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.
The EU Pay Transparency Directive is due to enter law in the 27 EU member states from June 2026.
This may seem like plenty of time for organisations to prepare, but with the bill having potentially far-reaching operational implications, it’s important to get your house in order as soon as possible.
Our new guide, A Roadmap to Prepare for the EU Pay Transparency Directive, looks at the implications of the directive for businesses, and sets out the practical measures needed to prepare.
The European Parliament and Council adopted the EU Pay Transparency Directive in 2023, and all EU countries are required to adopt it into their national laws by 7th June 2026.
As a result, organisations with employees working in any EU member states will have to comply with the legislation, whether or not they are based in Europe.
Our guide sets out these practical steps in greater detail, but this checklist sets out some of the key steps organisations can follow to be ready when the EU Pay Transparency legislation comes into force.
Under the directive, employees have a right to request information about pay levels for groups of workers who perform what is deemed to be the same work, similar work, or work of equal value as them.
The implication of this is that organisations need to have a robust framework and mechanism for grouping and analysing jobs.
It is not just about grouping jobs into a job architecture or family structure. The EU Pay Transparency Directive makes provisions for employees to ask to see pay levels beyond a traditional job framework.
Organisations need to be able to look at job groupings in three ways:
Essentially, organisations must have a way to consolidate and compare jobs of equal value and be able to justify any differences in pay that may exist.
Organisations need a mechanism to value jobs which is objective and unbiased. The Directive recommends using a job evaluation methodology that can systematically value roles based on objective criteria.
Job evaluation (also known as job classification or job levelling) is a process used by companies to evaluate and categorise roles within the company based on a range of factors. These include the role’s level of responsibility, the skills and knowledge required, and complexity of tasks.
There are a number of job evaluation methods to consider, but the steer from the EU is that a structured job evaluation based on objective criteria is recommended. A more analytical method can be less discriminatory due to their systematic and complex approach.
The implication of the EU Pay Transparency Directive is that pay structures should be linked to the groups of jobs of equal work and equal value.
Full compliance with the EU Directive could potentially mean a major change for many organisations that they may not be fully aware of yet.
We would recommend that organisations review current methods for creating pay ranges and their implications as they relate to the EU Directive.
Overhauling how you price your jobs could potentially be a daunting task, so the first step we recommend is creating job groupings aligned to equal work and equal value, mapping pay data onto this and seeing the extent of your risk, in terms of pay equity.
Under the directive, organisations need to be able to share with employees the criteria they use to define pay levels and make pay decisions, specifically how job value is determined and the pay structure methodology. This essentially means a higher level of pay transparency.
Salary transparency has been shown to have many advantages, including staff retention and brand reputation.
Organisations need to consider the level of transparency they want, think about the core principles around pay, and how they communicate this to staff.
Employers need to be clear about why pay varies in the company. They need to be able to explain the criteria for pay progression as well as why current pay for one role differs from that of another.
To comply with the EU Directive, organisations need to have clear mechanisms in place to:
Organisations need to ensure that job titles and job postings are inclusive and gender neutral and that their recruitment processes are inclusive and not open to bias.
This has several implications:
While job descriptions aren’t mentioned specifically in the Directive, they form the foundational building blocks to operationalize many of the requirements.
For example, the information contained within form the basis of job evaluation, and the information required to justify any discrepancies in compensation between jobs.
For this reason, ensuring you have standardisation and governance over your job descriptions is essential.
To ensure that you can prepare for the directive, and be able to carry out on-going management, you need to have a robust approach to standardising, creating, and governing your job descriptions.
Every organisation within the EU will need to have a deep understanding of the pay equity situation of their job groupings.
First of all, this requires grouping jobs based on equal work and equal value, according to objective criteria.
Until you know which jobs are of equal value, and therefore in the same group, it will be challenging to run the reporting. For any organisation who does not have a consolidated view of the value of their work, it is highly recommended that you review your job evaluation so that you have enough time to analyse the pay for each group ahead of June 2026.
The EU Pay Transparency Directive is far more rigorous than any pay transparency legislation seen so far, with far-reaching operational implications across both compensation and talent management processes.
It’s about more than just displaying pay bands on job postings, the Directive requires companies to consider their compensation and talent management practices.
