In today’s rapidly evolving business landscape, the only constant is change. Digital transformation, the rise of artificial intelligence, and shifting market demands are fundamentally altering the way organizations operate and compete. The skills that were valuable five years ago may be obsolete today, and the skills needed five years from now are likely just emerging. This relentless pace of change puts immense pressure on organizations to remain agile, innovative, and competitive. At the heart of this challenge is a new central focus: moving from managing roles to understanding and managing skills. This shift is critical for survival, as the ability to adapt, reskill, and redeploy talent is becoming the primary differentiator between success and failure.
Defining the Skills Gap Crisis
Organizations that have not yet adopted a structured approach to managing their workforce’s abilities often struggle to assess the skills and capabilities they possess. This leads to a critical business problem known as the “skills gap.” This gap is the chasm between the skills your organization needs to achieve its strategic goals and the skills your workforce currently has. Many businesses are operating with a significant skills gap, which hinders their performance and manifests in tangible, negative outcomes. These can include low-quality products or services, increased project timelines, higher employee attrition, and a fundamental inability to drive meaningful, innovative change. Without a clear map of your skills, you are flying blind, unable to plan for the future.
Consequences of an Unmeasured Workforce
The consequences of an unmeasured or poorly understood workforce are severe. When you cannot quantify the skills of your employees, you cannot properly resource key initiatives. Projects are staffed based on who is available rather than who is best qualified, leading to predictable failures or delays. Furthermore, this lack of clarity makes it impossible to close your skills gap. You may invest heavily in generic training programs that miss the mark, or you may try to “hire” your way out of the problem, a costly and often futile strategy in a competitive talent market. The inability to see your own strengths and weaknesses at a granular, skill-based level is a significant strategic disadvantage.
The High Cost of Skill Ambiguity
Ambiguity is the enemy of efficiency. When skill requirements are undefined, the entire talent management pipeline breaks down. In recruitment, job descriptions become vague, attracting unqualified candidates while deterring the right ones. In training, development budgets are wasted on programs that are not aligned with business needs. In performance management, assessments become subjective and prone to bias, as managers have no objective framework to measure an employee’s capabilities. This ambiguity leads to frustration for both employees, who see no clear path for growth, and for leaders, who feel unable to execute their strategies. This friction is a direct, though often hidden, drain on resources, morale, and productivity.
Why Traditional Role-Based Structures Are Failing
For decades, organizations have been built around a role-based structure. An employee was hired into a “job,” with a “title,” and a “description.” Their career path was a linear ladder of climbing those titles. This rigid, siloed model is no longer sufficient. Today’s work is dynamic and project-based, requiring cross-functional teams of people with diverse, evolving skill sets. An employee’s value is not defined by their title, but by the collection of skills they bring to a project. A “Marketing Manager” may also have advanced skills in data analysis, and a “Software Engineer” may be an excellent public speaker. A traditional, role-based organization has no way to see, track, or leverage these “hidden” skills, leaving vast amounts of talent potential untapped.
The Pressure on Talent Development
This new reality places talent development and human resources teams under immense pressure. They are now tasked with filling vacancies, retaining top performers, and future-proofing the entire organization, all while navigating a skills landscape that changes quarterly. They are expected to answer critical questions from leadership: Do we have the skills to launch this new AI initiative? Can we transition our sales team to a solution-selling model? Who are our high-potential leaders for the next generation? Without a skills taxonomy, answering these questions is a matter of guesswork, anecdotes, and data buried in disconnected spreadsheets. This is an untenable position for a function that is meant to be a strategic partner to the business.
Fragmented Learning and Wasted Resources
One of the most visible symptoms of a missing skills framework is a fragmented and inconsistent approach to learning and development. Without a structured approach, L&D initiatives can become a “flavor of the month,” driven by trends rather than by a-data-driven analysis of the organization’s actual needs. One department might invest in a leadership program, while another buys a technical certification, all with no connection to a central strategy. This leads to suboptimal results, wasted resources, and a workforce that is confused about what they should be learning. This fragmentation also makes it incredibly difficult to get executive buy-in for future training programs, as it is impossible to prove a return on investment.
The Challenge of Measuring Proficiency
Even if an organization can identify a skill, such as “data analysis,” how does it measure it? Without a standardized framework, assessing an employee’s proficiency level is a subjective and challenging task. A manager in one department might rate an employee as an “expert,” while another manager would rate the same employee as “intermediate.” This inconsistency makes it impossible to create reliable talent pools or to track an employee’s progress over time. A core function of a skills taxonomy is to solve this problem by providing a clear, objective, and shared rubric for what “good” looks like at every level, from beginner to expert.
The Shift to a Skills-Based Organization
To solve these challenges, leading organizations are transitioning from a role-based to a skills-based organization. This is a fundamental change in operating philosophy. It means that all talent-related decisions—hiring, promotion, compensation, and development—are based on an individual’s verified skills, not just their job title or tenure. This model provides immense benefits: it creates a more agile and resilient workforce, unlocks internal mobility, improves employee engagement by showing clear paths for growth, and allows the organization to respond to market changes with precision. However, this transition is impossible without a central, standardized mechanism to organize and understand skills.
The Foundation for Change: The Skills Taxonomy
A skills taxonomy is that mechanism. It is the essential, foundational component of any skills-based organization. It helps standardize what skills are, their hierarchy and importance within the organization, and much more. It is the “source of truth” for all things skill-related. This mechanism to organize skills brings the clarity that talent development teams desperately need to support broader workforce transformation initiatives. It provides the map that allows you to see where you are, the compass to show you where you need to go, and the common language that everyone in the organization can use to navigate the path forward.
Defining the Skills Taxonomy
A skills taxonomy is a hierarchical framework that classifies and organizes skills. It is a logical, structured system that takes the vast, chaotic universe of human capabilities and puts them into a clear, manageable order. It groups related skills into groups and subgroups based on their similarities, and it often defines a hierarchy of importance or complexity. The primary purpose of a taxonomy is to provide a structured, standardized way to identify, define, name, and measure skills within an organization. It creates a common language, a “dictionary” of skills, that everyone in the company can share and understand. This moves the concept of “skills” from an abstract idea to a concrete, manageable asset, much like a financial chart of accounts.
