AI‑powered SaaS competency mapping uses skills graphs and ontologies to infer employee skills, map them to roles with proficiency levels, and recommend development and mobility paths in real time, cutting manual framework upkeep and improving workforce decisions end‑to‑end. Leading platforms unify skills data across HR systems and content, provide explainable mappings, and keep models fresh with market signals and internal activity streams.
What it is
- Competency mapping aligns people to job profiles through a dynamic skills model that supports discovery of current capabilities, target proficiencies, and gaps for learning and staffing actions.
- Skills graphs/ontologies consolidate synonyms and relationships between skills to keep mappings up‑to‑date and machine‑actionable across roles, content, and internal mobility programs.
- SAP SuccessFactors Talent Intelligence Hub
- Central hub with Skills Ontology, Attributes Library, and Growth Portfolio to view and update skills/competencies with proficiency levels and AI‑driven recommendations.
- Cornerstone Skills Graph
- AI engine detecting 50k+ skills from profiles, roles, and content to power competency‑based recommendations, development, and mobility across the suite.
- Workday Skills Cloud
- ML‑powered universal skills ontology embedded in HCM to cleanse, relate, and continuously learn skills relationships for job/learning alignment.
- Eightfold Skills Intelligence
- Talent intelligence that infers skills and potential from billions of career paths to power skills inventories, gap analysis, and development/mobility at scale.
- Gloat Workforce Graph
- AI‑driven internal talent marketplace and workforce graph connecting skills, roles, and projects for dynamic allocation and agility.
- TalentGuard (WorkforceGPT + CMS)
- Competency management with AI‑generated taxonomies, proficiency statements, job architecture, and career paths with assessment and gap analytics.
How it works
- Sense
- Aggregate data from profiles, roles, job posts, learning, and projects; infer skills and competencies with AI and surface them in a centralized portfolio or graph.
- Decide
- Map people to job profiles with target proficiencies, run gap analysis, and recommend learning, projects, or moves based on ontology/graph relationships.
- Act
- Generate/upkeep role profiles, publish personalized learning plans, and match people to gigs and roles via talent marketplaces and internal mobility flows.
- Learn
- Continuously update the ontology/graph from market signals and internal outcomes to keep mappings current and recommendations effective.
High‑value use cases
- Skills‑based job architecture
- Standardize role profiles with required competencies and proficiencies, then auto‑maintain using AI to reduce manual taxonomy work.
- Targeted upskilling and academies
- Use gap analysis to assign learning tied to specific competencies and verify progress with proficiency updates.
- Internal mobility and project marketplaces
- Match employees to roles and projects using adjacent skills and aspirations to improve retention and agility.
- Workforce planning and succession
- Visualize capacity by critical competencies and build successor pipelines with evidence‑based skills profiles.
30–60 day rollout
- Weeks 1–2: Foundation
- Enable skills/competency portfolios or skills cloud features; import roles and map core competencies with baseline proficiency targets.
- Weeks 3–4: Inference and gaps
- Turn on AI skills inference and employee self‑attest/manager validation; run initial gap analyses by role family with recommended learning.
- Weeks 5–8: Mobility and governance
- Launch internal gigs/roles matching and set review cadences; institute role‑based permissions and audit workflows for changes to profiles.
KPIs to track
- Skill coverage and freshness
- Share of employees with AI‑inferred and validated competency profiles updated in the last quarter.
- Gap closure velocity
- Average time to move from current to target proficiency on priority competencies via recommended learning/projects.
- Internal fill and mobility
- Rate of roles filled internally and gig placements attributable to graph‑based matching.
- Architecture efficiency
- Time saved creating/updating job profiles and frameworks via AI assistance versus manual methods.
Governance and trust
- Explainable mappings
- Show why a skill/competency was inferred or recommended, with sources (profile, role, learning, market) and editable proficiency settings.
- Role‑based permissions
- Use enhanced RBAC to control who edits attributes, proficiencies, and job architecture across modules.
- Ontology care and feeding
- Rely on vendor‑managed ontologies/graphs that consolidate synonyms and evolve with market skills to avoid drift and duplication.
Buyer checklist
- Mature skills graph/ontology with inference, proficiency levels, and open integrations to HR/Learning systems.
- Talent intelligence and marketplaces that operationalize competency mapping into development and mobility actions.
- Job architecture automation (profiles, proficiencies, paths) with audit and governance controls.
- Transparent admin and employee experiences for editing, validating, and acting on competency data.
Bottom line
- Competency programs scale when a living skills graph or skills ontology drives inference, mapping, and actions—connecting people, roles, and learning into one adaptive system that continuously closes gaps and powers mobility.
Related
Which platforms here provide automated skills ontology generation
How does Eightfold’s competency mapping differ from Gloat’s graph
What data sources do these tools ingest for accurate skill inference
How can I integrate a competency map into my LMS or HRIS
What ROI metrics show impact of AI competency mapping on retention