In today’s business environment, workforce planning is far more than a numbers game. It’s about placing the right people with the right skills into the right roles — both for today and for the future. So, how can organisations achieve this level of alignment and agility? The solution is to establish a robust, future-focused, skills-based job architecture.
At its core, a job architecture is a structured framework that defines job roles, job families, job levels, and the relationships between them. It’s the backbone of an organisation’s people strategy, providing clarity and consistency across everything from pay and progression to skills mapping and compliance.
However, in many organisations, job data is chaotic. Rapid growth, mergers, or a lack of governance often lead to a proliferation of job titles, inconsistent levels, and unclear career pathways. Without a solid job architecture in place, workforce planning becomes guesswork and organisations risk falling behind on everything from pay transparency and pay equity to compliance.
1. Clarity and Consistency
A well-designed job architecture brings order to chaos. By standardising job titles and defining clear job families and levels, organisations can ensure that their workforce data is consistent and comparable across departments. This consistency is crucial for accurate workforce analysis, enabling HR teams to spot trends in headcount, turnover, and diversity, and to make data-driven decisions about future recruitment needs.
2. Skills Mapping and Gap Analysis
Workforce planning shouldn’t be just about filling seats; there needs to also be a focus on building capabilities. A well-designed job architecture helps with this by linking every role to a skills taxonomy, making it easy to identify the skills most needed, pinpoint areas of skill shortages, and track how these needs evolve over time. This visibility over the entire skills landscape allows organisations to identify skills gaps, plan targeted development programs, and ensure they have the talent required to meet future business goals.
3. Pay Transparency and Competitive Pay Benchmarking
With pay transparency legislation increasing worldwide, organisations must be able to justify how they reward employees and how pay progression occurs as employees transition between job roles. A job architecture provides a framework for determining the value of different roles and benchmarking salaries against the external market. This not only supports compliance but also helps attract and retain top talent by ensuring pay is fair and competitive.
4. Enabling Internal Mobility and Career Pathways
A powerful benefit of a job architecture is its ability to show employees potential career pathways. When roles and skills are clearly defined, employees can see how they might move from one role to another, leveraging their transferable skills. According to LinkedIn’s 2020 Global Talent Trends study, companies with active internal career management programs have 41% higher retention rates. A job architecture is the foundation for these programs, making it easier for employees to navigate their careers and for organisations to support internal mobility.
5. Streamlining Compliance and Reporting
From pay equity to diversity reporting, regulatory requirements are growing more complex. A job architecture makes compliance reporting more straightforward and less error-prone by providing a single source of truth for job data. When new legislation is introduced, organisations can respond quickly and confidently, knowing their job data is accurate and up-to-date.
While the benefits are clear, building and maintaining a job architecture can be daunting, especially if done manually. Some organisations spend over a year on this task, only for the data to become outdated by the time they finish. That’s where technology solutions come in - leveraging the latest advances in AI and language technology, RoleMapper helps organisations cleanse, transform, and govern their job architecture efficiently, ensuring it stays current and relevant.
Explore how RoleMapper can help you build and maintain a future-proof job architecture to enhance strategic workforce planning
With many organisations increasing their focus on fairness, inclusion and employee engagement, it's understandable that pay transparency has become a key business issue; not to mention it is also being driven by a growing body of legislation mandating greater openness around pay.
From the EU Pay Transparency Directive to new laws emerging across various US states and cities, regulatory requirements are pushing employers to take a more structured and transparent approach to how pay is communicated and managed.
But does salary transparency actually drive positive change?
A range of studies provides compelling evidence of the positive effects of pay transparency in the workplace, highlighting a host of key benefits, ranging from reducing pay gaps to employee satisfaction.
Pay transparency is increasingly recognised as a powerful tool for closing wage gaps, particularly those based on gender and ethnicity. A 2021 Canadian study found that implementing pay transparency requirements can reduce these disparities by 20 - 40%. Similarly, a 2022 study published in Nature Human Behaviour showed that when organisations publicly disclose pay information, gender wage gaps tend to narrow significantly. By making salaries more visible, salary transparency empowers employees to identify inequities - creating social and organisational pressure for employers to address them.
Salary transparency also improves employee morale and builds trust between employees and employers. According to a Glassdoor survey, two-thirds (67%) of workers believe that pay transparency is beneficial for business, and a further 71% think that openness about pay is beneficial for employee satisfaction. A 2018 study analysed survey data from a diverse sample of employees and found that the amount of pay information shared by employers was positively related to both pay satisfaction and trust in management
When employees understand the “why” behind their salary and how to progress, it fosters a sense of fairness and clarity, even if the pay itself doesn’t change immediately.
In a competitive job market, candidates increasingly demand clear salary information before applying. Organisations that are upfront about pay are seen as more trustworthy and progressive, making them more attractive to skilled job seekers. Salary transparency can also streamline the recruitment process, reducing mismatched expectations and wasted time for both parties.
