In the first two blogs in our “Enterprise Skilling Challenge” series, we explored why work-anchored strategies are key for successful enterprise skilling initiatives and how skill ontologies can be leveraged to future-proof your workforce.
This blog, the third and final one in the series, dives into how AI can transform your enterprise skilling approach.
Repackaging Your Data Can Make a Massive Skilling Impact
In 2013, a study published in the American Journal of Preventative Medicine examined apple consumption in school cafeterias. The researchers found that six schools that served apples in sliced form saw a 71% increase in sales and a nearly 50% reduction in food waste compared to three schools that offered whole apples.
This simple change in presentation demonstrated that convenience drives many of our decisions and that low-cost, scalable environmental changes can make massive impacts. Similarly, repackaging information in more accessible and actionable formats within organizations also has the potential to drive significant positive change. This is precisely why skill ontologies, with the assistance of artificial intelligence (AI), have the capacity to reshape entire organizations.
Skill ontologies provide a more sophisticated framework for building an effective skilling strategy compared to traditional skill taxonomies. Ontologies expose the rich relationships between skills, job tasks, and roles. Ontologies anchor the skills to the actual work people perform daily rather than relying solely on job descriptions.
By mapping these connections, organizations can better understand:
- Their workforce’s skills.
- The skills required for critical roles.
- The adjacent skills that enable targeted reskilling and upskilling initiatives.
- How to mobilize people into critical roles based on existing skill sets.
This granular, data-driven approach allows companies to slice and dice their talent pool in ways that align with evolving business needs, much like how slicing apples made them more appealing and accessible to students.
For organizations, AI-powered skilling strategies provide the insights needed to repackage workforce data, unlocking new opportunities to upskill and reskill employees in ways that benefit both the business and its people.
The Role of AI in Enterprise Skilling Strategies
AI can analyze large volumes of unstructured data from diverse sources (like job descriptions, resumes, and training content) to identify essential skills within a given industry or role. While a skills ontology needs much more than data from analyzing documents, AI can help reinforce, refine, and strengthen a skills taxonomy to prepare for the creation of a skills ontology.
Using AI to extract information from existing skill-related content means humans can spend more valuable time conducting work analyses and understanding the nuance and true value-add of specific job roles—the very heart of a skills ontology. Using AI helps make the intensive process of building and maintaining a skilling architecture more viable over the long term.
4 Ways AI Can Enable Your Skilling Architecture
AI makes creating and maintaining a skilling architecture much more sustainable in several critical ways.
Skill Clustering and Relationship Identification
Clustering algorithms allow AI to group similar skills, identify which ones often appear together, and highlight natural relationships between skills. It’s also possible that, in the future, AI can help define the relationships between skills by creating knowledge graphs that help define the interconnectedness of skills, showing which are foundational, which require prerequisites, and which are advanced or complementary.
Normalization and Standardization of Skills
AI-driven synonym detection can consolidate different terms that may refer to the same skill (like “software engineering” and “software development”), reducing redundancies to improve clarity—an essential part of building a clearly defined skills taxonomy. A skills ontology, on the other hand, might require an investigation into why specific roles define their skill as “development” versus “engineering.” Normalizing and standardizing skills before developing a skills ontology ensures your data is uniform. This provides you with a clean slate from which to build a nuanced skills ontology.
Tracking Emerging Skills with Trend Analysis
AI can scan job postings, industry reports, and academic publications over time to identify new and in-demand skills as they emerge. Predictive AI-enabled models can go further, forecasting which skills are likely to grow in demand based on historical data and industry insights. Whether your skilling strategy is built from taxonomies or ontologies, this sort of organization- and industry-specific trend analysis is incredibly valuable when preparing and planning future enterprise-wide skilling initiatives.
Automated Validation and Maintenance
As a skilling architecture becomes more mature, AI can support its continuous improvement by detecting anomalies or inconsistencies in data, like mislabeled skills or outdated terms. By maintaining feedback loops with users, AI models can update skill categories to reflect real-world relevance, ensuring the architecture remains accurate, relevant, and aligned with organizational goals.
Measurement and AI-Powered Skill Ontologies
Organizations can leverage AI-powered skilling architectures to generate highly informed and actionable skill development recommendations for their enterprise skilling strategies. However, while many organizations prioritize building a comprehensive skilling program, they frequently fail to embed the necessary metrics to prove and improve its effectiveness.
Without the ability to track progress, it becomes nearly impossible to assess whether employees are truly mastering new skills, applying them in real-world scenarios, and driving meaningful productivity improvements. Measurement is a foundational element that needs to be built into the strategy from the outset.
For measurement to be truly effective, it must be anchored to the day-to-day work people perform—one of the reasons why skill ontologies are superior to skill taxonomies. To be properly measured, skills need to be both tangible and quantifiable. For example, an individual with a level 3 proficiency in Excel has likely mastered intermediate functions like VLOOKUPs, pivot tables, and advanced data visualizations.
Within a well-designed skills ontology that can reveal related skills, it would become clear that this person’s Excel proficiency also indicates a level 2 data analytics skillset, meaning they can not only manipulate data but also draw meaningful insights to solve business challenges. An organization with a thoughtfully organized skills ontology and measurement system can identify this individual’s aptitude for data analytics and mobilize them accordingly.
The Role of AI in Enterprise Skilling Measurement
AI can support enterprise skilling measurement by:
- Helping to map employees’ existing skills and capabilities to the skills ontology using data from profiles, performance reviews, project records, and training data.
- Identifying potential skill gaps, development paths, and internal mobility opportunities aligned with individual and organizational goals to be verified and validated.
- Tracking the application and possible impact of new skills over time to assess the effectiveness of training initiatives.
- Providing insights that allow organizations to refine skilling strategies and drive greater value.
With these insights, organizations can continuously refine their skilling strategies to drive maximum value. The key is to ensure skills are directly tied to job tasks and measurable results. Are employees applying their skills to improve processes or drive meaningful business outcomes? If the answer is unclear, the skilling strategy isn’t fully realized.
Measurement must be continuous, tracking skills at various stages to identify what’s working and where gaps exist. A well-designed measurement framework can pinpoint breakdowns in productivity. For instance, if employees have completed training and follow the correct processes but workflow issues like errors or inefficiencies persist, there may be a mismatch between the skills taught and what’s needed in practice. This type of insight highlights gaps in the skilling strategy, guiding adjustments to training and the overall approach to measurement.
Slice Those Apples: Anchoring Skilling Strategies to the Work
In learning and development, we all want the same thing: to harness our people’s skills to achieve maximum contribution for the organization and maximum satisfaction for employees. But to achieve that—to truly capture the potential of our employees and the breadth and depth of the skills they possess and will need in the future—we must develop comprehensive skilling strategies that are actually anchored to the work they perform every day, not just their job description.
We have to put in the work. But with a bit of help from AI, we can slice those apples.
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