For any organisation with employees in the EU, there is now, more than ever, an urgency to get your house in order across key talent and compensation processes.
To help you prepare for EU Pay Transparency legislation, our new guide goes into detail on each of these steps.
A skills taxonomy forms a key step in the transition to a skills-based organisation, forming a part of a wider skills framework.
As market demands, technologies, and business strategies change, companies must continuously reskill their workforce. More than this, they need a structure that enables them to identify skills gaps and place talent where it is needed most.
WEF research found that, between now and 2027, businesses predict that 44% of workers’ core skills will be disrupted, simply because technology is moving faster than companies can design and scale up their training programmes.
This is why many companies are transitioning to become skills-first organisations, placing greater focus on understanding and developing the skills of each employee.
A skills taxonomy helps navigate this complexity by identifying transferable skills and gaps that need to be addressed, enabling companies to become more agile in response to changing business demands.
A key step towards a skills-first approach is knowing the skills that exist within an organisation. A skills taxonomy allows organisations to begin to understand the distribution of skills within the organisation.
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 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 consists of standardised tags showing, with skills customised according to an organisation's language and tone of voice.
The challenge for many organisations looking to bring in skills-first approach is that surfacing skills can be a time-consuming and complex process.
Technology can now shorten and improve this process through skills inference. AI-powered skills inference solution surfaces skills from job data such as job descriptions, job postings, and assessment data, validating and suggesting enhancements to skills data based on industry insights. This skills data is then used to create a customised skills taxonomy.
This skills taxonomy, with standardised skills tags, provides the ability to view and analyse the distribution of skills across an organisation, but it doesn’t provide the in-depth data required for many use cases, such as mapping out career paths or recruitment.
A wider skills framework is required for a more comprehensive skills approach. The skills framework comprises more granular data, such as skills types and categories, which define skills in more detail, and skills proficiencies so organisations can see the skills possessed by employees and the levels of each skill.
This skills framework provides the ability to view the range of skills across an organisation, and more importantly, detailed data which shows the category of skills and the level of competence for each employee.
It's this greater detail that then feeds into key business use cases, from targeted learning and development programmes based on identified skills gaps, greater workforce flexibility through knowledge of skills, as well as improved recruitment processes where candidates are targeted and assessed based on the skills required for the role, not just experience or qualifications.
A skills taxonomy provides the foundation for a resilient, future-ready workforce by offering clear insights into the distribution of skills. By building upon this taxonomy to create a comprehensive skills framework, organisations are able to put skills at the centre of their workforce strategy.
RoleMapper has launched the RoleMapper Skills Innovation Partnership to help fast-track the shift to skills by co-creating and building innovative AI and technology solutions to support people strategy and process challenges.
With a range of workforce challenges, budget constraints, skills shortages, and an aging workforce, greater use of AI in the public sector can deliver a range of benefits.
At the same time, people expect faster and more efficient services. With money still tight, the government has introduced the AI Opportunities Action Plan. which aims to use technology to streamline public services, ‘eliminate delays through improved data sharing’ and reduce costs.
The action plan includes the launch of a new package of AI tools for civil servants, training programmes for AI engineers and a proposed ‘experiment’, fund, to improve the use of digital tools across the public sector with the aim of making £45bn in productivity savings.
With this plan, the UK government has recognised AI’s potential in the public sector, and there is an opportunity to use AI in the public sector to address workforce challenges, and to improve efficiency and service delivery.
There are a range of challenges facing councils, many of which have been exacerbated by the pandemic.
AI and digital transformation can help local authorities tackle these issues head-on, enabling more efficient workforce planning and operations.
Some councils are currently experimenting with AI, but adoption so far is limited. An LGA survey carried out in February 2024 found that 85% of council respondents were using or exploring AI, with 52% at the beginning of their AI journey.
More advanced use was rare, with 16% developing capacity and capabilities around AI, 14% making some use of AI while 4% see themselves as innovative in their use of AI.
While the survey found that councils had discovered benefits in terms of productivity, service efficiencies, and cost savings, the use of AI is very much in its early stages.
One of the barriers to adoption cited by 41% of councils was a lack of understanding of the use cases for AI. It’s helpful to look at how AI can be used to address some common issues.
Recruitment, skills shortages, and DEI
Using AI to surface skills and identify current workforce capabilities enables councils to redeploy workers where they can be most effective, addressing skills needs with training to reduce costs associated with hiring, and helping councils to future-proof their workforce.
