The Enterprise Skilling Challenge: Ontologies vs. Taxonomies

In our first blog in this “Enterprise Skilling Challenge” series, we explored why work-anchored strategies are key for driving successful enterprise skilling initiatives.

Building on that foundation, this blog dives into two critical frameworks—taxonomies and ontologies—shedding light on how they shape skilling strategies and their impact on workforce development.

So what are ontologies and taxonomies, and what do they have to do with skilling strategies? Enterprise skilling strategies are built using either a taxonomical or ontological method. Taxonomies offer a hierarchical classification of skills, while ontologies provide a more flexible and nuanced view of the relationships between skills, tasks, and job roles.

Because skills ontologies are rooted in the actual work that gets done, they illuminate the relationships between skills, tasks, and job roles, as seen below.

The Limitations of Traditional Skill Taxonomies

Traditional skills taxonomies have served organizations well by providing a structured way to categorize and organize skills. Think of a skill taxonomy as a family tree of skills—each skill belongs to a broader category, which in turn belongs to an even broader category. For example, a skills taxonomy for the leadership skill of decision making might look like this:

• Decision Making
  • Problem Analysis
    • Root Cause Analysis
    • Data Interpretation
  • Risk Assessment
    • Risk Identification
    • Mitigation

While this hierarchical approach is straightforward and easy to understand, it falls short in today’s dynamic business environment. Why? Because it fails to capture the complex relationships between skills and how they’re actually applied in the workplace.

The Power of Skill Ontologies

A skills ontology goes beyond simple hierarchical relationships by mapping out the complex web of connections between skills, tasks, job roles, and business outcomes. It offers a flexible and inclusive framework to categorize skills and their relationships to each other.

A skills ontology can answer crucial questions like:

• How do different skills combine to enable specific tasks?
• Which skills are transferable across different roles?
• What are the prerequisite relationships between skills?
• How do skills connect to business capabilities and work outputs?

This richer understanding is particularly valuable in the age of AI and rapid technological change. Roles and required skills are constantly evolving, and we must adapt to the needs of our business and workforce.

Real-World Example: Content Marketing Evolution in the AI Era

Let’s examine how a skills ontology proves superior to a taxonomy in a practical scenario.

Imagine your organization needs to transform traditional content marketers into AI-powered content strategists. A skills taxonomy might simply list the required skills:

• Content AI Tools
• SEO Analytics
• Marketing Automation
• Performance Metrics
• Generative AI Prompting

In contrast, a skills ontology would reveal:

1. How existing storytelling and copywriting skills connect to AI-enhanced content creation.
2. Which current skills (like audience analysis or content planning) provide a foundation for an AI-driven strategy.
3. Prerequisites needed before leveraging advanced AI tools, such as prompt engineering and content optimization principles.
4. Related technical skills crucial for modern content marketing, like data interpretation and automation workflow design.
5. Tasks where these skills are applied, from personalized content generation to predictive content performance analysis.
6. Dependencies between different AI capabilities and core marketing competencies.

A well-designed skills ontology also provides the ability to connect networks of skills across the business. This means it has the potential to go beyond obvious connections to uncover unexpected skill relationships that may not be apparent in a traditional taxonomy. For example, it could reveal how project management skills that currently exist in other departments could be invaluable for coordinating complex, AI-driven content campaigns. It could also reveal that soft skills from seemingly unrelated roles are crucial for content creation with the help of AI—like the adaptability and systems thinking often used by change management professionals.

By illuminating obscure connections and insights on potential talent matches, a skills ontology empowers organizations to tap into hidden capabilities, foster unexpected collaborations, and develop a more agile, cross-functional approach to skilling its people.

Leveraging Skill Ontologies for Future-Ready Workforce Planning

Organizations can use skills ontologies to better prepare for future workforce needs in several ways.

1. Dynamic Skill Gap Analysis: Rather than simply identifying missing skills, ontologies clarify the impact of skill gaps on actual work outputs and business capabilities.
2. Precise Learning Pathways: By understanding skill relationships and prerequisites, learning and development teams can create more effective learning journeys that build on existing capabilities.
3. AI-Powered Skill Recommendations: Skills ontologies provide the structured data needed for AI systems to make intelligent recommendations about the next skills to develop, alternative career paths, cross-training opportunities, and internal mobility options.
4. Strategic Workforce Planning: With a clear understanding of how skills connect to business capabilities, organizations can better predict and prepare for future skill needs.

The Strategic Imperative: Why Skill Ontologies Are the Future of Skilling

While skills taxonomies provide a useful starting point for organizing workforce capabilities, the complex demands of modern business require the richer understanding provided by skills ontologies. As AI continues to transform work, organizations that leverage skills ontologies will be better positioned to develop agile, future-ready, and high-performing teams.

About the Authors

Ella Richardson

Ella Richardson, Global Head of Consulting & Experience Design, GP Strategies Ella is responsible for learning excellence across the Learning Experience team. She leads the Learning Experience Design team, ensuring that our work is cutting edge and delivers sustainable change, by shaping transformational learning experiences, often enabled by emerging technologies. With a background in psychology, her expertise lies in getting under the skin of company culture and strategy, to define revolutionary approaches that embed measurable behavior change.

Matt Donovan

Chief Learning & Innovation Officer

Early in life, I found that I had a natural curiosity that not only led to a passion for learning and sharing with others, but it also got me into trouble. Although not a bad kid, I often found overly structured classrooms a challenge. I could be a bit disruptive as I would explore the content and activities in a manner that made sense to me. I found that classes and teachers that nurtured a personalized approach really resonated with me, while those that did not were demotivating and affected my relationship with the content. Too often, the conversation would come to a head where the teacher would ask, “Why can’t you learn it this way?” I would push back with, “Why can’t you teach it in a variety of ways?” The only path for success was when I would deconstruct and reconstruct the lessons in a meaningful way for myself. I would say that this early experience has shaped my career. I have been blessed with a range of opportunities to work with innovative organizations that advocate for the learner, endeavor to deliver relevance, and look to bend technology to further these goals. For example, while working at Unext.com, I had the opportunity to experience over 3,000 hours of “learnability” testing on my blended learning designs. I could see for my own eyes how learners would react to my designs and how they made meaning of it. Learners asked two common questions: Is it relevant to me? Is it authentic? Through observations of and conversations with learners, I began to sharpen my skills and designed for inclusion and relevance rather than control. This lesson has served me well. In our industry, we have become overly focused on the volume and arrangement of content, instead of its value. Not surprising—content is static and easier to define. Value (relevance), on the other hand, is fluid and much harder to describe. The real insight is that you can’t really design relevance; you can only design the environment or systems that promote it. Relevance ultimately is in the eye of the learner—not the designer. So, this is why, when asked for an elevator pitch, I share my passion of being an advocate for the learner and a warrior for relevance.