Cognitive Computing Model for Organizational Skill Gap Analysis
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Solution Overview
Problem
Large corporate environments face challenges in aligning human resources with emerging business strategies due to disconnected resource planning and recruiting, leading to difficulties in assessing skill gaps and determining the cost of entering new markets, resulting in inefficient resource redeployment and potential business closure.
Innovation Solution
A computer-implemented method using cognitive computing to train a machine learning model that defines business capabilities, skill classes, and talent profiles, determining skill gaps and assessing the cost of executing new business strategies by aligning organizational skills with business needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional recruiting and human resources platforms are used to hire for jobs, then hiring processes can be completed, but holistic business view on candidate fit across multiple jobs and skill alignment with emerging strategies is lost
Solution Approach 1:
The machine learning model serves multiple functions: it evaluates current skill gaps, projects future skill requirements based on emerging strategies, assesses candidate fit across multiple potential roles, and determines redeployment costs. This multi-functional approach replaces multiple separate HR processes with a single holistic system that maintains comprehensive business view.
Solution Approach 2:
The system performs preliminary analysis of skill gaps and future requirements before actual hiring or redeployment decisions are made. By projecting skill needs based on emerging strategies in advance, the system enables proactive planning rather than reactive hiring, preserving information about how candidates fit across multiple future scenarios.
2Productivity
If companies quickly adapt to market changes by opening new revenue streams, then competitiveness is improved, but ability to accurately assess skill gaps and resource requirements deteriorates
Solution Approach 1:
The machine learning model performs preliminary projection of skill requirements based on emerging strategies before companies actually implement new revenue streams. This advance planning enables quick adaptation while maintaining assessment accuracy, as the system has already identified skill gaps and resource needs in advance.
Solution Approach 2:
The system continuously monitors and updates skill gap assessments as emerging strategies evolve. By providing ongoing feedback on skill requirements and comparing them against current capabilities, the system maintains measurement precision even as companies rapidly adapt to market changes.
3Speed
If resource redeployment is performed without holistic analysis, then quick reallocation is achieved, but cost effectiveness and strategic alignment deteriorate
Solution Approach 1:
The system performs preliminary analysis of redeployment costs and strategic alignment before actual resource reallocation. By calculating the costs and benefits of moving employees between roles in advance, it enables quick reallocation decisions that are both cost-effective and strategically aligned, rather than making hasty redeployment choices.
4Ease of manufacture
If traditional HR systems are used without cognitive computing, then simple hiring tasks are completed, but ability to project future skill needs and determine cost of entering new markets deteriorates
Solution Approach 1:
The machine learning model acts as an intermediary between current HR systems and future strategic planning needs. It takes existing hiring data and employee skill information, processes it through cognitive computing algorithms, and produces projections about future skill requirements and costs of entering new markets, thereby preserving information that would otherwise be lost.
Data Source
AI summary
Generating a model for evaluating organizational skills to determine cost of entering a new market includes training a machine learning model to define business capabilities, processes and required skills of an organization based on a current business strategy, training the machine learning model to define a plurality of skill classes of the required skills of the organization using the cognitive computing processor device, training the machine learning model to define skill profiles of the available talent of the organization based on the plurality of skill classes, determining skill gaps of the available talent of the organization by analyzing the required skills of the organization and the skill profiles, assessing skills required for a new business strategy for the organization, and determining a cost of the organization executing the new business strategy based on the skill profiles, the at least one skill gap and the new business strategy skills.