AI also enables councils to get their houses in order around job data and job descriptions with AI enabling the digitisation and centralisation of job data.
AI can also enable councils to automate the creation of inclusive, skills-focused job descriptions that can attract a wider talent pool, increasing the reach and appeal of roles in local government.
Funding
AI-driven automating of job descriptions saves councils time and costs by reducing HR admin, ensuring compliance, and improving hiring efficiency.
It enables accurate skills mapping, supports pay transparency, and minimises reliance on consultants.
At a time when finding for councils is scarce, faster, standardised creation of job descriptions help councils optimise workforce planning, reduce recruitment costs, and enhance service delivery with fewer resources.
Employee value proposition (EVP)
With staff retention a key challenge, AI-driven skills insights enable councils to create compelling career paths and training opportunities, making roles in local government more attractive to candidates looking for purpose-driven careers.
Workforce agility
A skills approach, using AI to identify key skills and capabilities, enables councils to identify transferable skills across departments, redeploying talent where it is most needed, and adapting job roles dynamically as demands change.
It also contributes to staff retention, as employees can be deployed where they can make best use of their skill sets.
The LGA identified several barriers to deploying AI in local government:
The UK government’s AI strategy aims to overcome these barriers through a structured approach to AI integration.
This includes building a secure and sustainable AI infrastructure, the piloting of new AI solutions before full implementation, and cooperation between the public and private sector through which innovative AI suppliers from the UK and around the world should be engaged to support demand and encourage investment.
The adoption of AI offers UK councils a way to overcome key challenges, from addressing skills shortages to reducing administrative burdens and improving service efficiency.
As councils navigate these workforce challenges, RoleMapper provides the foundation for transforming job data, managing organisational change, and enabling skills-based workforce planning.
Digital transformation is critical for delivering a 21st century service in the public sector. Across many local authorities, the current process for creating, organising and governing jobs is manual, inefficient, inaccurate, resource-intensive, and poor quality.
Jobs sit at the heart of delivering the changes required to support improvements in customer experience. How they are designed in the public sector is critical to harnessing talent and skills across organisations and systems, ensuring inclusion, accessing talent, developing people and planning for the future.
The way jobs and skills are currently organised and managed across many local authorities is a barrier to public sector digital transformation. So much so that, according to EY, “governments won’t be able to provide a 21st century citizen experience and better citizen outcomes with 20th century skills and working practices”.
AI has a huge role to play in digital transformation in the public sector, as underlined by the recent announcement of the government’s AI Opportunities Action Plan, which aims to use technology to drive efficiencies in the public sector.
The plan states that the public sector ‘should rapidly pilot and scale AI products and services…this will drive better experiences and outcomes for citizens and boost productivity.’
Traditional ways of working need to be improved, and there’s a need for greater digital transformation across the public sector. Those using public services have very different expectations about how they access services, compared to five or ten years ago.
This government initiative is potentially a significant step toward modernising the public sector and delivering more efficient local government services, and enabling public sector staff to focus on providing better service for the public through automation of admin tasks.
AI has the potential to transform how local authorities manage jobs and skills. Under this initiative, AI-powered tools will be deployed to streamline job descriptions and automate key processes, ensuring greater consistency across councils and public sector bodies.
An approach to jobs and skills using AI can enhance talent mobility, improve employee engagement, and ensure that councils can respond effectively to changing community needs.
However, this approach has to take into account the current state of the public sector. Data on jobs and skills can often be disorganised and inconsistent, created by different teams and stored in a range of formats.
Before councils and local authorities can implement AI for recruitment and a skills-driven approach to workforce planning, they must first establish a strong foundation through digital transformation, which includes the standardisation of job descriptions, the creation of a skills taxonomy, and the centralisation of jobs and skills content.
For some local authorities, there is also a need for data transformation to ensure data is digitised and accessible, as well as change management to equip HR teams with the knowledge and skills to use AI tools effectively.
For all local authorities, digital transformation and the use of AI to enhance and automate processes can bring a range of benefits.
Current systems require accurate job structures and job titles in place before implementation. The mistake many organisations make is simply loading in what exists already, which is likely to be outdated, rigid, and not fit for purpose. If jobs are not organised and up to date, this will hinder the value organisations can get from their technology investment.
