The ability to adapt and redistribute work at scale is becoming increasingly critical amid continued marketplace disruption. A pragmatic, work-centered approach to enterprise skilling provides the flexibility, agility and capacity needed to meet future demands. However, implementing such strategies is a complex endeavor, requiring organizations to:
- Align workforce capabilities with strategic business objectives
- Provide clarity for employees on how to enhance their value
- Enable managers to place the right talent in the right roles
- Develop comprehensive skill-development programs that are scalable and sustainable
An enterprise skilling architecture is the foundation for achieving these goals. It facilitates the assessment, validation and development of skills across the workforce while addressing the evolving needs of business units, human resources (HR), learning and development (L&D) and employees.
From generating skill taxonomies to personalizing employee learning experiences in the flow of work, artificial intelligence (AI) is revolutionizing enterprise skilling. But how do learning organizations enable an AI-infused enterprise skilling strategy, and what role do learning practitioners play in adopting and leveraging these tools effectively?
Understanding Skills Taxonomies and Skills Ontologies
A skills taxonomy is a structured framework that categorizes and organizes skills into hierarchical relationships. It provides a standardized way to define and label skills across an organization, enabling clarity and consistency in identifying workforce capabilities.
In contrast, a skills ontology goes beyond hierarchical structures to map the complex, interrelated connections between skills, tasks, roles and business outcomes. It enables organizations to understand how skills combine to accomplish specific tasks, identify transferable skills across roles and explore how skills relate to strategic business objectives. Skills ontologies are particularly valuable in dynamic environments where roles and skill requirements are constantly evolving.
The Role of AI in Modern Skilling Contexts
AI is reshaping enterprise skilling in several ways, particularly in supporting the creation and maintenance of skills taxonomies. While AI cannot build the nuanced, interconnected frameworks required for skills ontologies, it is highly effective at laying the groundwork through skills taxonomies. Here are four key applications:
- Skill Clustering and Relationship Identification: AI algorithms can group related skills and highlight natural relationships, helping organizations create more robust skills taxonomies. Future developments in AI may support the creation of knowledge graphs that define interconnected skills, laying the foundation for eventual ontology development.
- Normalization and Standardization of Skills: AI can consolidate different terms that may refer to the same skill (e.g., “software engineering” and “software development”), reducing redundancies and offering clarity. This process ensures a clean, consistent baseline for taxonomy creation, which can later inform ontology development.
- Tracking Emerging Skills Through Trend Analysis: AI can scan job postings, industry reports and academic research to identify emerging skills. Predictive models can forecast future skill demands, enabling organizations to proactively update their taxonomies and plan for enterprise-wide skilling initiatives.
- Automated Validation and Maintenance: AI can detect anomalies in skills data and align taxonomies with real-world relevance, ensuring they remain accurate and scalable over time.
AI-Infused Enterprise Skilling Strategies
The adoption of AI in skilling strategies involves several key steps:
- Building Robust Skills Taxonomies: Skills taxonomies are foundational in understanding workforce capabilities. AI can streamline the creation and maintenance of these taxonomies, ensuring they remain relevant and aligned with organizational goals. Over time, these taxonomies can evolve into more sophisticated skills ontologies with human input and refinement.
- Integrating Skills Ontologies: Once a robust taxonomy is established, organizations can map the complex relationships between skills, roles and business outcomes to develop a skills ontology. This enables organizations to:
- Identify transferable and adjacent skills for reskilling and upskilling.
- Mobilize talent efficiently into critical roles.
- Align workforce capabilities with evolving business needs.
3. Personalizing Learning in the Flow of Work: AI can tailor learning experiences by analyzing individual skill gaps and recommending relevant content. This approach empowers employees to take ownership of their development while aligning with organizational objectives.
4. Data-Driven Decision Making: AI can provide granular insights into workforce capabilities, enabling evidence-based decisions on talent deployment, training investments and strategic workforce planning.
The Role of Learning Practitioners
As AI becomes an integral part of skilling strategies, learning practitioners play a pivotal role in:
Building the Foundations for AI-Enabled Personalization
While AI is highly effective at generating skills taxonomies, it cannot construct the nuanced relationships required for a skills ontology without human input. Learning practitioners play a crucial role in shaping this foundation, ensuring that AI-driven insights are backed by a structured, evolving framework that reflects the realities of the workplace. Curating and refining these ontologies make it possible for AI to go beyond surface-level recommendations and provide precise, individualized skilling pathways that align with both employee growth and organizational strategy.
Influencing AI Integration
The role of learning practitioners goes beyond simply applying AI to existing practices. Learning practitioners need to take a step back and revisit the skilling workflow — identifying opportunities where AI can fundamentally improve processes, accelerate learning or enable previously unattainable outcomes. The focus should not just be on automation but on rethinking how AI transforms skilling at its core. Of course, learning practitioners must also collaborate with technical teams to ensure AI tools align with organizational learning goals and are implemented effectively.
Championing Human-Centric Change Management
Learning practitioners must guide employees and leaders through the cultural and operational shifts required to embrace AI-driven skilling approaches. The success of these strategies depends not just on the technology itself but on how people engage with it. AI brings intelligence, but it is human wisdom that ensures its effective application, enabling us to solve problems and drive innovation in ways previously unimaginable. For the first time, organizations must deeply reconsider the human role in work — not as a reaction to technology but as a proactive opportunity to redefine growth, creativity and strategic decision-making. Learning practitioners play a critical role in fostering this shift, helping employees and leaders see AI as a collaborator rather than a disruptor. By prioritizing transparency, accessibility and trust, they ensure that AI-driven skilling enhances human potential, creating a culture where transformation is driven not by technology alone, but by the people who shape and apply it.
Delivering Relevant AI-Driven Experiences
To ensure AI enhances skill development in a meaningful way, learning practitioners must go beyond simply deploying AI-driven recommendations. AI can tailor learning to individual needs, but its effectiveness depends on the human expertise guiding it. Practitioners need to focus on designing experiences that integrate AI insights with real-world applications, ensuring learning is contextually relevant and strategically aligned. This means creating opportunities where AI augments — not replaces — critical thinking, problem-solving and experiential learning.
Leading the AI-Driven Workforce Transformations
AI is transforming how organizations approach enterprise skilling, offering unparalleled opportunities to enhance workforce agility and adaptability. Organizations can future-proof their workforce and achieve lasting impact by leveraging AI tools to build dynamic skills taxonomies, personalize learning experiences and provide actionable insights. As key drivers of this transformation, learning practitioners must embrace their role as both strategists and enablers, ensuring that AI-infused skilling strategies deliver measurable value to employees and organizations alike.
