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.
The benefits of a skills-based approach are becoming apparent to more organisations, as they move towards a new operating model for the workplace - one which values skills over job titles.
This skills-based approach offers the chance to move from a job-centric, experience-driven workforce strategy to one that is more dynamic and agile.
Deloitte research found that 77% of business and HR executives believe ‘flexibly moving skills to work’ is critical to navigating future disruptions.
There are a range of potential benefits of a skills-based approach, from greater agility to improved employee retention and productivity, as well as more diverse and inclusive recruitment strategies.
Greater organisational agility
A skills-first approach supports speed and agility by redeploying the best talent to the most essential work.
Understanding the skills you have in the organisation helps you hire and move people to roles that will not only support business goals but provide on-the-job learning opportunities to develop people, and help you fill gaps in the future.
According to Deloitte, skills-based organisations are:
Just 14% of business executives surveyed by Deloitte strongly agreed that their organisation uses its employees’ skills and capabilities to their fullest potential.
A skills-based approach addresses this issue, allowing organisations to identify the talent and skills in their workforce, sometimes in unexpected places, which enables them to look at untapped talent.
This can have a beneficial effect on employee performance - making full use of their skills increases motivation and productivity; even more so when they can focus on activities that directly impact the business.
Ensuring that you have up-to-date and accurate data about the skills of your workforce is the optimal way to match people and opportunities and thereby improve the employee experience.
By focusing on skills, employees can be made to feel like unique, valued individuals, thriving in roles that allow them to put their skills into practice.
In the past, people may not have been considered for some roles purely because their previous experience and job titles may not have been a perfect match, even if they had the required skills.
One of the key benefits of a skills-based approach is a change of focus which values skills over experience can have a positive impact on diversity within an organisation and can reduce bias at every stage of the talent lifecycle beyond recruitment.
Employees who feel they have few opportunities for growth or progression can become bored and frustrated, and will naturally begin looking for a new challenge elsewhere.
These issues can be avoided if there are more opportunities for growth internally, and for employees to use their skills effectively.
Moreover, opportunities for development of skills and the opportunity to work on diverse projects can increase job satisfaction and loyalty. Skills-based organisations are 98% more likely to have a reputation as a place to grow and develop, and 98% more likely to retain high performers.
With pay transparency legislation being introduced around the world, establishing clear, skills-based pay frameworks helps organisations put this transparency into practice more effectively.
When compensation is tied to specific skill levels, certifications, or proficiencies, employees can understand how pay levels are calculated, and increases are earned, reducing ambiguity.
Standardised pay for similar skills also reduces bias and discrimination, supporting fair and equitable compensation.
Using skills data as the basis of performance management allows organisations to create a fairer and more transparent system that can drive both individual and team performance.
For example, by identifying employees’ specific skills through reviews, development plans can be created that impact organisational goals and individual aspirations. Through the tracking of skills key to business goals, performance management can also link individual contributions to strategic objectives.
Skills-based assessments based upon standardised skills benchmarks can also help to ensure consistency of reviews across teams and help to remove subjectivity and bias. Managers can then provide more specific and actionable feedback on skill gaps or strengths, rather than general comments about performance.
Another of the benefits of a skills-based approach is that skills data can make workforce planning more effective by focusing on employees' current and potential capabilities rather than job roles or titles.
Current employee skill data can be mapped against organisational needs, allowing businesses to pinpoint skills gaps in critical areas. This insight means that hiring, training, or reskilling efforts are targeted to address future requirements.
Skills data drives greater precision in workforce planning, as it can help identify competencies that are missing or under-represented, which in turn supports more inclusive hiring strategies.
There are challenges involved in switching towards a strategy based around skills, but there are also multiple benefits.
It can help organisations implement pay transparency policies, improve employee retention and performance, improve recruitment, and ultimately drive improved business performance.
In today's ever-evolving business landscape, taking time to comprehensively define potential career paths up and around your organisation has never been more important. This is particularly the case as companies struggle to retain employees post-pandemic.
Gallup recently found that 51% of U.S. employees are watching or actively seeking a new job. In the UK, 39% would like to switch jobs in the next 12 months, up from 33% a year ago.
As retention falls, recruitment costs soar. When costs are factored in for recruiting, hiring, training, and onboarding, replacing an employee can cost up to 21% of the annual salary for a role. When scaled up across all roles, this is a significant cost for any organisation.