AI can streamline the surfacing of skills within public sector organisations by automating skill identification, mapping, and analysis.
Skills inference using natural language processing (NLP) and machine learning can enable the surfacing of skills from sources such as job descriptions, employee profiles, and training records.
This enables local authorities to identify internal talent, match employees to new opportunities, and pinpoint skill gaps. By reducing manual effort and improving visibility, AI helps public sector organisations build a more agile, skills-based workforce.
For many platforms used by local authorities, the job structure powers the recruitment workflow. Without a clear structure in place for jobs, and centralised job descriptions, HR, Hiring Managers and Recruiters can waste a significant amount of time writing duplicate content or using out-of-date job descriptions that don’t accurately reflect the role.
Automation of the job description process creates greater efficiency, and ensures that job data is standardised and up to date, and more accurately reflects the skills needed for each role.
This centralisation and standardisation of job descriptions ensures that jobs remain up to date in terms of skills and responsibilities, enabling the public sector organisations to adapt more effectively.
With increasing pay equity legislation being introduced, along with the requirement to report on equitable pay practices, an accurate job framework is fast becoming a critical tool for local authorities to implement, monitor and govern pay equity strategies.
With a standardised job structure in place, pay equity analysis is made significantly easier, removing the management discretion around jobs and pay.
Having accurate up-to-date job and skills content is critical to objective setting and performance management. When this is working well, job content flows seamlessly from the recruitment process to the performance management process. If job content isn’t accurate, and doesn’t reflect the realities of a role, this can lead to employee attrition.
Research has shown a direct link between accurate job descriptions and attrition; 43% of employees who leave within 90 days state the reason for leaving is that their day-to-day role wasn’t what they expected.
An approach which focuses on skills has the same effect, improving job satisfaction for employees, and increasing retention rates.
Many organisations are moving to a skills-based approach and redesigning their operating models and strategies to have skills at their core.
This enables them to become more agile, to have higher levels of employee engagement, to encourage innovation and to show faster rates of growth.
A clear, streamlined job structure, with data available on the skills contained within the organisation enables possible career paths to be mapped out and communicated to employees. This opens up training and development opportunities and career paths up and around the organisation.
Employees will have clear visibility of roles and skills across the organisation and can identify possible roles in different teams and departments rather than simply focusing on movement within their current team.
From an organisational perspective, this enables greater succession planning, as skills can be identified internally to fill upcoming gaps in capability.
Planning your workforce around the skills that are needed now and in the future is a critical task that all local authorities need to undertake.
Skills data enabled by AI improves workforce planning and analytics for local authorities by identifying skill gaps, forecasting future workforce needs, and optimising talent allocation.
AI can be used to analyse employee skills, predict shortages, and recommend targeted training. It also supports data-driven decision-making, helping councils align talent with evolving public service demands.
Automation of workforce analytics enhances efficiency, reduces hiring costs, and ensures local authorities have the right skills in place to meet future challenges and deliver better public services.
Where to start
As a starting point for any organisation, technology can fast-track the harmonisation of your organisation’s job and skills data, reducing the process from years or months to just weeks, giving you the scope to start transforming jobs across your council or local authority.
Job groupings are a key step in preparation for EU pay transparency, enabling organisations to compare jobs and assess equal pay.
The EU Pay Transparency Directive is far more rigorous than any pay transparency legislation previously introduced, requiring organisations to closely examine their compensation and talent management processes.
Several requirements of the directive require the creation of ‘categories of workers’, or job groupings, to enable work and pay to be compared.
To comply with the right for employees to know the criteria being used for determining pay, the amount comparable employees are paid on average, and the equal pay disclosure and reporting requirements, the first step is to define Categories of Worker.
The EU Directive requires jobs to be grouped into categories of worker; namely workers performing the same work or work of equal value.
A ‘category of workers’ is defined as:
“Workers performing the same work or work of equal value grouped in a non-arbitrary manner based on the non-discriminatory and objective gender-neutral criteria referred to in Article 4(4), by the workers’ employer and, where applicable, in cooperation with the workers’ representatives in accordance with national law and/or practice.”
Although it refers to workers, it is helpful to think of this as being about jobs and not people. For this reason, we refer job groupings.
