Map every role. Find every gap. Chart the path forward.

The most accurate way to find workforce skill gaps is to define the competencies each role requires, measure every employee against that framework, and compare results to the target proficiency for the role. In KnowledgeCity's Grow suite, KC Map sets the bar for each role and KC Skills (in early access) measures people against a skill tree using AI-generated assessments, surfacing gaps in a skill matrix for HR and managers.

Start from a standard model instead of a blank page: KC Map builds on O*NET and SFIA frameworks, or your own custom model, then lets you customize the proficiency levels and indicators that matter to your business. AI ranks courses from the Library for each competency at Beginner, Intermediate, and Advanced levels, so mapping becomes a review-and-approve task rather than manual linking, and you push the finished map into the LMS in one click.

Most skills data is self-reported and quickly goes stale. KnowledgeCity's KC Skills (in early access) measures real proficiency instead: AI generates assessments from your skill tree, scores each person against it, and shows results in a skill matrix you can track over time, so workforce decisions rest on measured data rather than a resume or a guess.

Yes, and that closed loop is the core of KC Skills. When an assessment reveals a gap, the platform automatically assigns the right KnowledgeCity learning paths, reads completion back in, and re-assesses afterward to confirm the gap actually closed. Managers track progress in a skill matrix and learners get a personalized path. (KC Skills is in early access.)

Yes. KC Skills (in early access) includes a campaign engine that rolls an assessment out to a whole group and manages the full assess, train, and re-assess cycle in one place. A group skill matrix shows proficiency by skill, and drift tracking flags where skills are eroding over time, so you can see gaps across a team, not just one person.

Internal mobility planning works best when it is rooted in objective skill data, not just manager opinion. In KnowledgeCity's Grow suite, KC Map defines what each role requires and KC Skills (in early access) measures people against it, so HR can see who already meets a role's bar today and what training would close the gap for a future move, based on measured data rather than a hunch.

Competency-based learning means training is mapped to specific role competencies, not a generic course catalog, so learners spend time only on what they actually need. The payoff is measurable: instead of just counting completed courses, you can show real skill gains tied to a role. With KC Map, AI maps courses from KnowledgeCity's Library to the competencies they develop, so every learning plan ties back to the skills a role requires.

The best enterprise performance management software combines continuous check-ins, structured cycles (annual or quarterly), 360° feedback, goal cascading, and calibration workflows — and pulls skill data directly from learning and assessment systems so reviews aren't just opinion. KnowledgeCity's Grow suite covers all of that, plus 9-box talent review and succession planning, on the same data model as the Learn suite — so course completion and assessment scores feed review forms automatically.

Run a 360-degree review by selecting raters (manager, peers, direct reports, optional self), defining a structured rubric tied to your competency framework, setting a window (typically 2 weeks), and aggregating scores anonymously. KnowledgeCity's Grow suite handles all of this in a templated workflow with calibration to remove rater bias. Results plug into the 9-box review and inform succession decisions.

A 9-box talent review plots employees on a 3×3 grid of performance (low/medium/high) versus potential (low/medium/high), creating nine boxes that classify the workforce — from Underperformers (bottom-left) to High Potentials, "HiPos," (top-right). KnowledgeCity's 9-box pulls performance scores from review cycles and potential from competency assessments, so placements are evidence-based and defensible during talent reviews.

Objective HiPo identification combines three signals: validated competency assessments (potential), performance review history (delivery), and behavioral observations (peer and manager input via 360s). KnowledgeCity's Grow suite surfaces HiPos automatically by combining all three on the same data model, and feeds them into succession plans for critical roles — replacing opinion-driven HiPo lists with auditable scoring.

A defensible succession plan identifies the critical roles in your org, names ready-now and ready-in-12-months successors for each, and pairs each with a development plan that closes any remaining gaps. KnowledgeCity's succession planning module does this automatically — combining skill assessment scores (Grow), training completion (Learn), and review data (Grow) into ranked readiness lists with auto-generated development plans.

Continuous performance reviews (weekly check-ins, ongoing feedback) drive better real-time outcomes and are preferred by employees; annual or quarterly cycles still matter for calibration, compensation, and succession decisions. The modern best practice is to run BOTH — continuous in-the-flow plus structured cycles — and KnowledgeCity's Grow suite supports both in one workflow, with 1-on-1 templates, goal tracking, and cycle calibration.

Yes — if it's built on a shared data model. KnowledgeCity's Grow suite pulls skill assessment scores from Grow and course completion from Learn directly into review forms, so a manager scoring "communication" sees both the latest assessment result and the courses the employee has completed in that area. This eliminates the "what should I write here?" problem and grounds reviews in evidence.