A Simple Analogy: The Library of Your Company
Think of your organization’s total skill set as a massive, disorganized library. Thousands of books (skills) are scattered on the floor in random piles. Some books are in multiple, slightly different versions, and no one knows which one is the “official” version. It is impossible to find what you are looking for, and you have no idea what books you are missing. A skills taxonomy is the act of building the library. It is the process of deciding on a classification system (like the Dewey Decimal System), creating sections (categories like “Technical Skills,” “Leadership Skills”), building shelves (subcategories like “Software Development,” “Data Analysis”), and then labeling every single book (defining each skill) and placing it in its proper, logical location. This act of organizing brings clarity and makes the entire collection usable.
The Core Components of a Taxonomy
A robust skills taxonomy is built from several core components, each ofwhich serves a specific purpose. The first component is the individual skill itself, which is the most granular, observable, and measurable unit of capability. The second component is the category or “skill domain,” which is a high-level grouping of related skills. The third is the subcategory or “skill family,” which provides a more specific grouping within a larger category. Finally, and perhaps most critically, the fourth component is a set of proficiency levels, which provide a standardized rubric for measuring an individual’s mastery of a specific skill. Together, these components create a comprehensive map of the organization’s capabilities.
Component 1: Identifying Individual Skills
The foundation of any taxonomy is the “skill” itself. This is the “book on the shelf” in our library analogy. A skill must be defined in a way that is clear, concise, and distinct. It is an observable, measurable, and learnable capability. Organizations must differentiate between different types of skills. Hard skills (or technical skills) are specific, teachable abilities, such as “Python Programming,” “Financial Modeling,” or “Network Management.” Soft skills (or human skills) are interpersonal and cognitive abilities, such as “Communication,” “Problem-Solving,” or “Adaptability.” A comprehensive taxonomy must include both, as they are both critical for success. The process of identifying and naming these skills is a crucial first step, as it creates the shared vocabulary for the entire company.
Component 2: Creating Logical Categories and Subcategories
Once individual skills are identified, they cannot exist in a flat, thousand-item list. They must be organized. This is where the hierarchical structure comes in. Similar skills are grouped into logical subcategories. For example, the skills “Python Programming,” “Java,” and “C++” might be grouped under the subcategory “Software Development.” The skills “Data Cleaning,” “Data Visualization,” and “Statistical Modeling” might be grouped under “Data Analysis.” These subcategories are then rolled up into even broader categories. The “Software Development” and “Data Analysis” subcategories might both fall under the high-level category of “Technical Skills.” This hierarchical structure (Category > Subcategory > Skill) makes the taxonomy easy to navigate and understand.
The Hierarchical Structure Explained
The hierarchical nature of the taxonomy is its key feature. It provides a structured way to browse and analyze skills at different levels of magnification. A senior leader may only want to see the organization’s strengths and weaknesses at the high-level “Category” view (e.g., “We are strong in Technical Skills but weak in Leadership Skills”). A department head might need to drill down into the “Subcategory” view (e.g., “Within Technical Skills, our Data Analysis team is strong, but our Network Management team needs development”). A front-line manager or an employee will operate at the granular “Skill” level, identifying specific skills to learn or apply to a project. This multi-level structure serves the needs of all stakeholders, from the C-suite to the individual contributor.
Component 3: Defining Proficiency Levels
This is arguably the most complex and most valuable component of a skills taxonomy. It is not enough to simply know if an employee “has” a skill. You must know “how good” they are at it. A taxonomy defines standardized proficiency levels for each skill. A common approach is a five-level scale, ranging from “Beginner” to “Expert.” These levels must be clearly defined to ensure they are applied consistently across the entire organization. This standardization is what allows for objective assessment and measurement. It is the “ruler” that allows you to measure the skill, making it a tangible asset that can be tracked, compared, and developed over time.
Why “Beginner to Expert” Is Not Enough
A truly robust taxonomy goes beyond simple labels like “Beginner” or “Expert.” To make these levels truly objective, they must be defined by behavioral indicators or observable rubrics. A generic label is subjective. A behavioral rubric is concrete. For example, for the skill “Communication,” the levels might be: Level 1 (Beginner): “Can articulate simple ideas clearly in one-on-one settings.” Level 2 (Intermediate): “Can structure and deliver a clear presentation to a small group.” Level 3 (Advanced): “Can confidently present complex topics to a large, mixed audience and handle difficult questions.” Level 4 (Expert): “Can develop and articulate a strategic communication plan for the entire department.” This rubric removes subjectivity and gives managers a clear tool for assessment.
The Importance of a Common Language
Ultimately, the primary benefit of a well-developed skills taxonomy is the creation of a common language for talent. When everyone in the organization uses the same words to describe the same skills and the same levels of proficiency, ambiguity dissolves. A manager in the finance department and a manager in the marketing department can have a conversation about “project management” skills and know they are talking about the same set of capabilities and standards. This shared vocabulary is the glue that connects all talent management processes. It ensures that the skills listed in a job description are the same skills assessed in a performance review, which are the same skills taught in a training program. This consistency is the key to an efficient and effective talent strategy.
What is a Skills Taxonomy? A Recap
As we established in the previous part, a skills taxonomy is a hierarchical framework for classification. Its primary function is to organize skills by putting them into a logical, tree-like structure. It answers the question, “What is this skill and where does it belong?” For example, the skill “Python Programming” might be classified under the subcategory “Software Development,” which in turn falls under the main category “Technical Skills.” This is an incredibly useful system for creating order from chaos. It provides a standardized list, a clear hierarchy, and a way to manage skills in discrete, logical buckets. However, this model has a fundamental limitation: it is rigid and one-dimensional. It tells you what a skill is, but not how it relates to other skills.
What is a Skills Ontology? The Next Evolution
A skills ontology is the next evolution of this concept. It is a much richer, more dynamic, and more powerful way to represent skills. While a taxonomy focuses on hierarchy and classification, an ontology focuses on the relationships and connections between skills. An ontology is a “web” or a “graph” rather than a “list” or a “tree.” It notes only defines the skill “Python Programming” but also maps its relationship to other skills. For example, an ontology would define that “Python Programming” is “similar to” “Java,” “is a type of” “Programming Language,” “is used for” “Data Analysis,” and “is often paired with” “SQL.” This multi-dimensional mapping of relationships is what makes an ontology so powerful.
