The nature of work and the workplace are rapidly changing—and talent strategies are also changing. There’s evidence that a small number of companies are pivoting to a skills-based approach to talent development to address skills gaps through upskilling and reskilling their existing workforces; to build new, future-focused skills; and to tap into contingent workers for specialized projects or tasks.

The disruption of the COVID-19 pandemic is leading to a new willingness to consider how to redesign work around workers’ individual needs and circumstances.¹ Companies at the forefront of the pivot to a skills-based approach include several technology firms, including IBM, Cisco and Google, and other notable industry leaders such as Unilever and Schneider Electric.

The problem is that job frameworks may be too rigid for companies operating in unpredictable or rapidly changing skills environments. Jobs are evolving fast; one prediction is that 50 percent of all employees will need reskilling by 2025, and 40 percent of current workers’ core skills are expected to change between now and 2025.² These trends present the real danger of selecting and developing workers for jobs that soon may disappear, or which are changing to an unrecognizable extent due to technology integration, automation, and AI-based augmentation.³ These companies also recognize that their employees expect more flexible jobs and career paths that reflect their personal needs, interests, and goals.

This research was done in association with The Conference Board European Leadership, Talent, and Organizational Transformation Council, whose members participated in three focus groups and individual interviews to share their insights into this topic. The council includes global or EMEA heads of leadership and talent from some of the largest European-based businesses, which include Novartis, Toyota, and JTI.

Insights for what’s ahead

Companies that can pivot to skills-led talent strategies may gain competitive advantage by quickly recognizing emerging new skills and how to integrate these skills into the roles and jobs of the future. The advantage comes by targeting recruitment approaches based on these skills. Such an approach also ensures that workers with closely related or transferable skills have the development opportunities to transition into these new jobs and roles as they are created.

Building a common language and framework of skills requires companies to agree on detailed taxonomies of skills—but this is complex and difficult and companies are likely to look to cloud-based, AI-enabled solutions. Skills taxonomies are a vital means of aligning strategic workforce planning, talent planning, and learning strategies. However, companies are struggling to build and maintain their own skills taxonomies. Given the dynamic nature of the skills landscape, it seems large international organizations will look to cloud-based solutions powered by AI technology. The difficulty is that the commercial market for these tools is still maturing and consolidating, as is the technology, making investment decisions difficult and high risk for companies—yet the risk of delaying this investment may be even higher should the company fall behind in building a future-ready workforce.

To overcome talent shortages, future-focused and socially responsible businesses are likely to join industry-wide and cross-section initiatives to agree on skills taxonomies that will inform education and workplace-based learning and certification. There are growing calls from national governments and international organizations for public-private cooperation to develop open-source international and global skills taxonomies. The goal is to enable intra- and cross-industry collaboration on reskilling and redeployment efforts and to enable a better alignment between learning providers and employers. In the long term, it may be more beneficial for businesses to help shape the skills agenda by joining such collaborations. This cooperation will ensure a smoother and faster transition from education to employment and also help target skills training for groups of workers at high risk of skills obsolescence.

While some work and some jobs can be parceled out in new combinations via talent marketplaces, certain “fixed” jobs are likely to continue; as a result, companies may need to have a two-track approach to how they assign, promote, and reward staff. Jobs that areshaped by regulatory requirements, for example—such as a company secretary or risk officer—may need to be approached differently. This apparent double standard may lead to confusion, distrust, or serious concerns about equality and equity without transparent communication to ensure employee understanding of the processes.

Adopting AI-based talent marketplaces could be a two-edged sword. Companies need to guard against the danger that the way work gets parceled out to employers or even sent externally could appear to be a purely mechanistic process, determined by opaque algorithms, in which the personal information entered in an employee skills profile is simply a series of useful data points. There needs to be intentional leadership, governance, and messaging to ensure talent platforms are perceived as part of a human-centric and ethical system that aligns employer’s personal and career aspirations with work opportunities in the business.

If multinational companies adopt AI-enabled talent marketplaces, as seems likely, they may lose their distinctive talent cultures and find it harder to compete for in-demand talent. It will be important to ensure that any decisions about technology-based solutions keep in sight the underpinning an organization’s talent values and mindset. For example, some companies’ talent proposition is to offer varied career opportunities across the global business, while others work on the assumption that talented individuals will stay with them for a limited time but will continue to remain in their extended talent ecosystem.

