AI‑powered SaaS analyzes workforce skills in real time to identify individual and team‑level skill gaps, map adjacent skills, and recommend targeted learning and mobility actions that align with business priorities and emerging market demand. Modern platforms combine skills graphs, proficiency inference from employee data, and talent marketplaces to close gaps faster while improving retention and workforce agility.
What it is
- Skills intelligence engines build a living, ML‑driven profile of each employee by extracting and normalizing skills from resumes, roles, learning, and performance data to power organization‑wide gap analysis.
- These systems use a standardized skills graph/ontology to connect people, roles, and content, enabling consistent gap detection, role fit scoring, and personalized development plans.
Why it matters
- Global data shows rapid skill change and persistent gaps; employers cite skills deficits as the top barrier to transformation through 2030, underscoring the need for real‑time gap analytics and targeted reskilling.
- Skills‑first approaches improve mobility and hiring fairness by matching people to opportunities via skill evidence rather than proxies like pedigree or past employer.
Core capabilities
- Unified skills graph and ontology
- Curate tens of thousands of skills with multilingual aliases and relationships so gaps and adjacencies are detected consistently across roles and geographies.
- Proficiency inference and gap scoring
- Infer current level from signals (role tenure, projects, learning, endorsements) and compare to role or project requirements to quantify gaps and priorities.
- Personalized learning and pathways
- Recommend courses and practice aligned to gaps and role goals, updating plans as learners progress and new skills emerge.
- Internal mobility and talent marketplace
- Match employees to gigs, mentors, and roles that close gaps on the job while addressing urgent project needs.
- Market and future‑skills insight
- Blend internal data with external labor trends to spotlight rising skills (e.g., AI literacy) and inform workforce planning.
- Workday Skills Cloud
- ML‑powered skills framework embedded across HCM to normalize skills, map adjacencies, and drive skills‑based learning, career hubs, and talent decisions.
- Cornerstone Skills Graph
- Detects 50k+ skills from profiles, roles, and content; fuels personalized recommendations, mobility, and dynamic taxonomies across the Cornerstone suite.
- LinkedIn Skills Graph
- Maps 39k skills across 875M people and 59M companies to power skills‑first matching and insights on how job skill sets are shifting.
- Gloat Talent Marketplace
- AI‑driven internal marketplace using a Workforce Graph to match people to projects, roles, and mentors, accelerating gap closure via real work.
- SkyHive (skills intelligence)
- Explainable skills intelligence that expands discovered skills beyond self‑reports and identifies transferable skills and learning to bridge gaps; now part of Cornerstone.
How it works
- Sense
- Extract skills from HRIS profiles, resumes, job descriptions, learning history, and performance data; enrich with market signals on rising skills.
- Decide
- Compare current profiles to role requirements; use skill adjacencies to propose the fastest path to close gaps via learning, gigs, or mentors.
- Act
- Launch personalized learning plans, recommend internal moves or projects, and create skill goals visible in career hubs and manager views.
- Learn
- Update proficiency as employees complete learning or projects; refresh gap analytics as the skills graph and market trends evolve.
30–60 day rollout
- Weeks 1–2
- Enable the skills graph in the core HCM/LXP, import existing role frameworks, and run an initial gap analysis for priority job families.
- Weeks 3–4
- Publish skill profiles and role targets; turn on personalized learning recommendations and pilot a talent marketplace for one function.
- Weeks 5–8
- Add market‑rising skills (e.g., AI literacy) into role maps; launch mentor/gig programs focused on top gaps and track movement toward targets.
KPIs to track
- Gap closure velocity
- Percent of target skills moving from “gap” to “met” per quarter and time‑to‑proficiency for critical roles.
- Internal mobility and fill rate
- Share of openings filled internally via marketplace matches and gig participation rates.
- Learning effectiveness
- Completion and assessment uplift on skills tied to role requirements and career outcomes.
- Future‑skills readiness
- Adoption of rising skills (e.g., AI literacy) across relevant roles versus market benchmarks.
Governance and trust
- Common language and transparency
- Standardize on a shared ontology and show “why recommended” explanations for learning and mobility suggestions to build confidence.
- Bias and fairness
- Prefer explainable systems that prioritize skills evidence and monitor for demographic skews in recommendations and matches.
- Data privacy
- Limit use of sensitive data and provide employee controls over visibility of inferred skills and aspirations.
Buyer checklist
- Robust skills graph/ontology with multilingual coverage and continuous updates.
- Proficiency inference and explainable gap scoring integrated with learning and career paths.
- Talent marketplace capabilities to close gaps through projects, roles, and mentorships.
- External labor insights to prioritize rising skills and inform workforce planning.
Bottom line
- The strongest outcomes come when a living skills graph, explainable gap analysis, and an internal talent marketplace work together—turning skills data into targeted learning and mobility that future‑proofs the workforce.
Related
How does Workday Skills Cloud detect employee skills from profiles and activity
How does Cornerstone Skills Graph differ in skill taxonomy from Workday
What accuracy can I expect from AI skill gap assessments in enterprise SaaS
How can I integrate Skills Cloud or Skills Graph outputs into LMS workflows
What privacy or compliance risks should I consider when auto-detecting skills