Several studies have shown that workers who stay for a while in the same job, without a title change, are significantly more likely to leave for another company for the next step in their career. Employees who do not see a clear progression from their current role to a better position in the same organisation are more likely to turn to opportunities elsewhere.
LinkedIn research underlines this point. It shows that employees who make internal moves are 40% more likely to stay with organisations for at least three years, and have 50% longer tenures overall.
The solution to this is to be clear about the opportunities that exist around your organisation, and to build career paths that show employees a route to get from where they are now to where they want to be.
However, a barrier for many organisations is that their job titles, job content and job architecture are in such a chaotic state that this prevents the development of career paths within functional areas let alone laterally across the organisation. Employees are often left in the dark about opportunities within their business area or those that exist outside of their own team or function.
Career paths, sometimes also called Career Ladders, map out how internal movement can happen within an organisation. They provide a roadmap for employees to identify potential opportunities for the next step in their career based on their skills, interests and career objectives.
At a basic level, they show all the possible career path opportunities within a particular function. At a more advanced level they map out permanent and project-based opportunities laterally across the organisation based on skills requirements.
An example of a simple, vertical career path design within a function would be:
Source: Radford
Some organisations – such as Mastercard, BP and Rolls Royce - have also developed functional dual ladder career paths where employees can choose either a “technical / specialist” or “managerial” career path depending on whether they want to manage a team or not.
Example of Dual Career Path for Engineering roles:
More evolved, pan-organisational career paths will show how employees can use their existing skills to change disciplines, by moving laterally between functions, and where an employee can move up and across an organisation through a cross-functional promotion. These more varied career paths are particularly important as employees move away from wanting to progress through a traditional career path and instead are keen to navigate laterally and vertically, through a more complex web of opportunities, skill enhancements and role transitions.
Well-defined career paths tell employees exactly what the demands and requirements of each role are, so they are clear about what each job involves and what they must do to progress form one job to the next.
Each role in a career path needs a clear outline of the role showing the scope, responsibilities and requirements (knowledge, skills, competencies).
The career paths should make use of job levelling to show how different roles relate to one another in terms of level of responsibility and requirements. It should be clear where the similarities are between roles but also what the key differences are and what training and development is available for people wanting to progress into each role.
One of the main factors that prevents the development of career paths is the state of an organisation’s job structure, also known as a job architecture.
Many organisations exist as a long list of job titles and associated job codes that have been added to organically as the organisation has grown, changed, merged or acquired. Without proper governance or oversight, there is often a resulting state of chaos – hundreds of job titles, many just slight variations of each other, job levels all over the place and inconsistencies in salary ranges across roles, business areas and regions. Job descriptions can be equally chaotic with different formats and inconsistent information making it challenging to clearly articulate the differences in one job to another.
There are many impacts on career paths of this chaos:
Sometimes basic career paths can be defined without a job architecture in place, but these will remain within individual job functions and won’t give a clear picture to those looking for a wider range of opportunities.
A job architecture forms the building blocks of an organisation. It provides a framework for defining and aligning jobs within an organisation based on the work performed.
A well-designed job architecture can play a crucial role in defining career paths around the whole organisation by:
As companies are making preparations for the EU Pay Transparency Directive, job descriptions will play a key role.
The directive doesn’t explicitly mention job descriptions, but they are the building blocks which allow organisations to operationalise many of its requirements.
For this reason, ensuring that you have standardisation and governance over your job descriptions is essential.
This is not always the case though, with RoleMapper data showing that 60% of job descriptions are out of date and fail to reflect the skills required to do the job.
Job descriptions hold the information that can enable you to compare jobs of the same or similar value, to define the criteria for progression, and more.
Job groupings form the first step in our new Roadmap to Prepare for EU Pay Transparency guide, as they provide the means to consolidate and compare jobs of equal value and to justify any differences in pay.
As job descriptions define the work and skills required to do the job, they form the foundations of any job architecture. Job descriptions are the data inputs that enable you to identify equality and similarity of work, skills and value and group jobs to enable comparison and analysis.
Under the directive, organisations must have pay structures in place based on job evaluation and classification systems that use ‘objective, gender-neutral criteria’.
What this means is that organisations need to have a robust, objective, and unbiased mechanism to value jobs. The EU Directive recommends using a job evaluation or job classification methodology that can systematically value roles based on objective criteria.
Job descriptions are generally the basis for any job evaluation and classification methodology. They provide the baseline information of the work, skills, and scope of the role to enable an assessment of the objective criteria and, ultimately, the value of the job.