While pay transparency offers clear benefits, it also presents challenges. Without clear justification for differences in pay, it can expose uncomfortable truths and lead to frustration rather than trust. If organisations lack objective performance frameworks or fail to communicate pay decisions effectively, transparency can backfire, negatively impacting morale, retention, and even productivity. In some cases, it may also lead to wage compression, not by lifting lower salaries, but by limiting increases at the top.
The effectiveness of pay transparency depends on several factors:
The case for pay transparency is growing along with the legal requirements. When underpinned by fair pay structures, clear communication, and consistent practice, it can drive equity, trust, and performance.
We offer a range of pay transparency resources, including our guide, A Roadmap to Prepare for the EU Pay Transparency Directive.
We also run a weekly on-demand webinar, The EU Pay Transparency Directive: Impact & Roadmap, looks at:
Organisations face increasing pressure to attract, retain and develop top talent whilst maintaining fairness and transparency. With numerous changes and shifts occurring with global compliance requirements and the transition to skills, one of the most effective tools for achieving these goals is job levelling.
The benefits of job levelling are many and varied. Not only does it create a shared language for understanding roles across teams, business units and geographies, but also a structured and scalable process that defines and categorises roles based on factors such as level of responsibility, organisational impact and the skills required for the role.
A well-designed job levelling framework forms the foundation for fair and equitable pay. By assigning roles to specific levels or grades, based on objective criteria, organisations can ensure that employees performing work of equal or similar value are rewarded consistently, regardless of their department or function. This not only minimises any pay inequalities but also supports compliance with pay equity and pay transparency legislation, such as the upcoming EU Pay Transparency Directive. We know that when employees trust that pay decisions are fair and transparent, morale and engagement improve, whilst the risk of grievances or legal challenges decreases.
Job levelling provides employees with a clear roadmap for career progression. When each level in a framework is associated with defined skills, competencies, and responsibilities, individuals can more easily understand what is required to progress to the next stage of their career. This clarity is a powerful retention tool, as employees can see the steps needed for promotion and are more likely to remain with an organisation that invests in their development.
For HR teams, job levelling streamlines the management of job descriptions, performance metrics, and training needs. Standardised levels make it easier to benchmark roles internally and against the external market, supporting more informed decisions around recruitment, promotions, and pay.
Job levelling brings much-needed consistency to job titles, expectations, and responsibilities across the organisation. This uniformity helps managers and employees understand how different roles relate to each other and supports a common language for discussing work, performance and reward. Consistent frameworks also simplify decision making across recruitment, performance management and reward, reducing admin and the risk of arbitrary or biased decisions.
As organisations shift towards skills-based talent strategies, job levelling provides a structured way to define roles based on the skills and competencies required. This approach enables more agile workforce planning and helps organisations respond quickly to changing business needs by identifying and developing the right skills in their employees.
Job levelling creates greater organisational agility by giving leaders and HR teams visibility of the distribution of roles, skills and work being done across their organisation. These insights support succession planning and internal mobility, enabling proactive actions to address skills gaps and build a future-ready workforce.
As mentioned above, having a job levelling framework in place helps with workforce planning and determining appropriate pay levels. One outcome of this is that organisations are less likely to underpay or overpay for roles, reducing inefficiencies and risk.
A job levelling framework therefore supports more accurate budgeting, ensures pay equity across similar positions and improves financial predictability. It also prevents “title inflation,” which can unnecessarily drive up payroll costs over time.
Job levelling is more than just an HR exercise, it is a strategic enabler for fairness, transparency and organisational effectiveness. By establishing clear frameworks for pay, progression and talent management, organisations can create a culture of trust and opportunity, ensuring they are well-positioned to succeed in a rapidly evolving world of work.
Job levelling, sometimes known as job evaluation or classification, is a key process for ensuring pay transparency within organisations. By systematically categorising roles based on objective criteria, such as skills, responsibilities, effort and impact, job levelling provides a foundation for fair and equitable reward practices.
With the EU Pay Transparency Directive due to take effect in June 2026, job levelling has become a critical tool for compliance with new regulations aimed at eliminating pay disparities.
The aim of pay transparency is to create openness around how pay decisions are made and ensure employees understand the rationale behind their compensation. It helps address unjustified pay gaps and promotes fairness across the workforce. Pay transparency is also a good tool for employee retention, with research showing that companies with internal salary transparency have the lowest rates of employees planning to find new roles in the next 12 months. Workers at these companies also boast the highest levels of job satisfaction.
However, achieving true pay transparency requires a structured approach to evaluating roles, which is where job levelling comes in.
Issues can arise in an organisation when ways of determining pay vary between teams or business units. The same role (e.g. Project Manager) may exist in many different teams, but if different processes determine the pay for each of these roles, this can lead to pay inequities. Job levelling provides organisations with a framework to assess the relative value of each role systematically.
Using objective criteria such as skills, effort, responsibility, and working conditions to determine the level of a job ensures that pay decisions are based on legitimate factors rather than subjective biases or arbitrary classifications.
