Generative AI vs Statistical AI in recruiting
Most "AI" pitches blur together, but under the hood different tools behave very differently. This section explains, in plain language, the difference between generative and statistical approaches and how each one tends to show up in real recruiting tasks. The goal is to give recruiters and TA leaders enough clarity to choose an approach that matches the work they need done.
All views are my own. Examples are generalized or anonymized and do not reflect any single employer's confidential data, systems, or metrics.
Why this matters
Vendors pitch "AI-powered recruiting tools" without specifying which kind. This matters. Generative and statistical AI are different technologies with different failure modes.
The confusion: assuming "AI" means one thing.
Two kinds of AI
Generative AI
Generative AI creates new text based on patterns learned from massive datasets. It responds to prompts, drafts content, and adapts to instructions.
Examples: ChatGPT, Claude, Gemini.
Statistical AI
Statistical AI finds patterns in data to predict or classify. It scores, ranks, or recommends based on historical inputs.
Examples: resume screening models, sourcing recommendation engines, attrition prediction tools.
Same label. Different technology. Different risks.
Where you'll see them in recruiting
Generative AI use cases
- Outreach messages
- Job descriptions
- Interview guides
- Screening summaries
- Candidate Q&A responses
Statistical AI use cases
- Resume ranking
- Candidate matching
- Sourcing recommendations
- Attrition prediction
- Skill extraction
If the tool drafts text, it's probably generative. If it scores or ranks, it's probably statistical.
What each type is good at and where it fails
Generative AI
Strength: Flexible. Can draft anything with the right prompt. No training data needed. Works across roles and use cases.
Weakness: Unpredictable outputs. Needs strong review workflows. Can't make decisions—only draft content for humans to review.
Statistical AI
Strength: Consistent, measurable results when trained well. Good for repetitive classification tasks at scale.
Weakness: Only as good as training data. Breaks when hiring patterns shift. Hard to audit "why it chose this candidate."
How evaluation changes based on the technology
If the tool uses generative AI, ask:
- How are prompts designed and who controls them?
- What's the human review point?
- How do you validate outputs for consistency and quality?
If the tool uses statistical AI, ask:
- What training data was used and how recent is it?
- How do you test for bias?
- How does the model update when our hiring patterns change?
Different technologies require different diligence.
Sources
These sources explain how each type works and where research is heading:
Generative AI
Statistical AI in hiring
Bottom line
Most recruiting AI tools are one or the other. Knowing which type helps you ask better questions in demos.