Benefits of skills-based talent strategies

Focusing on skills requires work to be parceled out to the individual or team with the most appropriate skills and experiences. Benefits include:

• Stronger line of sight to the skills and experiences existing in the total talent pool (internal and external), enabling people to be matched with jobs and projects.
• Vital insight into skills gaps (especially in high demand or emerging new skills).
• The opportunity to design career paths based on skills and adjacent skills that will enable people to understand and navigate a career that suits them.
• Greater transparency in talent processes—skills become a common currency on which to base job selection, development and certification, reward, and career progression.
• An enterprise-wide approach to skills between talent and other vital HC processes, especially learning and development, people analytics, and strategic workforce planning.

Pivoting from jobs to skills

Most companies currently follow a job-based approach to talent development. This approach involves using a well-established jobs architecture to define levels, grades, career paths, spans of control, the criteria for career progression, and compensation based on perceived job value.

The problem is that job frameworks may be too rigid for companies operating in unpredictable or rapidly changing skills environments. Jobs and skills requirements are quickly evolving, accelerated by the COVID-19 pandemic: by 2025, 85 million jobs may be displaced by a shift in the division of labor between humans and machines—but 97 million new roles may emerge.⁴

An international survey found that nearly one in four organizations said their current HR infrastructure was ill-suited to deploy a new strategy to tackle address emerging skill gaps. Almost one organization in three lacked the tools or knowledge to quantify the business case for reskilling.⁵

One European talent leader describes the challenge for her science and technology company with more than 70,000 employees: “We have done a lot of work on job title, position, and experience but much less on skills, so this is a big shift for us. There’s a hesitancy across the business because we haven’t really measured skills or made a skills inventory. Individual managers deal with hard, technical skills. A small number of soft skills around leadership are in our performance management system, but even there, there’s an element of subjectivity.” The company is focusing on a step-by-step approach, using data and internal pilots to build the business case for focusing on skills.

In a global health care business with more than 100,000 employees, the business case for focusing on skills is accepted and based on three vital drivers:

1. The urgent need to hire and build the right skills to ensure growth and innovation in a highly knowledge-driven industry—for example, skills for new technology platforms and digital marketing and selling skills.
2. Profitability—if workforce skills fail to keep pace with business need, this increases the risk of costly business restructuring to build new capability (for example, through acquisitions and divestments).
3. Reskilling and upskilling are vital for building trust with society and social responsibility, and enhancing business reputation. The global head of talent comments: “We believe as a socially responsible employer, we should reskill our talent as much as possible. Even if we don’t retain them in the long term, we should reskill them to ensure they can redeploy themselves to opportunities elsewhere.”

While many companies are at the early stage of formulating the business case for shifting to a skills-based talent approach, a small number of companies have moved ahead by investing in cloud-based talent marketplaces that use AI-based tools to catalog skills and match workers to jobs and projects. First movers include technology firms like IBM, Cisco, and Google, and industry leaders like Schneider Electric and Unilever. Unilever has rolled out an internal talent marketplace for its 155,000 employees, Flex Experiences (powered by AI provider Gloat, integrated with Workday and Degreed’s learning platform) for a seamless process of learning and experience.⁶

Schneider Electric launched its internal mobility platform in late 2018 to develop and retain talent.⁷ Through this AI-driven platform, employees create a talent profile to describe their skills, experience, qualifications, and aspirations. They can access job postings, mentors, training, and temporary projects. The platform uses algorithms to match employees with courses and mentors. Schneider has rolled out the platform to its 144,000 employees in multiple languages (e.g., English, French, Spanish, Mandarin) to ensure the platform is as inclusive as possible. Before the introduction of the new platform, employees had to gain the approval of their line managers to apply for an internal vacancy or temporary project—leading to the tendency of talent hoarding. Now, the message is that employees own their own careers and can seize any opportunity to develop their full potential through Open Talent Marketplace (#OTM).

In the COVID-19 crisis, the inclusion of short-term projects in the platform enabled any individuals with spare time to help more hard-pressed colleagues. “These are very uncertain times…Giving employees a chance to lend a hand and feel they are contributing is great,” comments the then-vice president of talent digitalization.⁸

Making the decision to move to a skills-based talent strategy

Moving to a skills-based approach depends on the interplay of three drivers: how fast skills are changing; the need for organizational agility; and the extent that work is organized into projects and tasks.

As work is increasingly organized around specific tasks or projects for more operational flexibility or to deal with new requirements, it becomes harder to allocate work neatly based on someone’s job or role. Rather, individual skills and experience become the more important factor. Employees doing “extra” work outside their defined job might not be recognized or rewarded for this. Job descriptions are less and less accurate about what people actually do. Invaluable skills data are scattered, preventing insights about skills gaps, and creating blind spots about “sunrise and sunset” skills.