A structured, organisation-wide job levelling approach makes it easier to justify pay differences between roles or individuals and helps ensure that employees performing equal work or work of equal value are rewarded equitably.
Job levelling provides organisations with a framework to assess the relative value of each role, systematically
Without job levelling, organisations can be vulnerable to accusations of bias or non-compliance with legislation such as the EU Pay Transparency Directive.
Understanding the EU Pay Transparency Directive
The EU Pay Transparency Directive represents a significant step forward in tackling pay inequity. It aims to address the EU gender pay gap, which currently stands at 12%, although there is much variation between member states.
The key provisions of the Directive include:
The Directive states that organisations should use gender-neutral criteria to evaluate jobs and ensure transparency in pay structures. While it does not explicitly require job levelling frameworks, it strongly emphasises the need for structured approaches to categorising roles based on objective, gender-neutral factors.
The guidance refers to the fact that “where gender-neutral job evaluation and classification systems are used… they are effective in establishing a transparent pay system and are instrumental in ensuring that direct or indirect discrimination on grounds of sex is excluded”.
Other Benefits of Job Levelling
A well-implemented job levelling framework has other benefits beyond pay transparency compliance:
In summary, as global expectations around pay transparency continue to rise, investing in robust job levelling frameworks will not only ensure compliance with legislation but also yield significant organisational benefits. These include streamlining HR processes, enhancing employee retention by providing clear career progression opportunities and creating a culture of fairness and trust among employees.
To learn more about Job levelling and the EU Pay Transparency Directive, join one of our on-demand webinars, or download our guide, A Roadmap to the EU Pay Transparency Directive.
Job levelling, also referred to as job evaluation or job classification, is a structured process used by organisations to evaluate and categorise roles based on their responsibilities, required skills, and impact on the organisation.
Job levelling can be used to provide clarity around career progression, ensure equitable pay practices and align roles with organisational goals. From an employee perspective, it helps to make clear what is expected of them in a particular role and how their work and responsibilities fit into the broader company structure.
In this blog, we explore the benefits of job levelling, the different methods that can be used, some of the key levelling frameworks (such as WTW and Radford Aon) and a high-level approach to implementing a job levelling framework.
Job levelling is important for organisations for many reasons:
There are several different job levelling methods used by organisations, which range from very structured processes based on quantitative data to more informal, less structured systems that utilise qualitative data.
Here are some of the most common:
Ranking or job slotting
In this method, positions are directly assigned to predetermined grades or salary levels based on a quick comparison with benchmark positions. Job descriptions are compared to established role profiles and then placed in the most appropriate grade or level.
This method is faster and less resource-intensive than other job levelling methods, making it particularly useful for smaller organisations or when evaluating new positions. However, it can be less precise and more subjective than other levelling methods, potentially raising concerns about accuracy and fairness.
Job classification
Job classification is a more structured approach which involves systematically categorising positions into grades based on predefined criteria. In contrast to job slotting, it uses a more detailed analysis of job characteristics against established grade definitions.
This approach can produce greater consistency across similar roles, a clearer organisational structure, and standardised pay ranges.
The drawbacks are that implementation can be time-consuming, while the potential rigidity in level definition can make it a challenge to accommodate unique roles. It’s also a system which requires regular reviews to maintain relevance.
Factor comparison method
Factor comparison is a quantitative job levelling method that evaluates jobs by comparing them against factors or criteria (such as skills, effort, responsibility, and working conditions). It involves evaluating jobs on a factor-by-factor basis.
This is a more analytical and detailed job levelling approach which better supports pay equity and pay transparency. It’s also more effective for unique jobs because each role is considered individually.
The potential downsides are that factor comparison can be complex, time-consuming, and requires significant expertise to implement. It can also be expensive to maintain, and HR teams may face resistance due to its complexity.
Point factor method
The point factor method is essentially an evolution of the factor comparison method. It builds on factor comparison by assigning numerical points to factors. Each factor (such as skill, effort, responsibility) is broken down into levels, with specific points allocated to each level.
A questionnaire is developed so that points can be assigned for each factor for a job role. The points are then added up to produce a score. This score is then matched against the levelling structure to determine the job level. Each level has a predefined total score range, so the jobs are automatically sorted into levels via their total score.
This method allows organisations to adjust the relationship between points and pay more easily. The structured nature of this method provides greater objectivity and consistency in evaluations. It still requires significant time investment in developing and maintaining the point system and factor definitions.
This approach focuses on external data, using job descriptions to compare jobs to identical or similar positions in the external marketplace. Pay data is collected from published sources and the value of the position within the competitive market is determined.
This approach helps organisations to consider their positioning on compensation and is used by many companies to assess internal pay equity and the competitive value of individual positions.
Many job levelling frameworks have been developed – based on some of the methods outlined above - to help organisations with job levelling. Here are some of the key ones:
Radford (Aon) Job Levelling Framework
The Radford job levelling framework is a globally recognised system that categorises jobs into six levels: Entry (P1), Developing (P2), Career (P3), Advanced (P4), Expert (P5), and Principal (P6). These levels are applied across career tracks such as Professional, Support, Technical, and Managerial roles, allowing organisations to differentiate between individual contributors and managerial positions while maintaining internal equity and market alignment.