The Critical Difference: Hierarchy vs. Relationship
The distinction between hierarchy and relationship is the single most important concept to grasp. A taxonomy is a rigid, top-down system. A skill can typically only live in one place in the hierarchy. This is simple, but it does not reflect the complex, overlapping nature of human capabilities. An ontology, on theother hand, is a fluid, multi-dimensional network. A single skill can have many different types of relationships with many other skills, categories, and even job roles. It is a much more accurate and realistic representation of a real-world skills ecosystem. A taxonomy tells you what skills you have in your “library”; an ontology tells you how all the books in that library are interconnected.
An Analogy: The Shopping List vs. The Recipe
A skills taxonomy is like a shopping list for a grocery store, organized by aisle. You have a “Dairy” section with “Milk,” “Cheese,” and “Yogurt,” and a “Produce” section with “Flour,” “Sugar,” and “Eggs.” This is an organized hierarchy, a taxonomy. It tells you what you have, but not what you can do with it. A skills ontology, in contrast, is like a recipe. The recipe connects these items. It says that “Flour,” “Sugar,” and “Eggs” (from different aisles) can be combined to create a “Cake.” It shows the relationship between the skills, the process, and the outcome. An ontology is the recipe book for your entire organization’s talent, showing you how to combine skills to create value.
How an Ontology Reveals Skill Adjacency
The most powerful application of an ontology is its ability to reveal “skill adjacency.” This is the concept that some skills are “close to” or “related to” other skills. For example, an ontology would show that a person with a high proficiency in “Data Analysis in Excel” has a strong adjacency to the skill “Data Analysis in Python.” The foundational concepts of data manipulation and analysis are similar, even if the tools are different. This means it would be relatively easy and fast for that employee to “reskill” and learn Python. A traditional taxonomy would simply list these in two different, unrelated categories (“Spreadsheet Software” and “Programming Languages”) and would never reveal this powerful connection.
The Power of Transferable Skills
Skill adjacency is the key to unlocking internal mobility and identifying transferable skills. In a rapidly changing job market, job roles are being eliminated and new ones are being created. An ontology allows an organization to see these changes coming and prepare its workforce. For example, a company might know that its “Customer Service Representative” role is being automated. A taxonomy would simply show that these employees are now “obsolete.” An ontology, however, would show that these employees have high-proficiency skills in “Empathy,” “Problem-Solving,” and “Communication.” It would also show that these same skills are the foundational requirements for the new, high-demand role of “Customer Success Manager.” The ontology provides a clear “reskilling path,” showing that these employees do not need to be fired, but can be retrained with a small, targeted program.
Why a Taxonomy is Often the First Step
Given all the benefits of an ontology, why would anyone still build a taxonomy? The reason is that an ontology is exponentially more complex to build and maintain. A taxonomy is a good, solid first step. It is an arduous process, as we will see, but it is achievable. It solves the most immediate problem: creating a common language and a clear organizational structure. Many organizations start by building a robust taxonomy, getting their skills organized, and mapping them to job roles. This process alone provides immense value. They can then “evolve” their taxonomy into an ontology over time, gradually adding the relationship layers (like “is similar to” or “is a prerequisite for”) once the foundational hierarchy is in place.
From Static Lists to a Dynamic, Connected Web
The evolution from taxonomy to ontology is a shift from a static model to a dynamic one. A taxonomy is often a “snapshot” in time. It is a list that is manually built and then manually updated, perhaps once a year. An ontology, especially a modern, AI-driven one, is a living, breathing system. It is a “web” that is constantly updating itself. When a new skill emerges in the market (like “Generative AI Prompt Engineering”), it is not just added to a list; it is added to the web, and its relationships to all other skills (like “Natural Language Processing,” “Creative Writing,” and “Data Analysis”) are automatically mapped. This dynamic nature is what allows an organization to keep pace with the market in real time.
Choosing Your Model: Do You Need Hierarchy or Connection?
The choice between a taxonomy and an ontology depends on your organization’s maturity and goals. If your organization is currently in chaos, with no standardized skill definitions or job roles, you should start with a taxonomy. Your primary goal is to create clarity, consistency, and a common language. A simple, hierarchical classification system is the perfect tool for this. It will allow you to solve your most pressing challenges in recruitment, performance management, and L&D. If your organization is more mature and your primary goal is to unlock agility, drive internal mobility, and plan for future workforce transformations, you need an ontology. Your challenge is not “what skills do we have?” but “what can we do with the skills we have, and how can we quickly get the skills we need?” You need the power of skill adjacency and relationship mapping. For most organizations, the practical answer is a hybrid model: a system built on a clear taxonomic hierarchy, but enriched with ontological relationships.
The In-House Challenge: Building Your Own Taxonomy
For organizations that choose to “build” their own skills taxonomy from scratch, the process is a significant undertaking. This is the “arduous process” that many reports allude to, and it is the primary reason many companies seek external help. Creating a comprehensive, functional, and durable skills taxonomy requires a massive, cross-functional project that demands significant time, effort, and stakeholder management. It is not a task that can be completed by a single HR professional over a weekend. It is a rigorous, multi-phase project. However, for organizations with a unique culture or highly specialized skill sets, a custom-built taxonomy can be a powerful, perfectly-tailored asset.
Phase 1: Assembling the Stakeholder Team
Before any work begins, the first phase is to assemble a cross-functional stakeholder team or “skills council.” This is the single most critical step for ensuring buy-in and success. This team cannot be composed solely of HR professionals. It must include department heads, respected team leaders, and high-performing employees from all key areas of the business. You need the people who actually do the work to help you define the work. This team will be responsible for validating the skills, defining the categories, and championing the final taxonomy within their own departments. Without this diverse group of stakeholders, your taxonomy will be an “HR project” that lacks real-world credibility.
Phase 2: Identifying and Auditing Current Skills
Once the team is in place, the “data gathering” phase begins. The goal is to identify the essential skills required for various roles within your organization. This is a massive research project. The team will gather insights from multiple sources. They will conduct interviews with department heads and top performers (“What skills really matter for success in your team?”). They will perform a detailed analysis of hundreds of current job descriptions to extract common skill keywords. They will also look at industry reports and competitor job postings to identify emerging skills that the organization may not have yet but will need in the future. This process results in a massive, unorganized “master list” of hundreds or even thousands of potential skills.
