Strategic workforce planning (SWP) looks a lot different today than when we first encountered it more than a decade ago. This article begins with a quick overview of SWP’s recent evolution, which has been propelled by rapid changes in technology, the business environment, and organizations. Projecting this trajectory further, we offer our perspectives on the future. Employers, individuals, and government will all benefit from SWP’s enhanced capabilities, so long as companies can overcome the social, organizational, technology, and data challenges that lie ahead.
What Is Driving SWP’s Evolution?
SWP is evolving rapidly. The basic approach—projecting the supply and demand for talent and evaluating options for closing any gaps—has been around for at least as long as leaders have planned battles and commanded troops. But the ability to do so is advancing at a dizzying pace, even though the vast majority lags behind the vanguard.
A decade ago, being able to use historical data to project future retirement trends seemed like a tour de force. In fact, some business leaders doubted whether HR was even capable of such analyses. Since then, data and analytics have become ubiquitous throughout companies—including in HR—and technology advancements have strengthened SWP capabilities. Here are the advances we’ve seen on the leading edge of SWP and analytics:
Increased availability of workforce skill and capability data.
Human capital management applications can capture internal workforce data by job category, role, and skill, enabling insights that go beyond headcount segmentation (e.g., by tenure, level, location, etc.).
Social/professional profiles can help companies to develop role, skill, and task insights about their own employees and the external workforce.
Online job ads, data, and search tools can provide a proxy for internal and external market demand by role, skill, and tasks.
Better integration of workforce and other enterprise data.
Applications that link strategic, financial, and workforce data and processes can facilitate cross-functional work, communications, and understanding.
Third-party solutions or homegrown spreadsheets that integrate data, planning, and analysis across HR functions to inform build vs. buy decisions.
Wider distribution of workforce data and insights, combined with more extensive decision support enable more people to influence workforce planning.
Improvements in application design, analytics, and visualization enable users with a less technical background to share and manipulate data, analyze historical trends, and forecast.
Artificial intelligence (AI) and machine learning (ML) can deliver new insights, such as identifying ready-now and future candidates to fill skill gaps internally and detecting competitor threats in the talent marketplace. Several organizations, including Intel, have automated external talent/skill market-scanning capabilities.
Having better information than ever before, companies can now evaluate alternative options and make smarter buy/build/borrow/redeploy decisions about talent. Ironically, that information sometimes comes from external platforms, rather than from the company’s own human resource information system (HRIS). A large financial services firm, for example, wanted to bring all procurement activities into a single function, but its search tools couldn’t identify individuals if their titles or formal job descriptions didn’t mention procurement. By searching its employees’ profiles on LinkedIn, the company could root out procurement talent and activities that were otherwise hidden.
The Business Environment, Organizations, and the Workforce
In the 1990s, business strategists observed that companies had to navigate “permanent whitewater.” In recent years, those conditions have been upgraded to VUCA (volatile, uncertainty, complexity, and ambiguity). Digital transformation accelerates them even more. The rate of technological change has omnidirectional impacts, reshaping the competitive landscape, disrupting companies’ business models, redefining customer expectations, and reconfiguring work and how it gets done. The new calculus is not simply to ensure the company has the talent it needs; it’s to optimize the mix of talent and digital technology to maximize business results.
To respond to these changes, companies must operate very differently than in the past. They need to be much more open to their ecosystem, to allow external resources (knowledge, data, capabilities, and people) to move into and out of the organization, for example, through partnerships, open innovation, strong external networks, and contingent talent. To succeed at digital transformation, companies also need to be more flexible internally. Rigid hierarchies, functional silos, and job descriptions must give way to more fluid ways of working—often in cross-functional teams—to facilitate rapid problem-solving and innovation.
These changes dramatically alter what organizations need people to do. While technology won’t replace humans, altogether, they will need to contribute in ways that complement what technology is good at, such as standardized, repetitive tasks; or computation; or screening. People will need “soft” skills such as collaboration, communication, and creative thinking, in varying measure, depending on their roles. They will need to be avid consumers of new information, able to navigate their own learning journeys rather than waiting for the company to direct them.
Simultaneous with these changes, the nature of work, and changing skills requirements, the workforce itself is evolving. Companies are leveraging a more diverse mix of people such as long-term employees and others who do shorter stints and external talent with specialized, hard-to-find expertise, procured on a project-basis at premium rates. At many platform-based businesses, gig workers vastly outnumber employees. And as ecosystem partnerships become increasingly important to competitive advantage, a company may have no relationship with—or even know the names of—individuals whose work is critical to the company’s success.
The workforce is also changing due to demographic trends, generational preferences, and personal financial pressures. Older workers may stretch their careers long past the anticipated “sell-by” date, or opt for phased retirement, seasonal, or project-based work. Many younger workers expect rapid advancement and may have little patience with established career ladders. Their organizational attachments may be more fleeting, and the employment “deal” they seek may require employers to manage them in new ways.
