AI Skill Inference Models for Dynamic Workforce Gap Analysis
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Solution Overview
Problem
Existing HCM/HR systems fail to regularly update employee skill information, leading to inaccurate assessments of workforce skills and potential skill gaps, making it difficult for organizations to identify suitable candidates and address skill development needs.
Innovation Solution
Utilizing AI/ML data models to process structured, semi-structured, and unstructured data, generating a multi-dimensional ontology and taxonomy, and identifying inferred characteristics through a dynamic skills pipeline to enhance data visualizations for analytics dashboards and scorecards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If employee skill information is stored in traditional HCM/HR systems, then data storage and basic management are achieved, but the information becomes outdated and inaccurate because it is not updated on a sufficiently-regular basis
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and analyzing multiple data sources (resume parsing, project management tools, code repositories, professional networks) to detect skill changes before they are formally recorded in the HCM system. This proactive approach ensures skill information is captured at the moment it emerges, eliminating the lag between actual skill acquisition and system recording.
Solution Approach 2:
The system implements continuous automated data collection and analysis across multiple sources, maintaining an ongoing process of skill detection and validation. This continuous action replaces periodic manual updates, ensuring skill information remains current through persistent monitoring of employee activities, projects, and professional developments.
2Loss of information
If traditional HCM/HR systems are used to maintain employee skill databases, then data storage is achieved, but the systems cannot identify hidden or inferred skills that employees possess
Solution Approach 1:
The system introduces AI/ML models as intermediary components that bridge raw data from multiple sources and the HCM system. These models parse resumes, analyze code repositories, evaluate project contributions, and infer skills from professional network activities. The intermediaries transform unstructured and semi-structured data into structured skill information, enabling comprehensive skill detection without requiring direct complex processing in the core HCM system.
Solution Approach 2:
The system replaces manual skill assessment mechanisms with automated AI/ML-based analysis. Instead of relying on employees to self-report skills or managers to manually evaluate them, the system uses machine learning models to automatically detect, infer, and validate skills from various data sources. This substitution enables the system to identify hidden skills that would be difficult or impossible to detect through traditional mechanical assessment methods.
3Productivity
If manual methods are used to assess workforce skills, then simplicity of operation is maintained, but the ability to identify skill gaps and match candidates to opportunities is severely limited
Solution Approach 1:
The system implements feedback loops where AI/ML models continuously analyze employee activities, project outcomes, and skill demonstrations, then feed this information back to update skill profiles. The system also provides feedback to employees about inferred skills for validation, creating a closed-loop system that improves accuracy over time. This feedback mechanism enables dynamic skill assessment that adapts to new information, significantly improving both efficiency and precision compared to static manual methods.
Data Source
AI summary
Embodiments described herein are generally directed to computer-based data analytics and the processing of enterprise data, including the generation and use of data models for determining inferred characteristics associated with candidates. In accordance with an embodiment, the system utilizes data-processing pipelines and machine learning models to process structured, semi-structured, and/or unstructured sets of data, received from various sources; generate a multi-dimensional ontology and a taxonomy associated with the characteristics of open positions or potential candidates; identify, based on the data models, one or more additional or inferred characteristics associated with the candidates; and present the output by way of an analytics dashboard, scorecard, or other data visualization.