Phase 3: Categorizing Skills into a Logical Hierarchy
This phase is where the “taxonomy” (the hierarchy) is actually built. The team must now take the massive, flat list of skills and organize it. This is often done through a series of “card sorting” workshops. Each skill is written on a card, and the stakeholder team works collaboratively to group similar skills together. This is an iterative process of “affinity diagramming.” The team will first create small, granular groups (“subcategories”) based on their expert knowledge. For example, “Python,” “Java,” and “C++” are naturally grouped. Then, the team will group the subcategories. “Software Development,” “Data Analysis,” and “Network Management” are all grouped under the high-level category “Technical Skills.” This process of sorting, grouping, and labeling is what creates the logical, navigable, hierarchical structure of the taxonomy.
Phase 4: Defining Meaningful Proficiency Levels
After the skills are identified and categorized, the team must tackle the most complex part: defining proficiency. It is not enough to know if an employee has a skill; you must know how good they are at it. The team must establish a standardized scale for the entire organization. A five-level scale is common (e.g., 1-Beginner, 2-Intermediate, 3-Advanced, 4-Expert, 5-Master), but the number of levels is less important than the clarity of their definitions. These definitions are the “ruler” you will use to measure skills, so they must be clear, objective, and distinct. This prevents the subjective ambiguity where one manager’s “expert” is another’s “intermediate.”
Beyond “Novice to Expert”: Creating Behavioral Rubrics
To make proficiency levels truly objective, the team must create behavioral rubrics for each level of each skill. This is the most time-consuming but most valuable part of the entire “build” process. A label like “Advanced” is still subjective. A behavioral rubric is a concrete description of what an employee can do at that level. For example, for the skill “Data Visualization,” the rubric might be: 1-Beginner: “Can create a simple bar or pie chart from a clean spreadsheet.” 2-Intermediate: “Can select the appropriate chart type for a given dataset and combine multiple charts into a basic dashboard.” 3-Advanced: “Can build complex, interactive dashboards that integrate multiple data sources and tell a clear, persuasive story.” 4-Expert: “Can design novel or non-standard visualizations to represent complex, high-dimensional data, and teaches this skill to others.”
Phase 5: Mapping the Taxonomy to Job Roles
Once the skills hierarchy and proficiency rubrics are defined, the next logical step is to map this taxonomy to the organization’s existing job architecture. This is where the taxonomy becomes a practical tool. The team works with managers to define “skill profiles” for every role in the company. A “Data Analyst” role, for example, might be mapped to require “Data Visualization” (Level 3), “SQL” (Level 3), and “Communication” (Level 2). This “skill-based” job description is a massive improvement over a traditional, task-based one. It provides ultimate clarity for hiring, creates a clear roadmap for training, and gives employees a precise path for career development.
Phase 6: Validation, Piloting, and Gaining Buy-In
The taxonomy is not finished once the first draft is complete. It must be validated. The team should take the completed framework and “pilot” it with a single, cooperative department. They can use it to re-write job descriptions, assess the skills of the current team, and build a test learning path. This pilot will invariably reveal flaws: missing skills, confusing definitions, or levels that are too broad. The team must gather this feedback and iterate on the taxonomy, refining it until it is a practical and useful tool. This validation process is also a key part of change management, as the “pilot” team’s success will create the “social proof” needed to get the rest of the organization to adopt the new framework.
The Pros and Cons of the “Build” Approach
The primary “pro” of the in-house build approach is that the final product is a perfectly tailored, custom-fit asset. It will reflect the organization’s unique culture, its specific technical needs, and its precise business language. This high degree of customization can be a powerful competitive advantage. The “cons,” however, are significant. The process is extremely slow and time-consuming, often taking a year or more to complete. It is resource-intensive, pulling valuable senior leaders and employees away from their primary jobs. Finally, the “build” model places the entire burden of ongoing maintenance on the organization, a challenge we will explore later.
Why Most Companies Abandon the In-House Build
Given the significant hurdles, it is not surprising that many organizations abandon the in-house “build” process halfway through. The project often stalls. Getting consensus from every stakeholder on every skill definition is a logistical and political nightmare. Just getting all the required senior leaders into the same room for multiple, day-long workshops can be nearly impossible. As the market changes, the skills list they started with becomes obsolete before they have even finished. This is why, in recent years, it has become far less common for organizations to build a comprehensive skills taxonomy entirely in-house. They have increasingly turned to an alternative: the “buy” model.
The “Buy” Decision: Why Most Organizations Outsource
The in-house “build” model for a skills taxonomy, as we have seen, is an arduous, expensive, and slow-moving project. It places an enormous burden on an organization’s internal resources and often fails to keep pace with the rapid evolution of the market. This is why the “buy” model has become the default path for most modern organizations. This approach involves working with an outside consultant or, more commonly, licensing a technology solution from a specialized vendor. The primary drivers for this decision are speed, expertise, and sustainability. A “buy” solution can often be implemented in a matter of weeks or months, not years. It provides immediate access to a pre-built, expert-validated library of skills, and it outsources the critical, ongoing maintenance of that library to the vendor.
The Role of the External Consultant
For some organizations, the “buy” model starts with an external consultant. A talent management consulting firm can be hired to manage the process of building a semi-custom taxonomy. This is a hybrid approach. The consultants bring the methodology, the project management, and an external, objective perspective. They will perform the stakeholder interviews, facilitate the workshops, and use their industry expertise to provide a “starter kit” of skills. This can significantly accelerate the process and help navigate the internal politics that often derail in-house projects. However, this is still a one-time project. At the end of the engagement, the organization is left with a static taxonomy that it is now responsible for maintaining.
The Rise of AI-Driven Skill Management Platforms
The more common and more powerful “buy” solution is to adopt a technology platform. In recent years, a new market of software solutions has emerged to solve this exact problem. These platforms, often integrated into a larger Human Capital Management (HCM) or Learning Management System (LMS), provide a “skills engine” as a service. These systems go far beyond a simple, static list. They provide a dynamic, AI-generated skills ontology—often referred to as a “skills cloud”—that is continuously updated and managed by the vendor. This is a “living” solution that evolves in real time as new skills emerge in the global market, removing the maintenance burden entirely.
