Use case — Recruiting & HR
Improve hiring and people workflows without removing human judgment.
Recruiting is high-volume and people-affecting. AI can make evidence consistent and reviewable; it should not decide who advances. This workflow is built around that line.
Controls in this workflow: bias review of criteria, evidence citation on every extracted point, recruiter and hiring-manager decision rights, restricted access to sensitive candidate data, retention under the employer's existing policy, and human review of candidate communication. Employment-law duties, candidate notice, and fairness obligations vary by jurisdiction and remain with the employer.
The outcome
Hiring teams work from consistent, evidence-linked information and respond to candidates faster, while every decision about a person stays with a named human.
The friction today
What teams describe
- Job descriptions do not match what the hiring manager actually wants
- Screening notes differ between recruiters and are hard to compare
- Evidence behind a decision is difficult to reconstruct months later
- High-volume roles create backlogs and slow candidate responses
Current state, and the workflow afterwards
Current-state workflow
- IntakeThe role is described in a short conversation, and the requirement drifts between the manager, the recruiter, and the posting.
- ScreenApplications are read in different orders by different people, with notes kept in different formats.
- InterviewGuides are improvised, so evidence is not comparable across candidates.
- AdministerScheduling, status updates, and policy questions consume recruiter time that should go to candidates.
Future-state workflow
- IntakeA structured requirement is captured with the hiring manager, separating must-haves from nice-to-haves.
- ExtractApplications are parsed into a consistent structure, with the source passage attached to every extracted point.
- SummariseA neutral, evidence-linked summary is produced against the agreed criteria only.
- DecideThe recruiter advances or rejects, and records the reason.
- ReviewDecision patterns are sampled with the hiring manager and the criteria are refined.
Example workflows
- Role-intake extraction and structured clarification
- The intake conversation becomes a structured requirement, with must-haves separated from nice-to-haves and open questions raised back to the hiring manager.
- Candidate evidence summary against approved requirements
- A neutral summary against agreed criteria only, where every point links to the passage it came from.
- Interview guide draft
- Questions drafted from the agreed criteria, reviewed by the hiring manager before use.
- Scheduling and status routing
- Routine scheduling and status movements handled inside the ATS, with candidate communication reviewed.
- Employee policy knowledge assistant
- Access-aware answers from approved HR policy, with the source clause shown and a clear 'not found' response.
- Training-content and onboarding support
- Onboarding materials and checklists drafted from approved internal content for HR review.
What AI assists, and what people decide
What AI assists
- Structuring role requirements and interview guides
- Extracting evidence from applications with the source passage attached
- Routing scheduling and status administration
- Retrieving approved HR policy with citations
What people decide
- Who advances, who is rejected, and why — never automated
- Whether the criteria are fair and relevant to the role
- What is said to a candidate
- Whether an extracted piece of evidence is accurate
Approved data and systems required
Systems involved
- Applicant tracking system
- Document storage
- Interview scheduling
- Identity and access controls
Approved data inputs
- Applications and CVs submitted for the specific role
- Agreed role criteria, not historical hiring outcomes
- Interview notes where candidates have been informed
Controls and failure modes
Human-control pattern
- No automated rejection under any circumstance
- Every summary point links to the passage it came from
- Criteria are agreed in advance and reviewed for fairness
- A defined share of decisions is sampled by a second reviewer
Failure modes
- Bias encoded in criteria or historical data — mitigated with explicit criteria and sampling
- Extraction errors on unusual document formats, surfaced by the evidence link
- Candidate notice and employment-law duties vary by jurisdiction and stay with the employer
What this does not promise
- No scoring, ranking, or filtering that removes a candidate without a human decision
- No prediction of on-the-job performance
- No claim of bias-free outcomes; fairness is a controlled, monitored practice
Implementation path
- 1. Agree criteriaFix the requirement and the fairness review before any AI touches an application.
- 2. Ground in the ATSKeep candidate data inside the existing hiring boundary and access model.
- 3. Pilot on one role familyRun alongside current screening and compare notes for consistency.
- 4. SampleA second reviewer samples decisions and extraction accuracy on an agreed cadence.
- 5. ExtendAdd policy assistance and onboarding support once screening support is stable.
Measures
These are evaluation targets you agree and track together, not promised results. Baselines are measured before launch so any change is attributable, and figures stay specific to your data, volumes, and process.
What you can measure
- Time to first recruiter response
- Consistency of screening notes across recruiters
- Reviewer agreement on sampled decisions
- Extraction accuracy on sampled applications
- Time-to-shortlist on high-volume roles
What each stakeholder needs to know
- Executive sponsor
What is the risk position?
The people-affecting decision stays human and recorded, which is the position most regulators and candidates expect. The gain is throughput and defensibility, not automated selection.
- Functional leader
What changes for recruiters?
Less re-reading, more structured comparison, and a consistent record of why each decision was made.
- Operations leader
How do exceptions work?
Unparseable applications, conflicting evidence, and flagged criteria route to a named reviewer instead of being silently dropped.
- Technology & security reviewer
What does this touch, and how is it controlled?
Candidate data stays inside the ATS boundary where possible, retention follows the existing hiring policy, access is role-scoped, and every extraction is logged with its source document.
- End user
What am I responsible for?
The decision and the reason. The summary is evidence, not a verdict, and you can mark extractions as wrong.
The right AI solution is not selected by trend. It is designed around the workflow, approved data, systems, people, risk, and measurable outcome.
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