To understand how to group jobs and create categories of workers, it is important to understand what we mean by equal work or work of equal value. The Directive 2006/54/EC Article 4(4) defines this as “the same work or for work to which equal value is attributed”
“Workers have the right to request information on their individual pay level and the average pay levels, broken down by sex, for categories of workers performing the same work as them or work of equal value to theirs.” (article 7)
Why is it important to group jobs?
Many companies pay employees based on market rates, and there can be large differences between jobs, even when they are evaluated as equal.
This is no surprise as the market reflects society and incorporates the historical undervaluation of female and minority work. Grouping jobs enables companies to understand which jobs are of equal value, and to understand if there are pay discrepancies that need to be either explained or corrected.
The main issue is that companies have often not looked at jobs in terms of skills, effort and responsibility, as required by this legislation.
To ensure organisational readiness for EU transparency legislation and a robust approach to managing equal work or work of equal value, organisations need to be able to create job groupings and analyse their jobs in three ways:
1. Jobs of equal work
This involves identifying jobs where the same work is being done, usually jobs with the same job title and/or job description. Employees can request to see the pay level of other employees who appear to be doing the same work.
This means that organisations need to have a way of easily consolidating and comparing pay structures for jobs of equal work.
2. Jobs doing similar work
Jobs which involve similar work at a similar level or jobs with similar characteristics in terms of the role’s scope (skill, effort, responsibility and working conditions). Employees may claim that their job is similar to another in a different area and request to see the pay level.
This means that organisations need to have a way to easily consolidate and compare pay structure across jobs that could be similar both in terms of the work and show that you understand both the content and value of each job and can point to where the similarities and differences are.
3. Jobs of equal value
Jobs of equal value are those roles where the factors used to determine their value are of equal value i.e., all jobs that have similar levels of value across skills, effort, responsibility, and working conditions.
The value could be defined as the level, the grade or, if a more robust evaluation process is in place, the evaluation score. Employees can request to see the pay level of other employees whose jobs are of the same level of value.
This means that organisations need a way to easily consolidate and compare jobs of equal value and have a clear justification for any differences in pay.
Employees have a right to request information about pay levels for groups of workers who perform what is deemed to be the same work, similar work, or work of equal value as them
Job groupings are the key first step in our Roadmap to Prepare for the EU Pay Transparency Directive guide as they are key to analysing and reporting on equal work of equal value.
When it comes to skills, there is no one-size-fits-all solution. There are many nuances and every organisation is different.
This is why RoleMapper has been partnering with more and more customers to tackle the complexities of moving to a skills-based organisation. The reason for this support is down to the same frustration, surfacing skills is a huge challenge.
In response to this pain, RoleMapper has launched the RoleMapper Skills Innovation Partnership. The aim of the partnership is to help fast-track the shift to skills by co-creating and building innovative AI and technology solutions to support people strategy and process challenges.
Unlike generic platforms that rely on vast, pre-built skills databases with millions of data points, RoleMapper creates a customised skills taxonomy. It also leverages job data to ensure the skills framework is directly aligned with business needs, relevant roles, and strategic priorities
Additionally, RoleMapper's AI-powered skills inference solution surfaces skills and competencies from jobs, creates descriptors and proficiency levels, as well as validates and suggests enhancements to skills data based on industry insights.
The RoleMapper Skills Innovation Partnership is not only an opportunity to build solutions tailored to your organisation, but also a chance to move the dial on HR tech innovation within your business.
Gender-neutral job evaluation and classification play a key role in preparing for the salary transparency measures contained in the EU Pay Transparency Directive.
The Directive, which will become law across EU member states next year, requires companies to be more transparent around pay as a means of ensuring equal pay for work of equal value.
Gender-neutral criteria is a phrase mentioned throughout the EU Directive, and these criteria need to be used when grouping jobs of equal work and value, when carrying out job evaluation, and to ensure debiased recruitment processes.
For example, the Directive requires that organisations have pay structures based on job evaluation and classification systems that use ‘objective, gender-neutral criteria’. Article 4 states:
“Employers must have pay structures in place ensuring that there are no gender-based pay differences between workers performing the same work or work of equal value that are not justified on the basis of objective, gender-neutral criteria.”
