AI-powered Learning Recommendation
The AI-Powered Learning Recommendation Engine automatically generates personalized, skill-gap-driven learning suggestions for employees based on skill gaps, role requirements, and organizational priorities.
This feature ensures that employees receive relevant, high-impact learning recommendations that accelerate skill development and maximize the ROI of upskilling programs.
Overview
The Learning Recommendation Engine connects:
Skills classification needing attention
Departments impacted by skills gap
Job profile and upskilling
Employee learning behaviour
Using AI-driven ranking and prioritization, the system delivers dynamically updated learning suggestions tailored to each employee.
Recommendations can be reviewed and managed by managers after which the employee portal is updated accordingly.
The image represents the Learning Recommendations (LR) Executive Dashboard, designed for senior L&D leaders such as the L&D VP. This view provides a macro-level understanding of organizational skill health and learning coverage.
This dashboard acts as the command center for skill-gap-based learning orchestration.
Organizational Skill Landscape Overview
At the top level, the dashboard summarizes the organization’s total skills inventory and highlights how many skills require upskilling intervention.
Key indicators include:
Total Skills in Organization – The complete mapped skill architecture.
Skills that Require Upskilling – Skills where proficiency levels fall below target thresholds.
Skills with Learning Recommendations (LR) – Skills already mapped to learning interventions.
Skills without LR – Gap areas that still require learning coverage.
This immediately tells leadership whether learning supply aligns with skill demand.
AI Insight Panel
The AI Insight section provides strategic direction by identifying which skill classifications (such as Emerging Technology or Functional skills) have the highest percentage gaps.
Instead of manually analyzing charts, the system surfaces:
High-gap classifications
Areas with the greatest potential capability uplift
Strategic focus zones for L&D investment
Skills Classification Analysis (Radar Chart)
The radar chart answers the question:
“Which skill classifications need the most attention?”
It visually compares gap percentages across categories such as:
Functional
Emerging Technology
Leadership
Technical
Soft Skills
Wider areas on the radar indicate larger skill deficiencies. This helps leadership prioritize whether intervention should focus on technical capability, leadership maturity, or emerging digital skills.
Departments Most Impacted by Skill Gaps (Treemap)
The treemap visualizes departmental impact by combining:
Depth of skill gap
Employee volume
Larger and darker blocks indicate higher risk concentration. For example, if AI/ML or Engineering shows a 70% skill gap affecting 100 employees, it signals immediate intervention is required at scale.
This visualization helps L&D align interventions with business-critical functions.
Moving forward, the below image shifts from macro-level organizational insights to role-level prioritization.
Skill Gap vs Employee Volume Analysis (Bubble Map)
This bubble chart maps:
X-axis: Skill Gap Percentage (Low to High)
Y-axis: Number of Employees
Each Bubble: A Job Profile
This creates a risk heat-zone model.
The background gradient represents increasing severity of skill gaps from left (low) to right (high).
Job profiles appearing in the far-right zone with:
High skill gap percentage
Large employee count
are considered high-risk priority roles.
This view is designed to answer:
“Which job profiles are in the high-risk zone and require urgent upskilling?”
For example:
Project Manager
82% Skill Gap
175 Employees
Department: Product Development
Interactive Drill-Down View
All visualizations on the Learning Recommendations dashboard — including the Radar Chart (skill classifications), Treemap (departments), and Bubble Map (job profiles) — are interactive. When a user clicks on any segment, block, or bubble, the platform transitions to a unified drill-down view filtered according to the selected classification, department, or job profile.
This drill-down view provides a structured breakdown of the selected area and enables detailed learning orchestration.
1. Contextual Header Summary
At the top of the screen, users can see:
Selected Skill Classification / Department / Job Profile
Total Employees impacted
Overall Skill Gap status (e.g., High)
Users can further refine the view using filters such as:
Skill Classification
Department
Job Profile
Clear All option
This allows users to narrow down analysis while retaining the context of the selected segment. This provides immediate clarity on the scope and severity of the selected segment.
2. Skills Overview Panel
The left-side section displays all skills associated with the selected segment. For each skill, the system highlights:
Skill Gap level (high, medium, low)
Number of employees
Learning Recommendation (LR) count
Publication status (e.g., Published, In Review)
This enables users to identify which specific skills are contributing to the overall gap.
3. Learning Recommendation Coverage Summary
A summary panel provides visibility into learning readiness by showing:
Total Employees in scope
Employees requiring upskilling
Employees with assigned Learning Recommendations
Employees without Learning Recommendations
This helps assess whether learning coverage is sufficient to address the identified gaps.
4. Employee-Level View
The detailed employee table enables execution and monitoring. It displays:
Employee name
Skill gap severity
Number of Learning Recommendations assigned
Role details (Grade, Location, Experience)
Publication status
This view allows administrators to track intervention depth and identify employees requiring immediate attention.
When you click on an employee’s name from the table, the platform opens the Skill Profile Details view. This is the final level of the Learning Recommendations workflow and provides an individual-focused perspective.
This screen brings together:
Employee role and status
Overall skill gap level
Learning Recommendation (LR) status
AI-generated skill summary
Assigned learning recommendations mapped to specific skill gaps
Users can quickly understand the employee’s readiness and review recommended learning interventions. This view ensures that strategic skill-gap analysis at the organizational level translates into targeted, personalized upskilling at the individual level.
Per skill two courses can be recommended, from which managers can view and publish the course that best suits in filling the gaps.
5. Publishing Controls
Administrators can:
Publish Learning Recommendations
Unpublish Learning Recommendations
Customize columns and apply filters
End-to-End Transition
This unified drill-down experience ensures that users can seamlessly move from high-level insights to detailed execution, regardless of which visualization they select. It connects strategic analysis with operational action in a single, consistent workflow.
AI-Powered Personalized Learning Recommendations help organizations:
Prioritize high-impact skill investments
Improve learning engagement through personalization
Reduce manual curriculum management
Accelerate capability uplift across critical roles
Increase measurable ROI on upskilling initiatives