Article 6 of the Directive states that employers ‘...shall make easily accessible to their workers the criteria that are used to determine workers’ pay progression.’
This means that employers need to be clear on why pay varies in the company, and they should be able to explain the criteria for pay progression and why current pay for one role differs from that of another.
There is often a lot of bias and subjectivity associated with pay decisions. By requiring visibility of progression criteria, the directive is aiming to eliminate bias from compensation decisions.
Job descriptions are where you determine the skills requirements to articulate differential pay progression criteria from one job to another and the criteria to feed into inclusive recruitment practices.
Job descriptions form the baseline content for job postings. It is the job description that determines how inclusive and bias-free your postings will be.
In practice, you will need to review and revise job titles to eliminate gendered titles. So, ‘salesperson’ should be used instead of ‘salesman,’ for example.
Job postings must also be examined for gender-coded languages which might make the job less attractive to either gender. Words like ‘decisive’, or ‘courage’ may send subtle links that companies are looking to attract male applicants.
Under the directive, Member States ‘...shall put in place measures to prohibit contractual terms that restrict workers from disclosing information about their pay.’
In practice, employees will know a lot more about their pay as a result of the directive. They will know how their own pay has been determined, and how that compares to other jobs.
Employees will also have a right to discuss their pay with their colleagues. Without clear job descriptions and an understanding of why there are differences in compensation between jobs, this could be the cause of tension and misunderstandings.
The job description is used to support pay inequality disputes. There have been several high-profile equality court cases where the job description has been used as a basis for debating inequality of pay, with retailer Next being one such case.
In addition to this, Article 19 states that the assessment of equal pay ‘...shall not be limited to workers who are employed at the same time as the worker concerned.’
This means it is important that historical information on the job is held, and an audit trail of changes is retained, as it may be necessary to use it for analysis and comparison purposes.
More and more organisations are looking to move towards a skills-based approach, one which enables them to identify, develop and use specific skills to improve performance.
This approach is seen as necessary to adapt and remain competitive in an evolving business landscape, as well as to adapt to the dynamic nature of work.
A focus on skills can allow organisations to use the talents of their employees more effectively, placing them where they are most needed.
It helps to ensure that training programmes can be directed where they are most needed, as well as being able to identify and address gaps in organisational capabilities.
It’s an approach which has been shown to bring benefits to organisations. Deloitte research suggests that organisations adopting a skills-based approach are 63% more likely to achieve results.
Skills-based strategies also contribute to the ability to place talent more effectively, to anticipate and adapt to change, and to be more inclusive.
A skills-based approach also contributes to finding and retaining talent, as it’s popular with employees, with 73% believing skills-based practices would improve their experience at work, while 66% would be attracted to organisations that value skills over experience.
While the benefits of adopting a skills-based approach are clear to many organisations, there remain several challenges that make it more difficult to put this into practice.
These challenges involve data, resources, and the need for a unified approach to skills.
Putting a skills-based approach into practice requires data. If decisions are to be made around pay, promotions, or placements, then the data this is based upon needs to be reliable.
Organisations also need a single source of skills data so that decisions can be made across the organisation.
In practice, while some organisations have made progress in this area, skills data is often
siloed across departments and systems, making it difficult to achieve a unified view
An effective skills-based approach also requires a common definition of skills which are relevant to the organisation. These can vary between teams, as well as between organisations.
To be able to make effective decisions, it’s important that the language around skills is consistent and understandable. This information can then be used to inform decisions around recruitment and workforce planning.
Skills taxonomies are often large data sets of skills ‘tags’. In practice, more granular skills proficiency definitions are required for specific use-cases.
The challenge for organisations is that building up more detailed skills data requires significant effort and resources.
While off-the-shelf skills frameworks offer the attraction of a shortcut to implementing a skills approach, there can be issues with implementation.
Some frameworks can be too generic and don’t always fit in with the terms used in the organisation. This means greater effort in customising language and skills descriptions,
Building out the skills data across an organisation to the level of detail and granularity that is required for key use cases requires a significant investment in terms of resources, effort and cost.
Once organisations have completed some work collating skills data, there is further complexity in the process of consolidating this data across the business.
Practically speaking, this is about sharing different data sources, managing input and reviewing the process of approval around the business.
Once a skills framework has been agreed and completed, there remains the challenge of constant monitoring and updating. Organisations need to keep up with changing skills and changing needs in the business, and this requires continuous updates.
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