A US-based educational technology company needed to improve workforce readiness and development by connecting roles, skills, capability gaps, and career pathways. The company offered a broad learning ecosystem, including courses, books, labs, and role-based learning paths, but customers required greater clarity on capabilities and gaps.
Organizations used different definitions and taxonomies for similar skills, making it difficult to establish a consistent view of workforce capabilities. Manual effort was also required to map skills to roles and maintain these frameworks, which was time-consuming and difficult to validate at scale.
There were several challenges in workforce development, including limited understanding of capability gaps and priority development areas. Organizations lacked a clear way to understand the capabilities employees had developed and identify areas requiring further development.
Disconnected career progression was another challenge, with employees having limited visibility into the capabilities required to move from their current roles to aspirational positions. Weak development alignment also existed, with organizations unable to connect available learning resources to the specific capabilities employees needed to develop.
Harbinger developed an AI-powered skills intelligence layer to connect role information, skills, proficiency indicators, learning resources, and career pathways. The solution introduced a structured skills ontology, streamlined skill creation and validation, strengthened role-to-skill mapping, and linked development needs with relevant learning resources.
The platform analyzed learning and assessment signals to generate indicators of skill development and proficiency. These insights helped organizations gain a better understanding of capability gaps, identify development priorities, and support role-readiness decisions.
The solution enabled enterprises to define job roles, required skills, role hierarchies, and career progression pathways. Employees could understand the capabilities expected in their current roles, identify what they needed to develop for future roles, and explore potential pathways for career progression.
AI-based capabilities generated relevant skills for newly defined roles, validated existing role-to-skill associations, identified irrelevant mappings, and improved consistency across workforce datasets.
The solution gave organizations a more connected view of role requirements, workforce gaps, and development priorities, helping them align learning investments more closely with workforce needs. For employees, it made the capabilities and skills associated with current and future roles more visible and helped direct employees toward development resources relevant to their career goals.
By bringing together role requirements, capability insights, development gaps, and career pathways, the solution established a stronger foundation for workforce readiness and more performance-oriented development. This approach can help organizations address the challenges of workforce development and improve the overall effectiveness of their learning and development initiatives.
As a result, the company’s customers were able to make more informed decisions about workforce development, and employees were able to take a more proactive approach to their career development. The solution also helped organizations to better allocate their learning and development resources, leading to improved efficiency and effectiveness.
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