High-volume verification works best as a queue of separate candidate reviews, not one blended AI request. Each CV should retain its own evidence trail, status, and follow-up topics.

Scope and approach

The adoption figures below come from two disclosed but different samples: SHRM's 2025 HR research (N=2,040) and LinkedIn's survey of 1,271 management-level recruiting professionals in 23 countries. They indicate adoption within those samples, not a census of all employers.

Research snapshot

Relevant evidence and scale

43%

of organizations reported using AI in HR tasks in SHRM's 2025 research

Source
37%

of surveyed talent-acquisition professionals were experimenting with or integrating generative AI

Source
20%

average workload reduction reported by generative-AI users in LinkedIn's survey

Source
Figures describe the cited study or database and should not be generalized beyond its stated scope.

Use one report per candidate

Processing CVs separately prevents evidence from one candidate being attributed to another. It also supports reliable retries, predictable credit usage, individual deletion, and a clear audit trail.

Sources: National Institute of Standards and Technology

Check capacity before starting

Before accepting a batch, confirm that the account has enough report credits for every file. Reserve one credit per accepted CV and show how many reports are queued, processing, completed, or unavailable.

Standardize the output

  • Candidate name and upload date
  • Processing status
  • Claims reviewed
  • Supported and partially supported claims
  • Unverifiable claims
  • Discrepancies found
  • Direct link to the private report

Sources: National Institute of Standards and Technology

Route exceptions to people

Automation should organize public evidence, not make a hiring decision. Recruiters should review discrepancies and material unverifiable claims, ask candidates for clarification, and confirm important findings through authoritative channels.

Sources: National Institute of Standards and Technology; U.S. Equal Employment Opportunity Commission and Federal Trade Commission

Protect candidate information

Limit access to signed-in users, store uploaded documents privately, provide deletion controls, and avoid collecting sensitive data that is unnecessary for professional claim verification.

Sources: U.S. Equal Employment Opportunity Commission and Federal Trade Commission

How CredVerity applies this evidence

From research method to repeatable workflow

CredVerity checks batch capacity before acceptance, reserves one credit per CV, processes every CV as a separate task, and provides candidate-level status and evidence summaries in a private workspace.

Review the full CredVerity methodology →
Important

Public-source verification can be incomplete and should not be the sole basis for a consequential decision. Confirm material findings directly with the person or an authoritative source.

Sources and scope notes

  1. 2025 Talent Trends: AI in HRSociety for Human Resource Management

    SHRM reports results from N=2,040 and identifies recruiting as the leading HR use area for AI.

  2. Future of Recruiting 2025LinkedIn

    Surveyed 1,271 recruiting professionals across 23 countries in September 2024; results describe the surveyed LinkedIn-member sample, not all employers.

  3. AI Risk Management Framework CoreNational Institute of Standards and Technology

    Calls for documented human-AI roles, oversight processes, and continuous risk management.

  4. Background Checks: What Employers Need to KnowU.S. Equal Employment Opportunity Commission and Federal Trade Commission

    Summarizes federal employment-discrimination and consumer-reporting considerations; state and local rules may add requirements.