Impacts on SWP
To keep pace with these changes, SWP has shifted. Where once it was a big-picture exercise that focused on the long view, it has become more agile and nimble enough to model new scenarios even before they happen. SWP has also been democratized. Whereas it used to be the purview of a specialized, enterprise team or center of excellence, it’s becoming more accessible to a wider set of stakeholders.
SWP practitioners’ knowledge and skills have also grown, fed by a decade or more of collective experience. Ten years ago, when a large global corporation was looking for a candidate who had successfully run SWP in one or two comparable companies, the headhunter’s request seemed laughable. Today it is less so. Many SWP leaders we know have built credibility within their organizations by enhancing analytical skill and forging strong partnerships with business leaders, finance, strategy, risk management, and their own HR colleagues. Some have, indeed, moved on to bigger jobs at other companies. No longer is SWP the dark horse that it once was.
Figure 1 shows how we see these trends, and others, shaping SWP’s future, using a force field analysis. Force field analysis is a change-management tool for identifying the factors operating in situation that enable a specific change (in this case, the advancement of SWP) or block it. By gaining a clearer understanding of these forces, organizations can explore ways to increase the drivers, reduce the restraints, or both, to achieve the desired change: getting the maximum value from SWP.
Imagining the Future
In 2013, LinkedIn CEO Jeff Weiner set forth a bold vision for how LinkedIn would transform the labor market by, in effect, allowing total transparency among all the players. (While Weiner didn’t include government is this social graph of the global labor market, governments would inevitably be heavy users of this information as an input to their economic policies.)
“[W]e want to digitally represent every economic opportunity in the world—jobs, full-time, part-time.…We want to digitally represent every skill required to obtain one of those opportunities. We would like there to be a profile of every company in the world…, a profile representing every higher education organization, a professional profile of every member of the global workforce, over three billion people. And we’d like to ultimately overlay professionally relevant knowledge, and information for each of those individuals, companies and universities—to the extent that they would like to share it.
“Our job would then be to get out of the way and allow each of those nodes to connect where it can create the most value, and for capital—all forms of capital, intellectual capital, working capital, human capital—to go to where it can best be leveraged.”
Weiner’s notion that LinkedIn could make the labor market totally transparent to all stakeholders may be hyperbolic—as we know, he hasn’t continued voicing this aspiration—but it does presage significant changes for the labor market’s three major stakeholders: employers, individuals, and government.
Organizations will be better able to match supply and demand:
Increased availability of external workforce data, combined with improved processes and internal applications, will allow companies to update workforce data more frequently—monthly, for example. Coupled with robust analytics, this will enable companies to detect fluctuations in internal workforce supply (e.g., voluntary attrition) and external workforce supply and demand (competitors building staff in advance of product or service launch).
Better integration of workforce and strategic, financial, and sales data, applications and analytics/ML will help companies detect actual or potential changes in workforce demand, identify gaps, and evaluate options (such as staff more rapidly in response to a spike in sales, before hiring requests flow through HR).
More timely data signals and ML-based analyses and recommendations will give organizations a longer lead time to pursue a wider array of options for closing supply-demand gaps, including ahead-of-the curve reskilling, upskilling, and hiring initiatives. The IT services and solutions company DXC, for example, uses ML to read market trends and develop learning ahead of the demand for those skills.1
ML and other developments may also impact how we approach job and skill taxonomies, many of which have shortcomings. Taxonomy categories are rigid and updating them to reflect evolving business environments is difficult. They aren’t very good at capturing depth of skill (especially on the supply side) or the tasks associated with a particular job.
ML developments could address these shortcomings by enabling companies to infer employee expertise based on social profile and other information. They will also help predict job classifications / taxonomies based on evolving skills and tasks. Worktask planning is a better approach than workforce planning in light of AI and automation’s impacts on work, as Dave Ulrich argues in his In First Person interview in this issue of People + Strategy.
Better workforce segmentation. Today many segmentation approaches are based on departments, job categories, job roles, and/or skills that are part of structured taxonomies that may not fully capture or keep up with the actual work people are doing or the skills that they apply. Going forward, AI/ML could help to identify clusters of tasks and skills—doing this better and better as it learns—and detect how they change over time, which could provide an alternative foundation for segmentation. Based on social profiles and other information, AI/ML will also predict the level of employee expertise, identifying supply-demand gaps and what is needed to address those gaps.
Better alignment across all organizational levels in conducting SWP and recommending actions to address gaps.
Managers will also be able to improve workforce planning and management for their teams in several ways:
Greater visibility into work and skill supply-and-demand trends and how those trends could affect their teams.
They could also receive ML-based recommendations on how to address current/anticipated gaps.
Customized team insights (e.g., propensity of team members to leave; gaps in team skills) along with ML-based recommendations on ways to address these issues.
Better visibility to the skill/expertise of employees outside their team who they potentially tap to fill a need.
Individuals—whether they’re students choosing a field of study, job applicants, employees, or contingent workers—will have a clearer idea where the job market is headed so they can equip themselves for the future.
