Recruiting Tech & Vendor Evaluation Rubric

How I evaluate tools

Most tools fail for the same reasons: weak inputs, unclear ownership, no audit trail, outputs nobody trusts, workflows that don't match reality.

This is the checklist I use to separate useful tools from demo theater.

What I check first (before features)

• Use case: What job does the tool do? One core recruiting task per tool.
• Risk classification: Does it draft, summarize, rank, recommend, or affect candidate movement?
• Inputs: What does it need to produce a good output? Where does that data live today?
• Workflow fit: Who touches it, when, and how often? If it adds steps, adoption drops.
• Process reality: Which source-system workflow is the vendor assuming is standard?
• Proof: What does "good" look like, and how will we measure it?

The rubric

Score each line 1-5. Anything under 3 is a risk you'll pay for later.

Some answers are hard stops. If the vendor can't explain candidate impact, data retention, audit logs, or human override, the score is not the problem. The tool is not ready.

Output quality (in real workflows)

• Outputs are usable without a human rewriting everything.
• Results are consistent across similar inputs.
• The tool behaves predictably when information is missing.

Control and transparency

• You can see what influenced the output (inputs, rules, prompts, settings).
• You can identify the prompt or rule version, model or tool version where available, configuration, timestamp, and reviewer.
• You can reproduce an output later if someone asks, "why did it say that?"
• You can override, correct, and re-run without breaking the workflow.

Data handling and access

• Clear answer to: what data is sent, stored, retained, and where.
• Clear answer to: whether customer data trains the model.
• Clear answer to: subprocessors, retention controls, deletion controls, and admin access.
• Easy export of inputs, outputs, logs, reviewer action, and final disposition.
• Permissions make sense (recruiter vs hiring manager vs admin).

Compliance and risk

• You can explain how it's used in the process, in plain language.
• You know whether it can affect candidate movement.
• There is an audit trail for decisions, recommendations, and overrides.
• Human review is documented and can override the system.
• The vendor can explain notice, audit, and bias-testing support where required.
• Guardrails exist for sensitive content (protected class references, medical, compensation, etc.).

Integration and friction

• It fits your ATS/CRM reality, even if the integration is imperfect.
• The source process is documented before the new tool is configured around it.
• It removes work instead of relocating it to another screen.
• It doesn't require constant babysitting to stay accurate.

Adoption and ownership

• A clear owner exists (not "the team").
• Actual users were represented in discovery, not only senior leaders.
• UAT, defect triage, training, and go-live support have named owners.
• The SME is not the whole implementation plan.
• Training is short and specific.
• The system survives process changes without collapsing.

Demo questions that surface problems fast

Ask these in the first 10 minutes. The answers tell you more than the product tour.

• "Show me the exact input the tool uses."
• "Which source-system process are you assuming is standard?"
• "Who from the actual user group was involved in discovery?"
• "What approvals are required after purchase but before use?"
• "Where do outputs live after I generate them?"
• "Can I export logs for an audit?"
• "What happens when the input is incomplete?"
• "Where is human review required, and how is it enforced?"
• "Who owns UAT, defect triage, training, and go-live support?"
• "What happens if the workflow SME is unavailable?"
• "Does this tool rank, score, recommend, or affect candidate movement?"
• "Does customer data train the model?"
• "Can a human override the output, and is that override logged?"
• "What notices, audit records, or bias-testing support do you provide where required?"
• "What does 'good' performance mean here, and how do you measure it?"

Red flags

• "It learns from your data" with no clear retention boundaries.
• No exportable history of outputs.
• Results can't be reproduced.
• The tool nudges high-risk decisions without governance (decision outcomes, rationale).
• The vendor calls it workflow support, but the product changes candidate order, priority, or status.
• The demo assumes a clean process your team does not actually have.
• The implementation plan depends on one internal SME remembering how everything works.
• "Human in the loop" means someone can click approve, but not meaningfully review or override.
• Claims that replace recruiters instead of supporting workflows.

Why the rubric is stricter now

Recruiting AI is no longer just a product-fit question. Current guidance and laws put more weight on risk classification, auditability, notice, bias testing, and human review.

• NIST: AI Risk Management Framework
• NIST: AI 600-1, Generative AI Profile
• NYC DCWP: Automated Employment Decision Tools
• Colorado SB24-205: Consumer Protections for Artificial Intelligence
• EU AI Act: Regulation (EU) 2024/1689

Pressure-test fast

Use the demo questions above against your real workflow. If you can't see the exact inputs, export outputs, and reproduce results, you're buying demo theater.