How AI Generates a Dynamic Skills Ontology
These modern platforms use machine learning and artificial intelligence to discover, classify, and connect skills. Instead of relying on manual stakeholder interviews, these systems scan massive, public datasets to build their ontology. They analyze millions of job postings, professional social media profiles, and academic publications to identify skill trends as they happen. When a new skill like “Generative AI” starts trending in the market, the AI engine identifies it, defines it, and, most importantly, uses its ontological graph to instantly map its relationships to other skills like “Natural Language Processing” and “Prompt Engineering.” This provides a taxonomy that is always current, something a manual in-house process could never achieve.
Data Sources: Painting a Holistic Picture of an Employee
These platforms do not just provide an external skills library; their real power is in their ability to map those skills to the internal workforce. They do this by ingesting data from a wide variety of internal and external sources to paint a detailed, holistic picture of what an individual employee is capable of. This data can include the employee’s official resume, their project history from project management tools, their contributions to code repositories, their self-reported skills, and even their public social media profiles. The AI then “infers” a skill profile for each employee, identifying capabilities they may have that are not listed in their official job description. This is how an organization “discovers” the hidden talent it already has.
The Power of a Pre-Built, Continuously Updated Library
The most immediate benefit of the “buy” approach is access to a pre-built, comprehensive skills library. Building a list of thousands of skills and writing behavioral rubrics for each is a monumental task. A vendor solution provides this on day one. This library has been curated by experts and validated across hundreds of companies, ensuring it is robust and follows best practices. This not only saves thousands of hours of work but also provides a higher-quality, more objective foundation. The vendor’s AI team is constantly scanning the market, ensuring that the taxonomy stays relevant as new technologies and products are released, which is a full-time job that no individual HR department could ever hope to manage.
Integrating Taxonomies with Learning and Talent Systems
These skills platforms are not designed to be standalone tools. They are designed to be the “central hub” for skills, integrating with all other talent systems. This is where their true power is unleashed. The skills taxonomy is “plumbed” into the Learning Management System (LMS). When an employee’s skill assessment identifies a “gap” in “Python Programming (Level 1),” the system can automatically recommend the precise training courses needed to close that gap. The taxonomy is also integrated with the recruiting platform, allowing recruiters to search for candidates based on granular skills, not just job titles. This integration makes the skills data actionable, connecting the “skill” to the “solution” (the training or the job).
Challenges of the “Buy” Approach: Cost, Complexity, and Fit
This approach is not without its own challenges, as the source material notes. The solutions available on the market can be problematic. They are often expensive, requiring a significant and ongoing subscription fee. Some systems can be overwhelmingly complex, providing a massive, all-encompassing ontology when the organization just needs a simple, manageable taxonomy. Conversely, some solutions can be too simple, offering a rigid, generic taxonomy that does not fit the company’s unique culture or specialized needs. The platform’s AI-inferred skills for an employee might also be inaccurate, creating a need for human validation. Choosing the right vendor is a difficult process of balancing features, cost, and flexibility.
Choosing a Partner: Finding the Right Solution
The key to a successful “buy” strategy is finding the right partner. An organization must evaluate potential solutions based on several factors. How comprehensive and well-structured is their skills library? Does it cover the niche, industry-specific skills that are critical to the business? How “open” is the system? Can the organization customize the taxonomy, adding its own unique skills or modifying definitions, or is it a “locked box”? How well does it integrate with the company’s existing HCM and LMS platforms? Choosing the right vendor is a long-term commitment, so this due diligence is essential.
The Hybrid Model: Buying a Foundation to Build Upon
For many organizations, the best solution is a “hybrid” model. This involves “buying” a foundational, AI-powered skills ontology from a vendor, but then “building” on top of it. The organization gets the benefit of the vendor’s massive, pre-built, and auto-updating library, which saves them 90% of the work. But the platform also gives them the tools to customize it. The internal stakeholder team can then focus their limited time on the 10% that really matters: adding the company’s unique, proprietary skills and refining the definitions and proficiency levels to perfectly match their own culture and language. This hybrid approach balances the speed and power of the “buy” model with the custom fit of the “build” model.
The Foundation for Modern Talent Management
A skills taxonomy, whether built or bought, is not an end in itself. It is a foundational tool, an engine. Its value is not in its existence, but in its application. Once implemented and integrated, a skills taxonomy provides the central nervous system for an entire talent management strategy. It provides a structured, data-driven approach to managing skills, ensuring clarity, consistency, and efficiency in all talent processes. By addressing skill gaps, streamlining learning, and enhancing talent management, a taxonomy is what allows an organization to stay competitive, achieve its strategic goals, and truly unlock the potential of its people. We will now explore the four key areas that are revolutionized by this new foundation.
Application 1: Revolutionizing Recruitment and Hiring
The first and most immediate application is in recruitment. Organizations without a taxonomy often write vague job descriptions based on old templates, leading to a poor “signal-to-noise” ratio in their applicant pool. A skills taxonomy transforms this process. Recruiters and hiring managers can now create precise job descriptions based on the standardized “skill profiles” mapped to each role. They can specify not just the skill (e.g., “Python Programming”) but the exact proficiency level required (e.g., “Level 3 – Advanced”). This clarity helps attract candidates with the specified skill sets and immediately filters out those who are unqualified. One recent study showed that a majority of HR professionals believe having a taxonomy helps decrease bias in job definitions, as it focuses on objective skills, not subjective proxies for talent.
Application 2: Building Targeted Training and Development
This is perhaps the most powerful application for talent development teams. Once the taxonomy is in place, the organization can finally see its skills gap in high definition. By assessing the workforce against the standardized framework, leaders can see exactly which skills are missing and where. This allows them to stop wasting money on fragmented, generic training programs. Instead, they can develop highly targeted training initiatives based on data. They can curate specific resources and group them into “learning paths” designed to move an employee from one proficiency level to the next. They can tailor these paths to individual employees’ needs and career aspirations, showing them a clear, step-by-step map for their growth within the company.
Application 3: Objectivity in Performance Management
Performance management has traditionally been one of the most subjective and challenging parts of a manager’s job. A skills taxonomy introduces a new level of objectivity and clarity. Managers can now assess their employees’ performance against the defined skill levels and behavioral rubrics. This allows them to provide concrete, constructive feedback. Instead of saying “you need to be better at communication,” a manager can now say, “You are consistently performing at Level 2 in communication. To get to Level 3, let’s focus on the behavior of ‘structuring and delivering a clear presentation to a small group.’ Here is a learning path to help you build that skill.” This constructive, skill-focused approach makes the entire process more fair, transparent, and developmental.