Radford evaluates and levels jobs using key factors including:
Willis Towers Watson Global Grading System
The Willis Towers Watson (WTW) Global Grading System (GGS) is a robust job levelling framework operates on a scale of up to 25 grades. The framework uses a two-step process: banding and grading. Banding places jobs within a hierarchical structure based on their contribution to the organisation, while grading evaluates roles against seven key factors:
Korn Ferry Hay Method
The Korn Ferry Hay Method is another widely used framework for evaluating and comparing job roles across organisations. It employs a point-factor methodology to assess jobs based on three core elements:
Each of these elements is scored using detailed charts, and the total score determines the job's level within an organisation's grading or levelling structure. The method also incorporates checks to ensure logical relationships between roles in hierarchical structures, such as comparing knowledge depth and management breadth between a role and its supervisor.
Implementing a job levelling framework can be a complex process. Here are the key steps you recommend you take:
RoleArchitect makes it easy for Managers to create new jobs aligned to your levelling and evaluation framework. It also automates your job levelling and evaluation methodology , aligns job description content to your levelling framework, and automates approval workflows to reduce time and help govern your levelling process.
For organisations looking to review job levelling for the EU Pay Transparency Directive, download our roadmap to compliance.
A skills-first approach provides solutions to several key organisational challenges, as emerging technologies and evolving customer behaviour are reshaping work.
As skills requirements change, organisations need to be more agile, able to redeploy employees where their skills are most effective, improve recruitment and retention practices, and align learning and development (L&D) with skills needs in a skills-first approach.
In this article, we’ll present the business case for a skills-first approach, supported by data showing a return on investment (ROI), and provide examples of brands that have successfully pioneered this strategy.
Understanding the importance of a skills-first approach in today’s workforce is crucial.
There are several key data points you can use to build your business case for a skills-first approach. Here are a few examples:
Workforce Planning
Mapping the distribution of skills requirements across your organisation provides you with a more informed view to support redeployment and development pathways into priority skills areas.
By deconstructing jobs and breaking them down into individual tasks and activities, it becomes easier to highlight the specific skills required for each task. In this way, alternative career paths can be created based on resources and directed towards areas where skills are most needed.
This skills-led organisational design enables a more flexible resourcing model that can better meet the needs of your service delivery requirements and adapt to changes in demand. It can also support a truly agile project management and delivery approach.
Creation of Career Paths
The CIPD estimates that the average rate of employee churn in the UK is 34%. A skills-first approach can address this issue through increased internal mobility.
33% of leavers resign due to career growth and development reasons, and these are often more highly skilled and ambitious employees who are more costly to replace.
A compelling business case data point is to look at your eNPS score and leaver data to show whether your employees feel that they have access to career opportunities or if it was a reason for leaving.
Providing career advancement opportunities improves retention, with a skills-based approach being a key factor. Adopting a skills-based approach to providing linear and lateral career pathing and work opportunities can save up to £1.5m in retention costs.
Identifying and defining skills proficiency requirements across jobs and levels provides a framework for career pathways, with employees able to see opportunities for linear and lateral career moves.
Workers who have made an internal move at their organisation after two years have a 75% chance of remaining there, compared to 56% for those who have not.
Skills-Based Hiring
Research has shown that hiring for skills is five times more predictive of job performance than hiring for education, and more than twice as predictive than hiring for work experience.
A compelling business case data point is to identify the number and cost of hires who left within 12 months. This is often a good indicator of inconsistencies in the assessment process – where an unstructured process not aligned to the specific skills requirement has been taken.
Indeed, research shows that employees leaving within 12 months is often due to a lack of structured assessment processes and poor hiring decisions.
A skills-first approach provides a more consistent and fair approach. Mapping skills enables the identification of priority skills proficiency requirements and is a better indicator for interviews and assessment. This enables a much more robust approach to hiring, which in turn reduces the cost of bad hires.
There is plenty of data to support the case for skills-based hiring:
Performance Management
Poor performance can cost organisations millions in lost productivity. The cost of poor management and lost productivity from disengaged U.S. employees is estimated to be between $960 billion and $1.2 trillion per year.
Performance issues can also have a damaging, ripple effect across your entire business through loss of productivity, widespread loss of motivation, and a decrease in customer satisfaction. Other employees may become disengaged and resentful, leading to an increase in absence and staff turnover.
Another compelling business case data point is to review the number of average and/or under performers you have in the business. Low performance leads to low productivity, which results in lower profitability.
A skills-based approach enhances performance management, making it more robust and effective for both performance and career conversations and allows these issues to be addressed directly. Mapping skills and the specific skills proficiency requirements for your roles provides the baseline for performance in role and framework to support performance management decisions.
Learning & Development
A centralised view of skills distribution enables targeted L&D to support future workforce needs. This in turn helps you optimise your L&D budget, ensuring a focus on addressing the most in-demand and skills gaps.