For companies transitioning to a project-based or fully Agile operating model with Agile teams and working practices, there is a clear case for moving to skills as a common currency across the organization, enabling staff to understand the skills requirements when they pitch for inclusion on a project, and enabling project leaders more easily to select team members. In other companies, the decision is more difficult and may need to be taken at the divisional or business unit level—but the difficulty is that two parallel career management frameworks (one based on jobs and one on skills) may then co-exist, potentially causing confusion and complexity in global talent processes.

Operational challenges

Building a strong business case for change It’s essential for the human capital management function to gain buy-in and traction with business leaders by explaining clearly the business drivers and benefits. Key stakeholders in HR, such as talent, learning, workforce planning, and people analytics, can form a powerful coalition. A data-led approach can help articulate the forward-looking business case for the shift, especially when a job-based talent strategy appears to be effective in the short term.

One European-based multinational company spent five months conducting a feasibility study before putting forward detailed proposals for moving to a skills-based talent strategy. The analysis entailed working with a professional services firm; detailed audits of vendors and solutions; mapping of current and future skills; assessing market trends for skills; and looking at vendor offerings, especially cloud-based solutions. (In this company’s view, leading vendors are Eightfold, Gloat, Fuel50, Ascendify, and HR Forecast.) The company also conducted two pilots in two different contexts—one in its research and development center, and a second based on the top 150-175 critical roles across the business.

The global head of strategic talent management comments: “Our feasibility study was thorough, but in hindsight, we would have first created more excitement and pull from the business so that we could gain their buy-in to progress at the speed we in HR think is necessary to get the full benefits of a skills-based strategy.”

Partnering with the business It’s essential to work with both the executive team and senior leaders across the business for talent teams to establish a shared language and mindset about skills. Definitions of critical skills or future-focused skills need to be agreed on with the business. Ownership of skills also needs working out—for example, some functions or divisions “own” technical skills, while HR often has responsibility for generic or corporate-wide skills (leadership, change management).

One global health care business has decided to form a global skills council to enable collaboration and co-ownership of the future skills agendas. The council will work to allocate and secure funding and remove any operational hurdles as the company implements a skills-based framework and internal talent marketplace platform. The council is likely to meet quarterly and will comprise executive committee members, particularly from the commercial and scientific functions and the HR global team.

“We want to ensure we have a regular cadence with business leaders,” explains the global head of talent. “We all recognize that it will be critical to achieve consistency in how our divisions approach skills. While we don’t want to centralize everything across our commercial units, there are some shared skills needs, for example in data science, so we will need a common skills framework and a common approach to skills assessment, as well as supporting technology, to ensure we leverage these future-focused skills across the whole organization.”

Skills taxonomy This is a common language to describe skills (which could include technical skills, company-based generic skills, leadership skills, cognitive and behavioral skills). Companies can create and maintain their own taxonomies—but this is complex and onerous. “We tried to create a manual-based skills catalogue in the past, but this was a failure because it was so difficult to maintain and update,” says a senior HR leader from a global manufacturing business with 40,000 employees and more than 25 factories. “We are revisiting this issue exploring commercial taxonomies, but we want something simple and user friendly.” The company also needs to figure out how to incorporate experience in any taxonomy—for example, experience of working in different regions and markets.

Another option is to draw on international open-source taxonomies, such as the European Union’s European Skills Competencies and Occupations (ESCO) and the US Occupational Information Network (O*NET) frameworks. In 2020, the World Economic Forum also initiated an ambitious coalition to create a global skills taxonomy. The coalition comprises chief executive officers, chief learning officers, national ministers, industry bodies, and online learning providers including Coursera, LinkedIn, and internet technology companies including Google and Amazon.⁹

AI-based cloud taxonomies are the most likely solution for large MNCs. However, selecting a skills taxonomy is a difficult decision and requires finding the sweet spot between a taxonomy that is either too generic or too granular. This search can become something of a rabbit hole for companies, with one learning and development executive describing the experience as “like wading through spaghetti!”

Before going to the commercial market, one global company undertook extensive collaboration across the business to agree upon a common language of skills: “This was key to help us all align, but it was a difficult process as everyone had a point of view. The more technical experts were involved, the more granular it became,” says the global head of talent. The company eventually reached a set of definitions “which have been tremendously helpful to move us forward and enable us to communicate clearly to senior business leaders.” These definitions include:

• Skills: at the most granular level, skills are typically described by a word or phrase;
• Skills proficiency: level of attainment;
• Skills cloud: this describes an ontology or library of skills; it also reveals the relationship between skills (for example, adjacent skills or foundational skills leading to more advanced skills);
• Competencies: which describes a combination of skills, experience, and personal growth;
• Role: a description of responsibilities and accountabilities, including links to competencies and skills required for the role.