Application 4: Strategic Succession and Workforce Planning
A skills taxonomy is a powerful tool for strategic, long-term workforce planning. By mapping the skills of the entire organization, leaders can identify high-potential employees for future leadership roles. They can see which employees have the “adjacent skills” that make them ideal candidates for promotion. This makes succession planning a proactive, data-driven process, not a reactive scramble when a key leader leaves. Furthermore, the taxonomy allows the organization to “future-proof” its workforce. The leadership team can add the future skills they know they will need in three years (e.g., “AI Governance”), map those skills to the current workforce, and see the exact size of the “future gap.” They can then start building the targeted reskilling programs to close that gap before it becomes a crisis.
The Hurdle of Implementation: Change Management
Despite the clear benefits, developing and implementing a skills taxonomy can present significant challenges. A taxonomy is not just a technical implementation; it is a cultural one. For this reason alone, many talent development teams opt to work with partners or companies that can provide a satisfactory solution. But whether it is developed in-house or outsourced, its value must be communicated to the workforce. This kind of change can be hard for some. Employees and managers may be resistant to new standards or processes. They may fear that their own skills will be devalued or that this is just another “HR initiative” that will create more work. Establishing this new standard will require a robust change management plan, clear communication, and transparency to settle these fears.
The Challenge of Maintenance: A Living, Breathing System
The second major hurdle is maintenance. A skills taxonomy is not a “set it and forget it” project. It is a living, breathing system that must be constantly updated to remain relevant. As one prominent HR research analyst said, we are all in a world of continuous reskilling. As the company changes, as the marketplace changes, as the technology changes, and as the products you offer change, you have to reskill people on a regular basis. Sometimes, you need a lot of new skills because a job is going away. The skills ontology or taxonomy is intended to organize this. This means the taxonomy team must be in a constant state of evaluation, scanning the market for new skills, retiring old ones, and ensuring the framework continues to reflect the organization’s strategic needs.
Why Maintenance is the Top Reason to Buy Rather Than Build
In the contemporary business landscape, organizations face a fundamental decision when it comes to implementing essential systems and frameworks: should they build custom solutions internally or purchase ready-made products from specialized vendors? This question becomes particularly critical when dealing with complex, data-intensive systems that require constant attention and updates. While many factors influence this decision, including cost, customization needs, and implementation timelines, one consideration stands above all others in importance: the ongoing maintenance burden that comes with any sophisticated system.
The reality is that building a system is just the beginning of a much longer journey. What many organizations fail to fully appreciate during the initial planning phases is that the creation of a system represents only a fraction of its total lifecycle cost and effort. The real challenge lies not in the initial development but in the perpetual maintenance, updates, and improvements required to keep that system relevant, accurate, and valuable over time. This maintenance imperative has emerged as the single most compelling reason why organizations across industries are increasingly choosing to purchase solutions from specialized vendors rather than attempting to develop and maintain them internally.
The Hidden Complexity of Ongoing Maintenance
When organizations embark on building internal systems, they often focus their planning and resource allocation on the development phase. Teams are assembled, requirements are gathered, architectures are designed, and code is written. There is a tangible sense of progress and accomplishment as the system takes shape and eventually goes live. However, what frequently catches organizations off guard is the realization that launching the system is not the finish line but rather the starting point of an entirely different race.
Maintenance encompasses far more than simply fixing bugs or addressing occasional technical issues. In dynamic systems that must reflect real-world information, maintenance means constantly monitoring changes in the external environment, evaluating how those changes affect the system’s data and algorithms, researching and validating new information, implementing updates, testing those updates to ensure they don’t break existing functionality, and deploying changes in a way that minimizes disruption to users. This cycle never ends. As soon as one update is deployed, the environment has already changed again, creating the need for additional updates.
The complexity of this maintenance challenge grows exponentially with the scope and sophistication of the system. A simple application with limited data might require only occasional updates. However, systems that must track thousands or tens of thousands of discrete data points, each of which can change independently and at different rates, present an entirely different magnitude of challenge. The resources required to maintain such systems often dwarf the resources that were needed to build them in the first place.
The Skills Taxonomy Challenge
Perhaps nowhere is the maintenance challenge more evident than in the realm of skills taxonomies. Organizations today recognize that understanding the skills within their workforce is critical to strategic planning, talent development, recruitment, and competitive advantage. A skills taxonomy serves as the foundational framework that defines and categorizes the various competencies, capabilities, and expertise areas that exist within an industry or organization. This framework enables organizations to map employee capabilities, identify skill gaps, plan training initiatives, and make informed decisions about hiring and internal mobility.
However, maintaining an accurate and comprehensive skills taxonomy is an extraordinarily demanding undertaking. The modern workplace encompasses thousands of distinct skills spanning technical competencies, soft skills, industry-specific knowledge, tool proficiencies, methodological approaches, and emerging capabilities. Each of these skills exists within a constantly evolving landscape. New technologies emerge and create demand for entirely new skill sets. Existing skills evolve as tools and methodologies mature. Some skills become obsolete as industries shift and technologies are deprecated. The terminology used to describe skills changes as industry language evolves.
Consider the technical skills landscape alone. A decade ago, skills related to cloud computing were relatively nascent and limited to a handful of platforms and tools. Today, cloud skills encompass hundreds of specific competencies across multiple platforms, each with its own ecosystem of services, tools, and best practices. Skills related to artificial intelligence and machine learning have exploded from a narrow academic specialty into a vast array of practical competencies spanning multiple frameworks, approaches, and application domains. The same pattern repeats across virtually every technical domain: what was once a single skill category fractures into dozens or hundreds of specific competencies.
The Research Burden
Keeping a skills taxonomy current requires systematic, ongoing research across multiple fronts. Someone must continuously monitor job postings to identify which skills employers are seeking and how they are describing those skills. This involves not just reading individual postings but analyzing patterns across thousands or millions of job listings to distinguish genuine trends from noise. Are employers really starting to prioritize a particular new technology, or is the apparent increase in mentions simply due to a few large hiring campaigns that will soon end?