A skills-based approach identifies gaps, ensuring training is targeted effectively. By directly addressing employee needs, it can also be more effective, thanks to higher employee engagement.
As you build your business case, highlighting your L&D budget and any wastage can be a compelling data point.
Compensation, Pay Equity & Pay Transparency
Pay equity issues often occur when pay does not keep pace with the actual responsibilities and tasks of a specific role, or where certain types of skills are undervalued. By connecting skill proficiency and performance with pay, we can remove bias from compensation decisions, which in turn, helps to ensure pay equity.
In addition, a key requirement of the EU Pay Transparency Directive is to show pay progression criteria. This is effectively the need to show the skill proficiency criteria as you go up and down a grade.
Mapping skills and detailing the skill proficiencies provides clarity on requirements for performance and progression in role and satisfies reporting requirements for pay transparency legislation.
Diversity, Equity & Inclusion
A skills-first approach helps to remove bias. Making decisions about hiring, pay, promotions, succession, and deployment based on people’s skills rather than their job history, tenure in the job, or network reduces bias and improves fairness.
From a recruitment perspective, focusing on skills opens recruitment processes to a greater range of candidates and increases inclusivity. Indeed, one study estimates that organisations can increase their talent pool up to 20x with a skills-first approach.
The same LinkedIn study found that transparency around skills encouraged more women to apply for jobs. The increase in women applying was 1.8x that observed in men, with a similar impact on hiring outcomes.
84% of employees agree that skills-based hiring can help prevent bias in hiring, while 90% feel they are more likely to secure their dream job because of it. 81% say it has helped them gain access to new employment opportunities.
A skills-based approach can deliver a range of benefits for organisations. Improved hiring and retention can deliver a direct return on investment, leading to a more diverse workforce, while the greater agility and improved capabilities can directly impact business performance.
RoleMapper's Skills Innovation Partnership can fast-track the shift to skills by co-creating and building innovative AI and technology solutions to support people strategies and overcome process challenges.
In today’s competitive job market, retaining top talent is more challenging than ever, and well-defined career paths play a crucial role in keeping employees engaged and committed to their organisation.
Employee retention is one of the biggest challenges facing organisations today. High turnover disrupts productivity, increases hiring costs, and leads to the loss of valuable institutional knowledge.
There are several reasons for high staff turnover, such as working conditions, dissatisfaction with management, and pay, but lack of career advancement opportunities is a major factor.
According to LinkedIn, 93% of organizations are concerned with employee retention, yet many organisations are not addressing this issue by promoting internal mobility.
Just 15% of employees say their organisation encouraged them to move to a new role, while only 14% had been encouraged to build a new career development plan.
Employees want to know they have a future within their organisation, and internal mobility is key to this. When career progression feels uncertain or unattainable, they are more likely to look elsewhere for opportunities. CIPD research found that three in ten UK employees leave their roles within the first year, with a lack of career development being a key driver.
Career development is also linked to some of the other drivers of staff churn. For example, clearly mapped career paths help employees see a way to progress from current roles, easing concerns around pay.
Career pathing provides a roadmap, helping employees see how they can progress within the organisation, which increases motivation and reduces attrition.
After two years in a role, an employee who has made internal advancement is almost 20% more likely to stay with the organisation than one who hasn’t.
Career pathing plays a crucial role in employee engagement. It provides a roadmap for professional growth,helping employees see a clear future within the organisation. When employees know their potential career paths, they are likely to feel more motivated and committed to their roles.
Employees understand how their current role fits into their long-term career, increasing motivation and commitment.
A career path should show employees the demands and requirements of each role in the potential path, so they know the demands of the job and the progression criteria for moving from one job to the next.
This includes the scope, responsibilities, and requirements (such as knowledge, skills, and competencies). Employees should be aware of the types of skills required and the required proficiency for potential progression.
This feeds into career development, and the need to equip employees with new skills and knowledge that allows them to take on more responsibilities and engage in more rewarding work. It is also why employers see the provision of learning opportunities as the main method of increasing staff retention.
A strong career pathing strategy helps to create this culture of continuous learning. Employees who see growth opportunities are more likely to stay, reducing turnover. As a result, morale and job satisfaction improve. Employees also feel valued and invested in by their employer, strengthening their connection to the organisation.
A strong career pathing strategy doesn’t just benefit employees; it also helps organisations become more agile by enabling redeployment in response to changing business needs.
By mapping internal career pathways, companies can:
An organisation's skills strategy plays a vital role in career pathing by providing visibility into workforce skills, enabling the creation of more advanced career paths.
A skills framework provides a structured, data-driven approach to defining job roles, skill requirements, and career progression opportunities. By mapping skills and proficiency levels across different roles, organisations can create career pathways that align with business needs and employee aspirations.
Detailed skills data shows the type of skills and the level of proficiency for each skill, allowing organisations to map more sophisticated and dynamic career routes.
This enables vertical and lateral career paths and increases organisational resilience to change, as internal mobility can be based on skills rather than qualifications or tenure.