The Directive doesn’t explicitly define what this gender-neutral criteria should be, but it does refer to four categories of objective criteria:
In the past, criteria used within job evaluation methods have been accused of being gender-biased and discriminatory, and they certainly can be if not adjusted to correct this bias.
The issue is that these methods have often failed to address the gender pay gap as they have tended evaluate male and female dominated jobs differently. Until recently, female-dominated jobs were evaluated based on methods designed mainly for male-dominated jobs, which partly accounts for wage discrimination.
For example, job evaluation methods have focused on physical effort and valuing this more highly whilst overlooking mental and emotional effort.
Predominantly female jobs often involve different requirements from those of predominantly male jobs, whether in terms of qualifications, effort, responsibility or working conditions.
For example, a recent ILO (International Labour Office) guide to gender-neutral job evaluation explains a number of examples of physical pressures in female-dominated jobs that are often overlooked:
It is important to be vigilant when selecting the job evaluation method and ensure that its content is equally tailored to both female and male dominated jobs.
Key elements of gender-neutral job evaluation include:
Ensuring the criteria used within job evaluation are gender-neutral is one of the most important methods of achieving pay equality and can help to challenge market-based and gender-biased assumptions that are often built into pay structures.
A recent EPSU paper on gender-neutral job evaluation in the public sector suggests some examples where these principles have been put into practice:
“Good practice examples of agreements on gender-neutral job evaluation and classification exist in several European countries, and some unions have developed and implemented successful gender-neutral job evaluation using objective and analytical criteria. These are typically based on factors and subfactors (skill, effort, responsibility and working conditions) that address all aspects of the value of work carried out in different occupations.
Gender-neutral job evaluation is crucial in ensuring that factors used in job assessments are inclusive of all aspects of work carried out, including factors that address overlooked elements of work carried out predominantly by women. These include overlooked job factors such as acquired learning, emotional/empathy skills, working with people with complex problems, dealing with difficult customers, emotional demands, communication skills, multitasking, lifting or moving people who are frail, restrictive light repetitive movements, exposure to chemicals and corrosive cleaning products etc.”
What this recommendation demonstrates is that the categories of objective criteria discussed in the EPSU paper (skills, effort, responsibility, working conditions) are also a good starting point for ensuring criteria are gender-neutral.
If an organisation incorporates skills, effort, responsibility and working conditions as criteria into their job evaluation approach, then this should meet the requirements of the EU Directive in terms of both objective and gender-neutral criteria.
The EU Directive doesn’t stipulate a specific job evaluation method, but its associated working document does recommend more analytical approaches.
These more analytical methods enable the position of a job to be established in relation to another in a sector or organisation, regardless of gender.
“Methods should be designed so that all positions or groups in an organisation can be assessed using the same job evaluation system, enabling comparisons across disciplines and professional boundaries.
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. They can thus be used to establish one of the most important components of the equal pay principle, namely ‘work of equal value’.”
The more analytical job evaluation methods, such as the factor comparison or point factor methods, enable job content to be broken down into factors that enable jobs to be compared in a non-discriminatory manner.
As we’ve discussed above, the key is that the selected factors - the criteria for assessing the various dimensions and characteristics of jobs - are not discriminatory.
It’s possible that the EU may take a more prescriptive approach to job evaluation in future.
Article 4 states that: ‘Where appropriate, the Commission may update Union-wide guidelines related to gender-neutral job evaluation and classification systems, in consultation with the European Institute for Gender Equality (EIGE).’
In advance of any further guidance, organisations need to determine an approach to job evaluation that:
Specifically regarding dimensions or levels for each of the criteria, the recommendation of the Directive is that organisations use or develop a job evaluation or job classification method which has dimensions or levels for each of the criteria that are used.
The steer from the EU in terms of pay transparency is that a structured job evaluation based on objective criteria is recommended. This more analytical method can be less discriminatory due to a more systematic and complex approach.
However many companies are not currently using structured job evaluation methodologies, with many using market pricing as the primary method of assessing the relative value of jobs within their organisation.
Given the requirement to show employees pay levels for jobs of the same value, valuing jobs based on market pricing alone is fraught with challenges.
Organisations with employees in the EU may need to assess whether their approach and adoption of job evaluation is sufficient, robust, and unbiased enough to enable compliance with the new Directive and to mitigate ongoing risk exposure.
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.
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