Research must also extend to professional development offerings. Training providers, educational institutions, and certification bodies constantly introduce new programs and credentials. These offerings provide important signals about which skills are valued in the marketplace and how skills are being categorized and taught. Monitoring these sources helps ensure that a skills taxonomy reflects not just employer demand but also how professionals themselves are thinking about and developing their capabilities.
Industry publications, conference proceedings, and professional communities represent another essential research stream. These sources reveal emerging technologies and methodologies before they show up in significant numbers of job postings. They provide insight into how practitioners are discussing and categorizing their work. They highlight skills that are gaining or losing relevance within specific professional communities.
Academic research adds yet another dimension, particularly for cutting-edge technical skills and theoretical frameworks. University programs and research publications often introduce terminology and conceptual frameworks that eventually migrate into mainstream professional practice. Tracking these academic sources helps ensure that a skills taxonomy includes the language and categories that will become standard in the future.
All of this research must be not just collected but analyzed, synthesized, and translated into actionable updates to the taxonomy. Researchers must evaluate whether apparent trends are genuine or ephemeral, determine how new skills relate to existing categories, decide how to incorporate evolving terminology while maintaining consistency, and validate that changes improve rather than degrade the taxonomy’s usefulness.
The Data Quality Challenge
Beyond research, maintaining a skills taxonomy requires rigorous attention to data quality. Each skill in the taxonomy must be clearly defined, properly categorized, and correctly linked to related skills. Definitions must be precise enough to enable accurate assessment and matching while being flexible enough to accommodate the natural variation in how skills are described and practiced across different contexts.
Maintaining data quality becomes exponentially more difficult as the taxonomy grows. With thousands of skills, even a small error rate translates into hundreds of problematic entries that can confuse users and degrade system performance. Skills may be duplicated under different names. Related skills may not be properly connected. Definitions may become outdated as the skills themselves evolve. Categories may no longer reflect the most logical organization of the skill landscape.
Ensuring data quality requires ongoing auditing processes, validation against external sources, user feedback mechanisms, and systematic review cycles. Organizations must establish clear standards for how skills are defined and categorized, implement quality control processes to catch errors before they propagate through the system, and continuously refine those standards as they learn from experience. This work demands not just domain expertise but also a deep understanding of data management principles and attention to detail.
The Team Requirement
Maintaining a comprehensive skills taxonomy is not a task that can be assigned to one person or handled as a side project by team members with other primary responsibilities. The breadth of skills that must be covered, the multiple research streams that must be monitored, and the technical complexity of implementing updates all require a dedicated team with diverse expertise.
At minimum, such a team needs data analysts who can process large volumes of information and identify meaningful patterns, subject matter experts who understand the various skill domains being covered, researchers who can systematically monitor relevant information sources, data architects who can design and maintain the taxonomy structure, engineers who can implement technical updates and ensure system performance, quality assurance specialists who can validate changes and catch errors, and project managers who can coordinate all these efforts and ensure continuous progress.
For a truly comprehensive skills taxonomy covering an entire industry or spanning multiple industries, the team might need to include dozens of professionals, each bringing specialized expertise. The cost of maintaining such a team represents a substantial ongoing investment that many organizations find difficult to justify, particularly when skills taxonomy maintenance is not their core business.
The Vendor Alternative
This is precisely where specialized vendors provide compelling value. Companies that focus specifically on skills taxonomies and related workforce intelligence make the maintenance of comprehensive, current skills data their entire business model. These vendors employ dedicated teams whose sole purpose is to keep skills information accurate and up to date. They have developed sophisticated processes and tools specifically designed to identify skill trends, validate information, and implement updates efficiently.
These specialized providers typically maintain what they call a global skills cloud or similar construct: a comprehensive database of skills information that is continuously updated based on ongoing research and analysis. This database represents an enormous investment in research infrastructure, data collection, analysis tools, and domain expertise. The vendor amortizes this investment across their entire customer base, making it economically viable to maintain a level of comprehensiveness and currency that would be prohibitively expensive for any individual organization to achieve independently.
When an organization purchases access to such a solution through a subscription model, they are essentially outsourcing the entire maintenance burden. The vendor takes responsibility for monitoring the skills landscape, identifying changes and trends, researching new skills and technologies, validating information quality, updating definitions and categories, maintaining system performance, and continuously improving the taxonomy based on collective insights from across their customer base.
The Economic Logic
The economic logic of the buy-versus-build decision becomes clear when viewed through the lens of maintenance requirements. Building an initial skills taxonomy might require a significant but finite investment. Perhaps an organization dedicates a small team for six months or a year to research, design, and implement their taxonomy. The cost is substantial but manageable, and the organization ends up with a custom framework tailored to their specific needs.
However, once that initial taxonomy is built, the maintenance clock starts ticking immediately. The organization must now commit resources indefinitely to keep that taxonomy current. They need to maintain a team, albeit perhaps smaller than the build team, to handle ongoing research and updates. They need to allocate budget for tools and data sources. They need to establish processes and governance. These costs recur year after year, and they are largely independent of how much the organization actually uses the taxonomy. Whether they use it extensively or sporadically, the maintenance burden remains.
Compare this to the subscription model. The organization pays a recurring fee, but that fee purchases access to a constantly maintained and updated system. The vendor has already made the massive initial investment in building the taxonomy and has organized their entire business around maintaining and improving it. The per-customer cost of maintenance is relatively low because it is spread across many customers, but each customer benefits from the vendor’s full investment in quality and currency.
Furthermore, the vendor benefits from network effects that individual organizations cannot replicate. As the vendor works with more customers across different industries and geographies, they gain broader visibility into skills trends. They can identify patterns more quickly and with greater confidence. They can leverage insights from one sector to improve their taxonomy in other sectors. This collective intelligence makes the vendor’s taxonomy more comprehensive and accurate than what any single organization could develop independently.
The Expertise Advantage
Beyond the economic calculation, specialized vendors bring a level of focused expertise that is difficult to replicate internally. For a vendor whose entire business depends on the quality of their skills taxonomy, there is enormous incentive to become truly expert in every aspect of skills data. They invest in developing sophisticated methodologies for skills research. They build advanced tools for processing and analyzing skills information at scale. They cultivate relationships with data providers and research organizations. They hire specialists who dedicate their careers to understanding skills taxonomies.
