AI tools for HR & Talent Management in 2025

AI in Talent Management (2025)

From competencies and skills to performance, learning, mobility, and succession — what to automate, what to govern, what to avoid.

What Changed, What Works, How to Govern

A practical guide on AI in Talent Management in 2025 for HR, OD, Operational and Talent leaders.
AI can draft and surface options—but people remain accountable for high impact decisions on hiring, promotion, mobility and pay.

In practice, the first place many organizations feel AI is in competency management — generating models, inferring skills from CV/HRIS, mapping gaps, and recommending learning. These are high-impact uses that need clear guardrails.

What changed since 2024

HR & Talent Management Platform Tools

AI in Talent Management – 2025

There have been many advances in AI tools for HR since our 2024 report

  1. Copilots/AI assistants are deployed in the flow of work (Microsoft) to help employees/managers with everyday tools (Office/Teams/Outlook). Microsoft has flagged People Skills in Copilot as an emerging essential capability for skills management at team level.
  2.  “Agentic” HR suites (AI agents that act across workflows). This is a change from “copilots that draft text” to agents that can take actions inside HR and Finance workflows. Workday is an example.
AI in Talent Management 2025

3.  Competency engines moved from text-drafting to agentic workflows that suggest skills, generate indicators, and pre-fill gap analyses — requiring verification and version controls.

4. Centralized skills hubs  aggregate skills data from multiple sources

5. Recruiter tools: AI-assisted search & outreach (AI assisted messaging) at scale.

6.  Talent management platforms are packaging broader AI.  The use of  AI and skills intelligence to personalize individual development, compliance and performance requirements.

7. “Multi-model” AI options are expanding in HR applications – a choice of data models for data residency and costs.

8.  Vendor ecosystems are being opened up. Workday’s Agent Gateway (coming late-2025) aims to connect third-party agents using shared protocols so HR automations can traverse systems

Changes in Regulatory Environment

AI Legal & Regulatory compliance
  1. Regulation went from theory to timelines.
    The EU AI Act is now in force with a phased roll-out. It includes bans on “unacceptable” practices (including emotion inference at work and untargeted scraping of facial images) applying since Feb 2025. Other obligations relating to use of AI for employment decisions phase in through 2026.
  2. In the US the first broad state AI law covers employment decisions. Colorado’s AI Act (SB24-205) imposes duties on developers and users  of high-risk AI (including hiring/promotion tools) starting Feb 1, 2026. These obligations include risk management, notices, impact assessments, etc.
  3. In New York City the Local Law 144 requires bias-audits and notices when using automated employment decision tools. California is developing a similar regulation.
  4. New Jersey issued 2025 guidance reminding employers that state anti-discrimination law applies to AI-aided hiring.
  5. The US  EEOC emphasizes that employers remain responsible for outcomes when using vendor applications with AI across recruiting, selection, monitoring, pay and promotion.
  6. ISO/IEC 42001 (AI Management System) moved from “new” to adopted/certifiable in 2024–25, giving HR teams a recognizable governance frame (like ISO 27001 but for AI).
  7. DeepFakes & identity-fraud risks are now HR problems.  Increasing use of  AI voice/video in job applications means more verification requirements when hiring.
  8. The NIST AI Risk Management Framework gained traction —making it easier to operationalize oversight, logging, and human-in-the-loop for AI use in HR processes.

Where AI actually helps

▏Competency modelling & mapping
AI can draft role and competency models, then suggest indicators for job families.
Example: automatically creating a draft competency profile for “Maintenance Technician,” which HR reviews and edits before use.

▏Skills inference & profiles
AI can propose skills based on CVs, HRIS, or LMS records to speed up profile building.
Example: detecting “AutoCAD” proficiency from training logs — flagged as “inferred” until a manager verifies it.

▏Gap analysis & learning alignment
AI can compare current employee skills against role standards, then link gaps to learning resources.
Example: spotting that a nurse has an outdated IV certification and suggesting the correct refresher course.

▏Workforce intelligence
AI consolidates messy data across HR, LMS, and performance systems.
Example: cleaning duplicates so “Project Management” and “PM Certification” appear as one verified skill.

