Pearson, a global leader in education and lifelong learning, empowers individuals to achieve their aspirations through impactful learning experiences. Central to its strategy is a robust skills ontology that helps individuals build clear career pathways while enabling organizations to adapt to evolving market demands. This mission is supported by Credly, the world’s largest credentialing marketplace, and Faethm, an AI-driven platform aligning learning with future workforce needs. Pearson’s skills-based initiatives showcase how a common skills language can transform learning and career pathways to drive organizational growth and adaptability.

Approach and Actions

Pearson's skills ontology serves as a cornerstone for tailoring learning experiences, support career progression, and enable strategic workforce planning. Its development began with a comprehensive analysis of established frameworks, including ONET, ESCO, and the World Economic Forum’s Global Skills Taxonomy. By cross-walking these frameworks, Pearson created a proprietary ontology encompassing over 9,050 skills. Structured into a five-level hierarchy and a five-level proficiency framework, it enables detailed skill mapping and cross-referencing across occupations, learning materials, and career pathways.

Pearson’s experience in supporting workforce development strategies has highlighted the importance of determining the appropriate level of granularity in a skills taxonomy. A well-designed taxonomy must strike a balance—detailed enough to provide actionable insights and contextual relevance but not so complex as to create redundancies or overwhelm users. The required level of granularity often depends on the specific use case. For example, hiring processes may focus on broader skill categories, such as AI or big data, to identify talent, whereas tailored learning programs necessitate a more granular breakdown into specific skills like machine learning, deep learning, or AI ethics. Moreover, linking the taxonomy to specific job roles and incorporating proficiency levels enhances its practical application. Clearly defining the proficiency levels required for each role enables more precise workforce planning, targeted learning programs, and effective skills development.

Interoperability is essential for maximizing the value of a skills taxonomy. A common challenge for organizations is the use of diverse technological solutions or platforms for various functions, such as hiring, talent management, and learning and development. Establishing a unified skills language that links learning, tasks, and career pathways allows organizations to enhance workforce agility and align educational initiatives with strategic objectives. Pearson addresses this challenge with API-driven solutions and advanced AI models that ensure both adaptability and interoperability. These technologies consolidate and structure unstructured data from diverse sources—such as job descriptions, resumes, and course materials—aligning it seamlessly with the taxonomy to create cohesive and actionable insights.

A common skills language offers transformative advantages. It helps organizations better understand workforce capabilities, maximize training investments, and establish clear skills-based pathways for employees. This approach reduces time-to-hire, enhances employee engagement, and boosts productivity. By embedding skills into all aspects of its solutions, Pearson ensures its taxonomy supports workforce growth, adaptability, and alignment with the evolving demands of a dynamic global market.

Key Lessons Learned

Pearson identified several critical success factors in developing and implementing skills taxonomies:

• Adapting the Skills Taxonomy Granularity to Strategic Needs: Organizations must align the level of granularity with strategic needs, such as workforce planning, skills based hiring or targeted learning. An effective skills taxonomy balance granularity and usability. Too much detail can result in an overwhelming volume of unstructured data, while insufficient detail limits actionable insights.
• Interoperability and Technology Integration: Interoperability unlocks the full potential of a skills taxonomy. APIs and AI tools enable efficient mapping of skills to learning content, and career pathways. These technologies streamline the extraction, normalization, and analysis of skills data, ensuring scalability and actionable insights. 
• Demonstrate Return on Investment for Skills Investment: Demonstrating the tangible benefits of for skills initiatives is crucial for sustaining engagement and momentum. Tangible benefits such as reduced hiring times, improved retention, enhanced internal mobility, and higher training completion rates demonstrate the value and impact of a skills-based initiatives.
• Establish the Right Frameworks and Processes to Sustain Skills-Based Practices: Adopting a common skills language is just the starting point. Organizations must also establish clear roles and responsibilities, robust frameworks that directly link skills to measurable business outcomes—such as productivity gains, innovation, or employee retention—and operational processes that support effective implementation of skills-based practices. Without these foundational elements, many organizations face challenges in driving the adoption and long-term success of skills-based practices.