Implementing more advanced career pathing requires a solid foundation, with centralised and standardised job descriptions and job data, and a well-defined job architecture.
Many organisations struggle with fragmented job data, where job titles, descriptions, and progression opportunities lack consistency.
For more advanced career pathing, which identifies the full range of opportunities for internal mobility, a view of skills across an organisation, with detailed data on proficiencies and consistent description of skills, is vital.
Although some of this work can theoretically be done manually, it is resource-heavy and time-consuming. Technology accelerates the creation of skills frameworks, streamlines job description management, and strengthens career pathing.
With retention a key concern for many organisations, career pathing and employee development help organisations address key causes of staff turnover.
Career pathing also helps to build a more engaged, motivated, and skilled workforce, which is ready to adapt to change where required. Businesses can benefit through the retention of top talent, lower hiring costs, and improved stability.
RoleMapper's Skills Innovation Partnership can help enable the development of advanced career paths through greater visibility of skills.
An essential aspect of building a skills-first organisation is the ability to understand and manage the range of skills across a business.
Creating a skills framework enables organisations to identify, develop, and manage key skills in a structured way. It enables a view of skills across the organisation, with granular data allowing a detailed view of capabilities.
In this article, we’ll look at the six key elements that make up a skills framework - one that can be applied to a range of valuable use cases.
A skills framework is a structured system that defines, categorises, and maps the key skills needed for roles within an organisation or industry. It creates a common language for assessing, developing, and managing skills across various functions and levels.
It outlines core competencies, technical abilities, and skills (hard and soft), organised by proficiency levels.
The six elements described here make up a detailed skills framework. It’s the combination of more basic data such as skills tags and types, detailed data describing skills in detail and the level of proficiency for each skill that makes an effective framework.
1. Skills taxonomy
A skills taxonomy categorises and organises the skills required across an organisation. It provides a common language for defining and assessing workforce capabilities, aligning them with business goals.
This taxonomy can be created using skills inference and should be customised for the individual organisation so that the skills framework is directly aligned with specific business needs, relevant roles and strategic priorities.
A skills taxonomy provides a structure and vocabulary for describing skills and forms the foundation upon which the skills framework is built.
2. Skills Tags
A skills tag is an individual skill label that is inferred from your job and the requirements for this job. This may be data analysis, for example.
It is the unique identifier for a skill and enables a whole range of tracking, searching, reporting, and analysis.
3. Skills types and categories
Skills types are, for example, technical skills such as proficiency in programming language, or soft skills such as teamwork or communication. The skills types can encompass a wide range of hard and soft skills.
Categories of skills might be aligned with a specific job or job family and according to their position in the organisation. Having these types and categories defined aids grouping, data analysis and categorisation.
4. Skills proficiencies
With the addition of proficiencies, we can build a clearer picture of both the skills and their proficiency levels.
Proficiencies may be described as following, from basic to strategic:
5. Skills descriptors
Skills descriptors define the broad scope of the skills described. What is needed for a descriptor is a description of what the skill means, with the content consistent with the organisational tone of voice.
For example, the skills descriptor for ‘Databases’ could look like this:
Creating, organising, and managing electronic collection of data, using database management systems to store, retrieve, and analyse information efficiently.
6. Skills proficiency descriptors
These describe the proficiency of the skill and the level it is at, from a basic to a strategic level. This is more granular and, therefore, more valuable data which can be applied to a range of use cases.
To return to the ‘Databases’ skill, the proficiency descriptor could describe the level of each skill like this:
While a skills taxonomy enables some insight into the distribution of skills throughout an organisation, it’s the whole skills framework and the granular data contained within skills and skills proficiency descriptors that really make the difference.
This data not only describes the distribution of skills but also provides detailed proficiency levels, enabling a deeper understanding of skills within an organisation and enabling a range of valuable use cases.
For example, identifying and defining skills requirements to this level enables the creation of career paths, as you need to be specific about identifying the skills proficiency requirements when you look vertically and laterally at jobs to create a framework for career pathways.
The skills, and levels of skills, need to be described to bring the career path to life and outline the proficiency requirements for employees to make both linear and lateral moves.
With skills and proficiencies well defined, organisations are more able to identify skills requirements as the foundation for learning and development plans. This allows for optimisation of L&D through targeted academies or through a focus on specific skills.
Detailed skills data also enables performance management, as skill proficiency data provides clear criteria for progression and a framework to support performance management decisions.
A skills framework is the foundation for a skills-based approach because it provides a structured and standardised way to identify, define, and manage skills across an organisation.
Without a clear framework, businesses struggle to assess workforce capabilities, align talent with strategic goals, and create meaningful career development pathways.
By categorising skills and defining proficiency levels, a skills framework establishes a common language for understanding workforce potential, enabling better decision-making in hiring, learning and development, workforce planning, and performance management.
This structured approach ensures that skills can be recognised, developed, and applied effectively, rather than relying on traditional job titles or outdated role descriptions.