An in-house team, by contrast, must typically balance skills taxonomy work against many other priorities. Even if team members are highly skilled and dedicated, they simply cannot develop the same depth of specialized expertise as a team that focuses exclusively on this domain. The internal team is learning as they go, developing processes from scratch, and building tools that may work but are unlikely to match the sophistication of purpose-built commercial solutions.
This expertise gap manifests in numerous ways. The vendor’s taxonomy is likely to be more comprehensive, covering more skills in greater detail. Their categorization is likely to be more sophisticated, reflecting years of refinement and learning. Their processes for identifying emerging skills and deprecating obsolete ones are likely to be more systematic and responsive. Their data quality is likely to be higher, with fewer errors and inconsistencies.
The Scalability Factor
Maintenance challenges scale differently than build challenges. Building a skills taxonomy of 500 skills versus 5,000 skills might require ten times more initial effort, but that relationship is relatively linear. Maintenance, however, scales more than linearly. A taxonomy with ten times more skills likely requires more than ten times the maintenance effort because the interactions between skills become more complex, the potential for errors and inconsistencies grows exponentially, and the research burden increases both in volume and complexity.
Specialized vendors achieve scalability in maintenance through purpose-built infrastructure and processes. They have automated systems for monitoring skills trends across multiple data sources. They use machine learning to identify potential updates and flag anomalies. They have established workflows that efficiently route different types of updates to appropriate specialists. They maintain comprehensive testing environments to validate changes before deployment. These capabilities allow them to maintain very large taxonomies without proportionally scaling their teams.
An organization attempting to maintain a comprehensive skills taxonomy internally would struggle to achieve similar scalability. As their taxonomy grows, they would need to continuously expand their team and invest in better tools and processes. The cost curve would be steep, and they would constantly be playing catch-up, implementing capabilities that specialized vendors developed years ago.
The Innovation Dimension
Skills taxonomies are not static artifacts but rather living systems that should continuously evolve and improve. Beyond simply adding new skills and updating existing ones, there is ongoing innovation in how skills are structured, how relationships between skills are represented, how skills are measured and assessed, and how skills data is applied to various use cases.
Vendors in this space are continuously innovating because their competitive position depends on offering superior capabilities. They invest in research and development to enhance their taxonomies and the systems built around them. They experiment with new data sources and analytical techniques. They respond to customer feedback by developing new features and capabilities. This continuous innovation means that customers benefit not just from maintenance of existing capabilities but from ongoing enhancement of the entire solution.
An internal team, focused primarily on keeping the taxonomy current with basic updates, typically has little bandwidth for innovation. They are in reactive mode, responding to changes in the skills landscape rather than proactively developing new capabilities. The gap between what they can deliver and what specialized vendors offer tends to widen over time as vendors accumulate innovations year after year.
The Risk Mitigation Angle
Maintaining a skills taxonomy internally also creates organizational risk. What happens if key team members leave? If the skills taxonomy is a custom-built system maintained by a small team, the departure of one or two people could cripple the organization’s ability to keep it current. Knowledge may be concentrated in individuals rather than embedded in robust processes and documentation. Specialized expertise may be difficult to replace through normal hiring channels.
With a vendor solution, this risk is distributed across the vendor’s organization. The vendor has multiple people with expertise in every aspect of taxonomy maintenance. They have documented processes and institutional knowledge. If an employee leaves, the vendor has both the capability and the incentive to backfill that position quickly to maintain service quality. The customer organization is insulated from these human capital risks.
There is also the risk of technical obsolescence. Skills taxonomies sit atop technology infrastructure that itself evolves over time. APIs change, databases are upgraded, security standards evolve, and performance expectations increase. Maintaining the technical infrastructure of a skills taxonomy system adds yet another dimension to the maintenance burden. Specialized vendors must keep their technical infrastructure current to remain competitive, and customers benefit from these ongoing technical investments without having to make them directly.
The Focus Imperative
Perhaps most fundamentally, the decision to buy rather than build reflects a strategic choice about where to focus organizational resources and attention. Every organization has limited capacity for major initiatives. Time and talent invested in building and maintaining a skills taxonomy is time and talent not invested in other priorities that may be more central to the organization’s mission and competitive advantage.
For most organizations, skills taxonomy is an enabling capability rather than a core competency. It is important and valuable, but it is not what differentiates them in their market or defines their unique value proposition. These organizations are better served by purchasing high-quality skills taxonomy solutions from specialists and focusing their own internal talent and resources on the activities that truly define their competitive position.
A manufacturing company should focus on manufacturing excellence. A healthcare provider should focus on patient care quality. A financial services firm should focus on financial products and customer service. None of these organizations becomes more competitive by building internal expertise in skills taxonomy maintenance. They become more competitive by having access to excellent skills data that helps them develop their workforce more effectively, but that access is most efficiently obtained through partnership with specialized providers.
The Integration Consideration
Modern skills taxonomies do not exist in isolation but must integrate with multiple other systems: applicant tracking systems, learning management systems, performance management platforms, workforce planning tools, and various analytics and reporting systems. Maintaining these integrations adds yet another layer to the maintenance burden.
Commercial skills taxonomy providers develop and maintain standard integrations with common enterprise systems. They provide APIs and documentation that facilitate custom integrations. They have expertise in how skills data is used across different types of systems and can advise on integration best practices. When integrated systems are updated or changed, the vendor can update their side of the integration, reducing the burden on the customer organization.
With a custom-built internal taxonomy, the organization must develop and maintain all integrations themselves. As the landscape of enterprise systems evolves and the organization adopts new tools, they must continuously build new integrations and update existing ones. This integration maintenance work can easily consume as much effort as maintaining the taxonomy itself.
Conclusion
A skills taxonomy provides the structured approach that modern organizations need to manage their most valuable asset: their people. It provides clarity, consistency,s and efficiency in all talent management processes. In a world defined by a persistent skills gap and the need for constant adaptation, a taxonomy is the map that allows an organization to navigate. It helps address skill gaps by identifying them, streamlines learning by creating clear paths, and enhances talent management by providing an objective, data-driven foundation for decisions. As we face the next era of technological change, continuous learning and development will be the key to remaining adaptable. Leveraging a skills taxonomy is the first and most critical step for organizations to embrace this change, drive growth, and unlock their full human potential.