Reliability of skills profiles Skills taxonomies can be used as a basis for employee profiles and to describe roles and projects to enable accurate matching. However, the system relies on workers keeping their profiles updated (and some may need help in “curating” their work experiences). How to validate these profiles is also an important consideration, with some companies looking to 360-degree evaluations from team leaders, colleagues, and internal or external customers.

Vendors The market for AI-based skills taxonomies and talent marketplaces is young and still consolidating. Although many vendors position their platforms as talent marketplaces, these vary considerably in their sophistication. For example, some platforms may simply alert employees to internal vacancies, while other systems match (and recommend) individuals to a range of opportunities, such as temporary projects, secondments, and stretch assignments. They might also extend to developmental opportunities such as mentoring and coaching, and also link individual career paths to learning systems. Some platforms are much more advanced in their use of AI, using machine learning and natural language processing to harness structured and unstructured data, distill knowledge, and also employ user data to improve matching processes.

Interviewees spoke about the dilemma of looking to large established vendors for a variety of solutions or opting for small niche players that have more innovative products but might have difficulty in scaling these for a large global business or lack the maturity to partner with the business to design bespoke software. The degree of interoperability of these platforms with other key processes and systems (especially learning platforms) is a vital and tough challenge, requiring close collaboration with other key functions such as IT and finance.

Recognizing that technology will keep changing, a global pharmaceuticals company has agreed on a set of principles to guide investment decisions going forward. These include having only one data lake in the HR profit and overhead organization (P&O); creating intuitive and user-friendly design; not aiming for perfection but prioritizing consistency, seamless connections, and end-to-end experience; and taking a balanced approach by ensuring solutions tackle common needs across learning, talent management, talent acquisition, and rewards.

Trust and transparency Companies opting for platforms that use powerful search, matching, and AI-driven prediction algorithms to match people with tasks and projects need to be sensitive to concerns about privacy and fairness. These platforms will need to be compliant with Europe’s General Data Protection Regulation. Attitudes to internal mobility can also have a bearing on trust. For example, if employees fear the ire of a line manager, they will not be honest about their interest in moving to different roles and projects.

Aligning or integrating key talent processes Other key processes will need to pivot to skills; for example, learning strategies will need to focus on identifying critical skills, adjacent skills (to aid internal talent mobility), and future-focused skills. Reward strategies may also need to shift—for example, a recent survey found that 30 percent of respondents agreed their organization needed to increase the importance of skills in pay decisions (relative to performance and job).¹⁰ Managers in IBM use CogniPay, an AI-based system, to make compensation decisions based on three main considerations: market demand, internal forecast demands, and attrition data for a skill or cluster of skills.¹¹

Recommendations

• Start with the work that needs to be done and consider whether describing this work in terms of skills instead of a job would reveal a larger reservoir of talent.
• Look for warning signs that job descriptions are less and less accurate—for example, is there a growing need for discretionary reward and recognition for achievements that are outside someone’s “day job”?
• Be led by strategy, not technology: when companies have the same level of technology advancement, your values and philosophies for how you attract, engage, and retain skilled staff will be your competitive differentiator.
• Aim for a human-centric approach by ensuring key career decisions (promotion, selection onto a stretch project) are made by a human being and not just a machine. Build in highly visible human-based touchpoints, such as opportunities to discuss and provide feedback or question a decision—limiting the danger of and suspicion of hidden agendas (job losses, automation) and opaque algorithms deciding careers.
• Define some guiding principles (for example, data security, interoperability, prioritizing based on business need or impact) as a compass for selecting technology, as this will keep evolving.
• Seek out opportunities to exchange knowledge and experience with other organizations, especially in their experience of using vendors or professional service firms.
• Pilot and experiment: start with a critical talent segment or section of the business where skills profiles are fast changing, and think of a minimum viable project before scaling up.

Related Resources from The Conference Board

Artificial Intelligence for Onboarding, January 2021

Artificial Intelligence for Executive Compensation, June 2020

Career Management for What Lies Ahead, April 2020

Artificial Intelligence for Total Rewards, April 2020

Artificial Intelligence for Talent Acquisition, January 2020

Artificial Intelligence for HR: Separating the Potential from the Hype, December 2019

Total Talent Mobility: Strategic Purposes, Barriers, and Best Practices, April 2019