RoleMapper's Skills Innovation Partnership can fast-track the shift to skills by co-creating and building innovative AI and technology solutions to support people strategies and overcome process challenges.
In this article, we'll look at job evaluation for EU pay transparency, the different methods organisations can use and how to decide on the best approach.
With the EU Pay Transparency Directive due to become law across EU member states in 2026, job evaluation is a key process that enables organisations to systematically value roles based on objective criteria.
While job evaluation has been used for years, particularly in the public sector, it has fallen out of favour in the private sector, with many organisations having moved to a more flexible approach to jobs and pay.
The EU Pay Transparency Directive talks about the need to have pay structures in place based on job evaluation and classification systems that use 'objective, gender-neutral criteria'.
This EU legislation has now brought evaluation back into focus in the private sector, as it provides a systematic, objective framework to assess the relative worth of different jobs within an organisation.
Job evaluation and classification systems should ensure that any pay differences are based on legitimate job-related factors rather than bias or discrimination, helping organisations comply with pay transparency legislation and promote workplace fairness.
There are several different methods used by organisations to evaluate and classify jobs. They range from very structured processes based on quantitative data to more informal, less structured systems that utilise qualitative data.
This is one of the more simplified approaches to job evaluation, where positions are directly assigned to predetermined grades or salary levels based on a quick comparison with benchmark positions.
Rather than conducting detailed point-factor evaluations, job descriptions are compared to established role profiles and then placed in the most appropriate grade.
The benefit is that ranking is faster and less resource-intensive than other job evaluation methods, making it particularly useful for smaller organisations or when evaluating new positions.
However, it can be less precise and more subjective than comprehensive evaluation methods, which could lead to concerns about accuracy and fairness.
Job classification is a slightly more structured approach than ranking. It involves systematically categorising positions into grades based on predefined criteria.
Unlike job slotting's quick comparison approach, classification uses a more detailed analysis of job characteristics against established grade definitions.
The benefits of this approach include consistency across similar roles, the development of a clear organisational structure, and standardised pay ranges. This systematic approach makes it more defensible than job slotting.
However, it can be time-consuming to implement, and the potential rigidity in grade definition can make it difficult to accommodate unique roles. It’s also a system that can become outdated if classifications aren't regularly reviewed.
This is a quantitative job evaluation method that evaluates jobs by comparing them against factors or criteria (such as skill, effort, responsibility, and working conditions). Unlike job classification or slotting, it involves evaluating jobs on a factor-by-factor basis.
The main benefits of this approach are that it is highly analytical and that this more detailed job comparison approach better supports pay equity and transparency. This method is more effective than the ones previously described for unique jobs, as each role is considered individually.
The potential downsides to this approach are that it can be complex and time-consuming and that it requires significant expertise to implement. It can be expensive to maintain and may face internal resistance due to its complexity.
The point factor method is an evolution of the factor comparison method. It builds on factor comparison by assigning numerical points to factors. Each factor (such as skill, effort, responsibility) is broken down into levels, with specific points allocated to each level. A questionnaire is then developed so that points can be assigned to each factor for a job role.
These points are then totalled into a score, which is then matched against the levelling structure to determine the job level. Each level has a predefined score range, so the jobs are automatically sorted into levels via their total score.
The point factor method allows organisations to adjust the relationship between points and pay more easily. The structured nature of this method provides greater objectivity and consistency in evaluations. However, as with the factor comparison method, it still requires the investment of significant time in developing and maintaining the point system and factor definitions.
Documentation provided by the EU and International Labour Office suggest that the more analytical methods have the potential to be less discriminatory due to their systematic and complex approach, and therefore more appropriate for job evaluation when it comes to gender equality.
However, many organisations in the private sector have been moving away from very structured job evaluation approaches - they don’t always want to work with a rigid and complex point methodology and so opt for ranking/levelling or classification instead.
Indeed, the Deloitte/Empsight 2024 global job architecture practices survey report showed that 84% of companies use job evaluation, but only 18% of companies use point-factor job evaluation.
The EU Pay Transparency Directive talks about pay structures based on job evaluation and classification systems that use 'objective, gender-neutral criteria'.
However, the steer from the EU in terms of pay transparency is that a structured job evaluation is recommended, which means that many companies with employees in the EU may need to assess what they are doing in light of the EU Directive.
The challenge for organisations is to determine a job evaluation approach that meets the requirements of the legislation whilst still retaining the flexibility they require.
They also need to ensure that the criteria used in the job evaluation approach that they adopt uses factors or criteria that are in line with the recommendations in the EU Directive.
The main concerns around job evaluation are that it is time-consuming and that there are ways of ‘gaming’ the system. It tries to reduce jobs to a mathematical formula for comparison, but, as with all data, the inputs need to be accurate for the outputs to be correct.
Unconscious bias can creep in at any point, so it’s important to remember that, although it tries to be systematic and consistent, it is not scientific. Job evaluation works best when representatives from across the organisation are involved so that there is general agreement and buy-in.