▏Automating routine tasks
AI can take over repetitive admin like scheduling, reminders, and compliance checks.
Example: auto-sending reminders for competency reassessments every 12 months.

▏Sourcing & screening
AI can de-duplicate CVs, extract skills, and rank candidates against set criteria.
Example: highlighting which applicants already hold a required safety license.

▏Assessments & interviews
AI can generate interview questions, assist with structured scoring, and summarize responses.
Example: auto-drafting situational judgment questions aligned to the competency model.

▏Performance & feedback
AI can summarize multi-source feedback into clear themes.
Example: clustering 360 feedback comments into “communication strengths” vs. “needs development in delegation.”

▏Learning & development
AI recommends learning activities that map directly to verified skill gaps.
Example: suggesting an advanced Excel module only for employees who’ve already mastered the basics.

▏Internal mobility & succession
AI surfaces hidden skills and matches people to new roles or projects.
Example: spotting a technician with welding certifications who could step into a higher-skilled maintenance role.

Common Problems

Most of these were highlighted in our previous articles and remain today;

▏Bias and fairness drift
Training data often reflects historical bias, leading to unfair outcomes across protected groups.
Example: a hiring model that prioritizes candidates from certain universities, even though it isn’t job-related.

▏Confident but wrong outputs
AI can generate polished but inaccurate summaries or recommendations (“hallucinations”).
Example: suggesting an employee is “ready for promotion” based on incomplete data, without manager review.

▏Data protection & consent gaps
AI tools sometimes over-collect personal data or use unclear legal bases for processing.
Example: scraping candidates’ social media without consent to infer skills.

▏Inconsistency in results
The same query may return different answers, and model updates can change results without notice.
Example: an employee marked “competent” one month and “not competent” the next, due only to a model change.

▏Lack of explainability

Employees and candidates often don’t know when AI was used or how to contest a result.

Example: a candidate rejected by an algorithm without being told what factors were considered.

▏Opaque vendor claims
Vendors may present black-box systems with little transparency on data sources, fairness testing, or audits.
Example: a platform claims to be “bias-free” but refuses to share validation evidence.

Vendor Claims

AI vendors often market their tools as fair, accurate, and compliant. In reality, most claims hide limitations or shift responsibility back to the employer. Treat every promise as a starting point — and always ask for evidence, audits, and explainability.

Bias in workplace assessments1) “Our AI is bias-free / fair by design.” There’s no such thing. Fairness depends on data, context, and ongoing testing. Example: a tool may still favor certain age groups or schools unless audits are run regularly.

detecting emotion from facial or auditory cues2) “We can detect emotions or deception in interviews..”

Regulators call this technology unproven. The EU AI Act even bans emotion inference at work.
Example: rejecting a candidate because AI misread their nervous expression as “dishonesty.”

Compliant3) “We’re compliant out of the box.”

Compliance is shared. Employers remain responsible for notices, bias audits, and record-keeping.
Example: a vendor says “fully compliant,” but you still must publish bias audit results under NYC law.

Automation4) “We can automate hiring decisions end-to-end.”

Employment AI is “high-risk” under the EU AI Act and requires human oversight.
Example: auto-rejecting applicants without a manager ever reviewing the decision.

Anonymity5) “We anonymize candidates, so there’s no bias or privacy issue.”

Removing names isn’t enough. Proxy data like education or location can still reproduce bias.
Example: a model favors certain zip codes that correlate with socio-economic status

accuracy6) “We’re 95% accurate at predicting performance/readiness.”

Accuracy” often means only on a test set — not real-world outcomes.
Example: a tool scores high on past data but doesn’t predict actual job success.

continuous learning7) “We continuously learn from your data to improve results.”
Continuous updates can make results unstable unless versioned and logged.
Example: a candidate ranked #1 one week drops to #3 the next, with no explanation.

Inference8) “We infer your skills objectively from CVs/HRIS ”

Skills are context-dependent. Inferences may be wrong without human validation.
Example: inferring “leadership” from a “team lead” title, even if the person had no direct reports.

sensitive data9) “We don’t use sensitive data.”
Models may still rely on demographic proxies.
Example: excluding names but still inferring gender or ethnicity from career gaps or schooling.