A new career compass. The information architecture that enables companies to do SWP can also serve “people.” One of The Conference Board’s earliest research reports on SWP included an unanticipated finding. Once the U.S. Coast Guard had developed what was, for the time, a highly sophisticated process for analyzing workforce dynamics and developing and deploying talent, it realized that these capabilities could be repurposed to become a navigational tool for individuals. “Coasties” could investigate the requirements for other jobs, search for developmental resources to strengthen their qualifications, and even connect with others who might offer guidance. Since then, many other organizations have made a similar discovery. Much as Weiner envisioned, labor market data doesn’t become more transparent to just one party, it becomes (at least somewhat) more transparent to everyone.2
Personalized learning recommendations, based on employees’ interests, career goals, or the needs of their current team. Recommendations may also point to new skills the company will need in the future to support an up-and-coming product or service offering.
Governments will have deeper insights about regional, industry, and employer trends, enabling them to manage supply-demand imbalances and support labor force transitions:
Access to online professional profiles and ads that will give them a more extensive real-time view of labor supply and demand than can be obtained through surveys.
Ability of national, state, and regional programs to facilitate and support workforce planning and transitions. Singapore, for example, has two governmental boards that are involved in aspects of workforce planning for the country. SkillsFuture Singapore is focused on skills development and career planning, industry-relevant education and training, and training in emerging skill areas. Workforce Singapore supports the transformation of the local workforce and industry to meet ongoing economic needs. It helps workers to secure jobs, businesses to secure workers, facilitates matching the right people to the right jobs, and has programs that support business competitiveness and job redesign. Singapore’s Future Economy Council (FEC) also helps to drive the growth and transformation of Singapore’s economy for the future.
A Cautionary Warning
While we are excited about path forward, we also see risks and challenges that SWP will need to address along the way.
1. Growing public concern about data privacy. The European Union’s General Data Protection Regulation (GDPR), which went into effect in 2018, isn’t the first to reign in companies’ use of personal data, nor will it be the last. While GDPR allows greater latitude in handling personal data that is material to the employment relationship—for example, employee demographics, work history, learning activities, career movement, and performance reviews—employers still need to be transparent, ethical, and legally compliant about how they use this data, including in SWP and human capital analytics. The regulations are considerably stricter for personal data from non-employees, including contingent workers and job applicants. Companies that do not meet these standards are likely to face legal, financial, and reputational consequences. They may also squander employee trust, which could have numerous repercussions, constraining their ability to do SWP and analytics.
To mitigate these risks, SWP needs to become an active player in the company’s data-protection efforts: identifying problems or potential vulnerabilities, putting in place advisory and governance structures to address them, designing controls, educating data collectors and users, developing other solutions, and communicating the company’s data-protection policies and practices.
2. Adequate leadership support and resources to develop and sustain strategic workforce planning capabilities. To achieve the full benefit, companies will need to invest in:
Data, tools, and technology to provide accurate and timely information for planning and decision-making.
Standard workforce segment taxonomies (roles, skills) to enable skill-based segment planning.
Expertise to develop or interpret data-based workforce planning insights and recommendations.
To mitigate the potential risk of inadequate funding, SWP should develop investment cases and roadmaps including priorities, expected costs and returns, and work closely with stakeholders involved in investment decisions and allocations.
3. Mechanisms to ensure collaboration and coordination across organizational boundaries. There is a tendency for workforce planning stakeholders (including operations, finance, and HR functions) to work in siloes even when technology gives them access to a common set of workforce information and insights. Plans and actions that are not aligned across groups can put SWP effectiveness at risk.
To mitigate this risk, companies should invest in mechanisms (e.g., agile teams) to align strategic workforce insights, decision-making, and action enterprise-wide.
4. Ensuring that supporting technologies are accurate (in terms of insights, predictions), integrated, and flexible (able to respond to continued changes in the organization and its business environments). Methods that produce insights and predictions (e.g., ML, regression), and systems that house data and support processes are key elements of the scenarios described in this article. They provide signals, produce insights, and support decisions and actions, yet they are only as good as the people and circumstances behind their creation. There is a risk in continuing to use tools and processes for business environments that have changed.
To mitigate that risk, companies should regularly review and update technologies and processes to ensure they remain optimal for ongoing changes in the business environment.
Will SWP Go Away?
Looking even farther ahead, we foresee the potential of a bigger change: Data and analytics will increasingly permeate business strategy, operations, resource- and capability-planning, and every HR function. As companies become better at integrating data enterprise-wide, the functional ownership of that data will become less consequential. In the end, it will all be business data. In that case, SWP may outlive its usefulness as a separate function. It may simply become the way companies operate and compete. That’s not necessarily bad news for SWP professionals. They’ve already got the right knowledge, skills, and experience to position themselves for where the job market may be heading.
Mary Young is Principal Researcher at The Conference Board. She can be reached at Mary.Young@conference-board.org.
Seth Hollander is Senior Manager in Skills and Talent Market Insights at IBM. He can be reached at email@example.com.
1 Rhodes, Evan, How Workforce Reskilling Meets Business Strategy. CHRO Quarterly (Gartner 2018), 10-15.
2 Mary B Young, Strategic Workforce Planning: Forecasting Human Capital Needs to Execute Business Strategy, The Conference Board (2006).