (1–5 each)
Hiring manager feedback (week 6)
Are AI-screened candidates reaching interviews better prepared / better qualified? (Yes / No / Mixed)
Phase 3: Evaluation and Scale Decision (Days 46–60)
Final primary metric result
Baseline: ___ / Pilot result: ___ / Change: ___% / Met threshold? Y / N
Final completion rate
Total invitations: ___ / Total completions: ___ / Rate: ___% / Met threshold? Y / N
ATS write-back accuracy
Total interviews completed: ___ / Correct write-back: ___ / Accuracy rate: ___% / Met threshold? Y / N
Recruiter adoption rate (final)
% of eligible candidates sent through AI screen: __% / Met threshold? Y / N
Unexpected issues documented
List any integration failures, candidate complaints, compliance questions, or recruiter objections that arose during the pilot.
Scale / Stop / Extend recommendation
Based on the above: ___ / Rationale: ___
If scaling: proposed rollout scope and timeline
___ roles / ___ locations / target go-live: ___
If stopping: primary reason and alternative next step
___
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