# Free AI Assessment, Scoring & Assurance Resources

Part of the PeerLab [Sales Enablement Resource Library](https://www.peerlab.ai/library/). Every resource is published in full, free, with no sign-up.

- [AI Copilot Quarantine Decision Framework: Live Call Readiness](https://www.peerlab.ai/library/ai-copilot-quarantine-decision-framework-live-call-readiness/): Framework. A scored decision tool that tells managers exactly which reps earn copilot access during live calls, what rules apply at each phase, and how to run the post-call debrief that makes the whole system work.
- [AI Roleplay Scoring Vendor Vetting Checklist (50 Questions)](https://www.peerlab.ai/library/ai-roleplay-scoring-vendor-vetting-checklist-50-questions/): Checklist. Fifty structured questions to put to any AI scoring vendor before you trust its output with real learner data, organised across five risk areas specific to AI-scored sales assessment. Each question includes a plain-English note on what a weak or evasive answer looks like and why it matters.
- [AI Scorecard Inherited Validation Checklist](https://www.peerlab.ai/library/ai-scorecard-inherited-validation-checklist/): Checklist. A structured checklist for validating an AI scoring rubric you didn't build, before you stake your team's development decisions on its output. Covers the five most common failure points in pre-built scorecards and gives you a concrete test for each one you can run within a week.
- [AI Scoring Blind Spot Register: What a Transcript Cannot Show](https://www.peerlab.ai/library/ai-scoring-blind-spot-register-transcript-limits/): Checklist. A structured checklist of every sales competency that transcript-based AI scoring physically cannot assess, with the specific failure mode each gap produces and the supplementary evidence needed to close it. Use this before extending trust to any AI score, to draw a permanent boundary around what the model is and is not being asked to judge.
- [AI Scoring Calibration Session Guide for Enablement Owners](https://www.peerlab.ai/library/ai-scoring-calibration-session-guide-enablement-owners/): Guide. A structured, repeatable process for stress-testing your AI scorecard against human judgment before scores reach a learner. Run this before every new cohort to catch systematic drift early and document agreed fixes to thresholds or rubric language.
- [AI Scoring Criteria Constructability Audit: Can the Model Actually See It?](https://www.peerlab.ai/library/ai-scoring-criteria-constructability-audit/): Scorecard/Rubric. A criterion-by-criterion rubric that pressure-tests every item on your AI roleplay scorecard against four mechanical dimensions before a single live assessment runs. The output is a written record of which criteria are safe to automate, which need a human overlay, and which should be cut from the AI layer entirely.
- [Human vs. AI Score Disagreement Protocol (2-Band+ Gaps)](https://www.peerlab.ai/library/human-vs-ai-score-disagreement-protocol-two-band-gaps/): Framework. A step-by-step triage framework for when a human marker and an AI score diverge by two bands or more on a sales roleplay or call assessment. Covers the five root causes, a decision tree with four possible outcomes, and ready-to-use language for communicating the result to the learner and to HR.