Beyond the Basics: Exploring the Meaning & Impact of Skill Proficiency

Skill proficiency frameworks enable organizations with deeper insights and improved workforce adaptability. These frameworks improve hiring, optimize talent development and inform strategic workforce planning by providing a common language and a clear structure for understanding skills and skill levels. 

You might think about skill proficiencies like turn-by-turn directions from a GPS. Instead of taking you to an unfamiliar address, these directions can help individuals navigate their careers and organizations navigate workforce upskilling and transformation. 

Traditionally, establishing and maintaining accurate and up-to-date skill proficiency frameworks was challenging. Now with continually updated data, a new framework for skill proficiencies that leverages artificial intelligence (AI) puts employers and workers in the driver’s seat. 

What are Skill Proficiencies? 

Job titles and degrees on a résumé don’t tell the whole story. What matters most are skills — what people can do and how well. 

A Skill Proficiency framework measures this, capturing the level of knowledge, expertise and ability an individual possesses in a particular skill. Like a ladder, skill proficiencies are typically measured on a scale, often from a 1 to “x” scale, with the higher number indicating higher skill. 

What are Skill Proficiency Frameworks? 

While skills provide a general snapshot of what people can do, organizations often need a more structured way to define and measure skills across their workforce, as well as to assess potential new employees and plan for changes in the workforce market.  

This is where skill proficiency frameworks come in. A skill proficiency framework provides a standardized language, definition and criteria for assessing and measuring employee skills. 

Skill frameworks are essential for strategic workforce planning, enabling organizations to thoroughly identify skill gaps, track employee growth, guide workforce transformation initiatives and increase talent mobility.

Drawbacks of Traditional Skill Frameworks 

While skill proficiency frameworks offer tremendous potential, not all frameworks are created equal. Traditional approaches can fall short. Here's why: 

• Lack of specificity and scope: Many frameworks lack detailed guidelines on proficiency levels for specific roles or cover just a narrow range of skills or occupations. This ambiguity can make it difficult to establish clear role requirements and benchmark performance. 

• Risk of outdated views: Traditional frameworks struggle to keep pace with the rapid evolution of technology, emerging skills and shifting market demands. This makes it difficult to adapt to the changing skill requirements. 

• Resource-intensive development: Developing and maintaining a comprehensive skills framework can be a significant undertaking. Perhaps this is why only 10% of HR executives say they effectively classify and organize skills into a skills taxonomy — although nearly all (85%) have some efforts underway, according to Deloitte.

Advancing Skill Proficiency Frameworks 

Advances in AI and machine learning are giving us new and better tools.  

When searching for richer skill insights, find a modern solution that incorporates skill proficiencies. One that is designed to use extensive, continuously updated datasets that offer a dynamic, real-time overview of the evolving skills landscape. 

For example, Pearson has pioneered the development of a patented Skill Proficiency Framework, the first of its kind, which enhances the Pearson Skills Ontology.

Drawing on data from millions of job postings, this ontology maps the intricate connections between skills, tasks, occupations and technologies. Updated in real-time, and now with the patented addition of a Skill Proficiency Framework, the Pearson ontology allows for a more granular understanding of skills and the levels within.  

Focusing on the proficiency framework, it classifies skills into five levels: Novice, Advanced Beginner, Competent, Proficient and Expert. By leveraging AI, Pearson generates detailed skill proficiency descriptions at scale and accurately maps them to a diverse range of occupations using job market data, ensuring they reflect the latest market trends. 

Pearson are the first to apply these cutting-edge techniques in this context, transforming how skill proficiency is described and linked to occupations. This effort has been formally recognized through publication in a leading data science conference and journal, CIKM – a Tier-A conference in Data Science, Data Management and Artificial intelligence.  

Overall, Pearson’s AI-powered Skill Proficiency Framework captures the nuance between different skill levels, offering a standardized approach to defining, measuring, and benchmarking performance in a skill. This framework opens the door to unprecedented insights and the potential to unlock unique opportunities.

Snapshot: Detailed Skill Proficiency Levels 

For example, consider Microsoft Excel. With mastery of several tasks or actions required for each level, advanced proficiency skill frameworks allow organizations to understand Excel skills with nuance and precision. 

Here’s an example of how Pearson details the various skill levels

Many Applications for Different Users 

Skill proficiency frameworks offer value to many people. 

Individuals and career advisors: 

• Workers and advisors can clearly understand their strengths and weaknesses, map out their career paths and spot opportunities for upskilling. 

• Frameworks help job seekers identify roles that match their skills and articulate their proficiency to potential employers.

Recruiters and hiring managers: 

• Recruiters can define precise skill requirements for roles, attract best-fit candidates and streamline the hiring process. 

• Frameworks use standardized criteria to evaluate candidates’ skills rather than subjective factors, increasing fairness and equity in hiring. 

Employers and HR professionals: 

• Frameworks help shape a comprehensive view of the organization’s skills inventory, identify future skill needs, and develop strategies to bridge skill gaps and support succession planning. 

• Managers can establish clear performance expectations, provide constructive feedback and track employee progress.

Education and training providers: 

• Providers can align training programs with industry standards and employer needs, ensuring graduates possess relevant skills and align to talent development paths.
• Frameworks can guide personalized learning pathways that cater to individual career goals.

Shape the Future of Work 

Modern skill proficiency frameworks, especially those powered by AI and robust datasets, offer a range of advantages for HR leaders, including: 

• In-depth, data-driven insights: Provides a deeper understanding of workforce skills and critical skill gaps to make informed decisions about talent acquisition, development and deployment. 

• Real-time, scalable adaptability: AI-powered frameworks that dynamically update with evolving skill demands ensure organizations stay ahead of the curve. 

• Objective measurement: Standardized and unbiased approaches to assess and benchmark employee skills move beyond subjective evaluations to a system that’s actionable, inclusive and bias-free. 

Navigate your workforce to the next level. Use a trusted skill proficiency framework to develop a workforce that drives growth and performance.