ROI10) “Proven ROI: +40% speed, +30% retention.”

Many ROI claims come from vendor case studies with no controls.
Example: reported retention gains that were actually due to new onboarding processes, not the AI tool.

Identifying risks by process

Process

Sourcing

Screening

Interviewing

Performance

Mobility

L&D

Succession

Typical AI uses

Skill extraction, CV de-dupe

Rank candidates vs criteria

Question generation; scoring

Scoring; language analysis

Goal/feedback summaries

Suggested learning plans

Mobility  Role matches;

Readiness suggestions

Main risks

Hidden bias via proxies

Disparate impact; lack of explainability

Over-automation; privacy issues

Lack of Validity; cultural bias

“Confident wrong” summaries

Un validated learning resources

Stale/incorrect skills

Opaque scoring; adverse impact

Competency-specific risks to watch

  • Unverified skills inflation: AI may mark skills as “verified” without real evidence.
    Example: a system infers project management from someone’s job title, even though they’ve never managed a project.
  • Context loss: the same skill label can mean very different things depending on the role and required level.
    Example: “Excel” for an administrator (basic formulas) vs. for a financial analyst (advanced modeling). For certifications like First Aid, “current” training may be essential — older or expired certificates aren’t valid.
  • Opaque scoring: readiness or fit scores can be based on hidden proxies rather than job-related evidence.
    Example: AI infers leadership ability from tenure or education, not from observed competencies.
  • Drift over time: model updates can change results without notice.
    Example: an employee who was “ready now” for promotion last quarter appears “not ready” this quarter — with no explanation, simply because the algorithm updated.

Five Smart questions to identify risk

question mark

  1. Bias: Show your latest bias test (groups, metrics, sample) and how we run our own.
  2. Oversight: Where do humans approve/override? What gets logged?
  3. Updates: What’s your model/version policy and rollback plan?
  4. Data: List training/runtime sources, retention, and location; confirm no emotion inference or untargeted facial scraping.
  5. Validity: Provide evidence linking scores to actual job outcomes, not just test-set accuracy.

Oversight & explainability

What to require from any AI-in-talent tool

  • Suggest-only for high-stakes steps (hire/promo/pay/mobility) with mandatory human approval.
  • Per-decision rationale shown to managers (top factors/evidence with links).
  • Exportable audit logs: inputs used, model/version, suggestion, final decision, approver, timestamp, reason.
  • Update controls: version IDs in UI, release notes, and a rollback path.
  • Human sign-off for high-stakes steps: AI should only suggest, not decide, for hiring, promotion, pay or mobility.
    Example: the system can draft a shortlist, but a manager must approve the final candidate.
  • Clear rationale for each suggestion: managers should see the top factors and evidence behind the recommendation.
    Example: “Matched on certified skill X and 3 completed projects,” not just a score of 87%.
  • Exportable audit logs: keep a record of inputs, model/version, recommendation, human approver, and final decision.
    This allows you to prove compliance if regulators audit.
  • Version control: show which model/version generated the suggestion, with a rollback option if outcomes drift.
  • Data boundaries: clearly exclude banned or high-risk inputs like biometrics, emotion detection, or indirect demographic proxies.
  • Competency verification workflow: separate “AI-inferred” from “assessor-verified” skills, with sign-off required for high-stakes use.
  • Role context display: show the standard or level expected for the role, alongside the recommended skill or competency.

 

  • Data boundaries: feature whitelist/blacklist (no emotion inference/biometrics; no proxy fields).

Mini Governance Checklist

  • Publish a plain-language AI use notice (what, where, human review path).
  • Define criteria first (job-related, business-necessary) before ranking/scoring.
  • Use risk tiers; keep humans in the loop for high-stakes steps.
  • Run a simple bias-audit memo; act on findings.
  • Minimise inputs; avoid obvious proxies.
  • Keep a model/update log so results don’t drift.
  • Maintain an AI Register (owner, data, oversight step, last audit, last update).
Want the detailed templates?
Download the AI Governance Guide

References