Skills & Training AI | HR Automation

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Skills Tracking & Training Recommendation Engine

Understand employee skills, identify gaps, and recommend internal training paths.

HR automation case study featuring ai implementation and reporting.

Significantly Improved

Team Allocation Accuracy

Reduced

External Hiring

Achieved

Career Growth Clarity

Overview

A technology company needed to understand employee skills, identify gaps, and recommend internal training paths. The system improved team allocation accuracy and reduced external hiring by identifying internal candidates.

Business Context

The technology company had grown to 600 engineers across multiple product lines, but leadership had no systematic understanding of their collective capabilities. Project staffing decisions relied on manager intuition and informal networks rather than actual skill data. The L&D team offered training programs with poor attendance because courses were not targeted to individual development needs. Most frustratingly, the company frequently hired externally for specialized roles only to discover months later that existing employees had the required skills but had never been considered. The CFO estimated that unnecessary external hiring and suboptimal project staffing was costing the company over $500,000 annually.

How We Built It

We built a comprehensive skills intelligence platform with a Neo4j graph database at its core, modeling the complex relationships between skills, roles, projects, training resources, and employees. Python ingestion services process data from multiple sources including LinkedIn profiles, internal project histories, performance reviews, and completed training certifications to build initial skill profiles. A structured competency framework defines skill categories, proficiency levels from beginner to expert, and the relationships between related skills — for example, proficiency in React suggests likely familiarity with JavaScript and front-end development patterns. Employees complete self-assessment surveys through an intuitive Next.js interface, rating their proficiency and interest level in skills relevant to their role and career aspirations. The AI recommendation engine powered by OpenAI analyzes each employee's current skill profile, their stated career goals, and the company's strategic skill needs to generate personalized training paths. Manager dashboards visualize team skill distributions as heat maps, highlighting gaps that could impact project delivery and identifying employees ready for stretch assignments. The system integrates with the company's learning management system to surface relevant courses, and tracks skill development over time as employees complete training and apply new capabilities on projects. Skill search functionality allows project managers to find employees with specific competencies across the entire organization, breaking down the silos that previously limited staffing options.

Challenges

No clear picture of company-wide skill distribution

Training done ad hoc

Managers struggled to assign projects

Hiring decisions lacked data

What We Delivered

Skills graph backend with Python ingestion and Neo4j

Competency model with skills-gap detection and scoring

Self-assessment surveys for employees

Training recommendations based on role, skills, and career path

Manager view of team skills and gaps

AI summarization of strengths and project risk flagging

Tech Stack

Python, Neo4j, Node.js, PostgreSQL, Next.js, OpenAI

Results

Significantly Improved

Team Allocation Accuracy

Reduced

External Hiring

Achieved

Career Growth Clarity

Strategic Impact

Team allocation accuracy improved dramatically as managers now make staffing decisions based on verified skill data rather than assumptions or limited personal networks. The company reduced external hiring by 25% in the first year by identifying internal candidates with the required skills — candidates who would have been overlooked in the previous informal system. Employee engagement scores related to career development increased by 45% as staff appreciated having clear visibility into skill gaps and personalized recommendations for closing them. Training program completion rates doubled because employees now receive recommendations relevant to their specific development needs and career aspirations rather than generic course catalogs. The skills graph revealed unexpected talent — several employees had advanced certifications in emerging technologies that their managers were unaware of, leading to new project opportunities. Project risk decreased as the system flags teams with skill concentration issues, where a single departure could leave critical capabilities uncovered. The L&D team uses aggregate skill gap data to prioritize investment in training programs that address company-wide needs. Succession planning improved with visibility into which employees are developing the skills needed for leadership roles. The platform has become central to the company's talent strategy, informing decisions about hiring, training investment, and organizational structure. Internal mobility increased by 35% as employees and managers could identify opportunities that matched emerging capabilities.