It is also necessary to ensure that bias isn’t accidentally included when choosing the level and type of skills to include. The directive specifically states that ‘relevant soft skills shall not be undervalued.’ While the level of some skills can be determined by the length of time it took to acquire them, others need to be evaluated more holistically.
While the EU doesn’t prescribe a specific job evaluation method, it does give a strong steer towards a more analytical approach in its working document.
“The analytical job evaluation methods, being systematic and complex, have the potential of being less discriminatory than non-analytical methods and they are therefore considered to be most appropriate for job evaluation in a gender equality context.”
Methods of job evaluation for EU pay transparency should, therefore, be guided by this advice and the need to have a robust process in place.
On-demand webinar: Join RoleMapper CEO Sara Hill and Vicky Peakman, Director, Fair Pay Partners, as they talk through the EU Directive requirements, drill into the operational implications, and set out practical steps for preparation.
In this article, we’ll describe what skills inference can do, and how it can enable the building of skills frameworks, speeding the adoption of a skills-first approach.
A critical component of building a skills-first organisation is the ability to understand, manage, and monitor the skills within your workforce.
Ultimately, organisations need to know the spread of skills across their workforce, common definition and language to understand and describe those skills, and they need to know the proficiency levels of skills within the business. .
With a skills framework that can provide this level of insight, organisations are able to put a number of use cases into action. These include mapping career paths, improving training and performance management, hiring more effectively, and tracking pay progression data for pay transparency legislation.
The challenge for many organisations in reaching this stage has been that surfacing skills from a range of sources, often spread around different teams and in varied formats, has proved to be a barrier. This is where skills inference can make the difference.
One of the biggest challenges for organisations has been the identification of existing skills across the business. 75% of organisations don’t have a centralised skills taxonomy which allows them to at least have a view of skills.
In addition, skills taxonomies are large data-sets of skills ‘tags’. They can provide information of the skills that exist, but lack the detail, such as proficiency, which is required for many use cases.
Another common barrier has been the significant resource, effort, and cost required to build out skills data and the level of descriptions required to support desired use-cases. This includes the complex review process needed to consolidate data from spreadsheets, and to gain review and approval from across the business.
For these reasons, organisations have often struggled to get skills initiatives off the ground, or have only partially achieved their goals.
Skills inference can enable organisations to overcome some of these barriers, reduce costs, and speed the process.
Skill Inference is a method of extracting skills from text, using natural language processing, and more specifically. semantic data analysis - the process of analysing data to extract meaning and insights.
The aim of skills inference is not necessarily to collect data, but to detect skills.
Job data is extracted and deconstructed into component parts, each of which has meaning and can inform the context of the skills.
These component parts are then processed by an NLP pipeline using Token Extraction and Named Entity Recognition techniques.
This job data is then processed to identify skill synonyms and skill concepts. From this the skills are mapped and the collection of skills is classified, with further processing to normalise and classify skills.
The skills data can now be enriched with category classification, so skills are classified according to their type - soft skill, technical, professional skill, and so on.
Classifications also include detecting the level of seniority, industry and occupation. This is classified against recognised industry standards such as ISCO, O*NET and SOC.
These inferred skills are now given a confidence score based on frequency analysis against our job model, co-occurrence patterns (interconnectivity) of groups of skills and historical usage data.
The key to effective skills inference is in the contextual validation and refinement of the data. It’s vital to interpret that skills data to make it meaningful.
Anyone can buy in a skills taxonomy and plug it into a set of data, but this is likely to leave you with an enormous amount of data which is neither meaningful or useful.
It’s important to have confidence that the skills inference has found good skills. This is a challenge, as the inference process is finding every skill it can, which means the initial result is a large data set of skills.
It is also easy to infer poor quality skills data, because the nuance is in understanding the full sentence and the context of the job. For example, some words in skills data, such as ‘support’ may have several applications with different meanings, from help desks to supporting business strategy.
Some of these will be very relevant but sometimes there are multiple variations of skills, meaning you may have some duplication and overlapping of skills - financial analysis and financial data analysis for example.
Skills data may also be too generic to be relevant or to add enough value to be truly useful.
Consistency is also vital. For example, job descriptions may be written in different ways across teams, with varying language used to describe jobs and skills.
This can mean there is inconsistency and a lack of standardisation of skills.
To potential issues around context, consistency, and standardisation, there is a process of training the data to decide which skills are right and wrong.
Skills inference is really sensitive to the way the content has been written, while not all job descriptions reference the actual skills required, this is assumed by the context of the job and its level.
What’s required is a benchmark showing what good looks like, so that the skills data you have surfaced can be tested and improved to produce valuable and accurate data.
For this, external data is used to benchmark the role and suggest additional skills based on other similar roles, or skills that are more relevant in the market.
This means you can be confident about the data, knowing that the skills data surfaced has been validated, compared against external benchmarks, and accurately describes the range of skills across the business.
RoleMapper's Skills Innovation Partnership can help to fast-track the shift to skills by co-creating and building innovative AI and technology solutions to support people strategy and overcome process challenges.
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