Worker burnout is rife, and the public sector is no exception. Across many government agencies throughout the world, leaders are telling us that workloads are growing and their employees are increasingly overtaxed—especially the core teams they have entrusted with the most mission-critical priorities. But reallocating positions (also known as billets) to where they are most needed is a constant struggle. Many agencies have time-consuming, complex processes that can make it difficult for leaders to identify where teams need to grow and where workloads may in fact be shrinking or have room to shrink. Managers often tell us that they have little faith that their workforce requests will be approved, so they inflate requirements in the hope of getting what they need. This is called a “plus-up” mentality: every year’s request simply includes the current team plus some personnel inflation.
Our conversations with government agency managers have conveyed, in a broad sense, that workforce planning is broken, despite the significant time devoted to it. To compound the problem, the public sector has long struggled to compete with the private sector for top talent and to retain workers—a trend that has only intensified during the COVID-19 pandemic.1 Moreover, our experience with supporting leaders at large national agencies has revealed that they rarely believe strategic priorities receive the time and resource investments required to deliver on key goals. And that risk can be distributed unevenly, because leaders are often unaware of where the workload is truly far beyond the capacity of the team.
These challenges to effective workforce planning are difficult to overcome—even in the private sector where leaders can have more direct control over budgets and hiring flexibility. But cutting-edge companies are evolving their workforce planning to include data-driven practices that public-sector leaders could adapt to help them move beyond a plus-up mentality and strategically align positions with priorities.
With this in mind, we’ve designed five steps that could help government agencies more accurately estimate the workload required to deliver on their mission, improve how they analyze their teams and capabilities, and get a better handle on where the workload is likely to increase or decrease. These steps can also be adapted to a three-, five-, or ten-year planning horizon.
The examples cited in these steps are based on McKinsey research.
Step 1: Build a data-driven list of demand drivers. The first step is to identify and catalog the factors that make an organization’s workload increase or decrease. While government budgets may serve as a rough guide for the volume of work or the types of outputs and outcomes that may change over the next three to five years, public-sector workforces could benefit from going a layer deeper to identify the factors that accompany those top-line figures. Using the United States as an example, are any of the outputs becoming more complex, such as Department of Defense platforms that need to be built or Medicaid requests that need to be processed? In military contexts, this complexity might be measured by variants of a particular technology on a plane or ship, or the length of time a system spends in R&D.
When some agencies embark on this initial step to strategic workforce planning, they might find that they lack a unified inventory of their investments and that building this catalog is a necessary starting point. Other factors to consider: Are any previous requirements being sunset? Is there a growing variety of requirements that necessitate a broader range of expertise? And how might maintenance and sustainment needs increase?
During this process, some organizations can get stuck trying to build a baseline of exactly how many hours and full-time employees (FTEs) are required to meet current requirements. To help clear this hurdle, workload demand estimates could focus on year-over-year changes, as opposed to an exact estimate of how many people the agency needs today. For example, if processing a particular type of agency request is likely to increase by 20 percent over the next five years, the baseline allocation of resources to workload may not be perfected in a single year. But focusing on how workloads might change may help leaders make more informed decisions about where to assign new positions and enable the organization to rebalance over time.
Step 2: Estimate how variable the workload is for different parts of the organization. Once agencies have a good idea of how their workload may increase or decrease, they can estimate how variable each team’s workload is. The relationship between output and workloads can differ tremendously across agency teams. For example, for supporting functions such as the comptroller or HR, there may be very little correlation between the two. Circumstances can also alter the equation. Consider a department that needs to purchase twice as many goods or services over a three-year period. That may only require increasing the order with an established supplier, resulting in a negligible impact on workload. But it’s also possible that the department will need to find and manage multiple suppliers and double oversight to fulfill its remit, adding significantly to the team’s workload.
One common tactic used in the private sector is to categorize roles as either front office or front line, middle office, or support and then to estimate the demand drivers that could create more workload for each.
Front-office workloads tend to scale most closely with demand drivers. The private-sector analogy would be jobs that are customer facing and therefore can vary heavily depending on customer numbers. In the public sector, it could encompass roles such as public-sector doctors whose workloads increase with the number of patients they need to treat, consular-affairs officers processing passports, or government contractors working in a manufacturing facility.
Middle-office roles are strategic, but their workloads may not correlate as closely to demand drivers as front-office roles. In the private sector, these positions could fall under marketing or R&D. Program managers, for instance, often coordinate design and implementation of key outputs, such as new aviation technology, but the number of orders does not necessarily drive their workload. Support roles encompass many back-office functions that keep the lights on in an organization, such as HR, finance, and IT. Here, too, the workload may not correlate as closely to demand drivers as front-office functions.
Beyond the tactic of categorizing roles, it may be helpful to analyze the maturity of the processes required to deliver the outputs. For example, if the processes are already highly standardized, they are less likely to require excessive problem solving, iteration, and managerial input. The estimates could incorporate a review of whether team members are performing unique tasks that require in-depth coordination, or if tasks are repeated frequently and can be done with few additional meetings and oversight.
Finally, depending on the organization, these estimates may need to blend quantitative and qualitative data—gleaned from surveys or interviews—to determine fixed versus variable workloads. While some leaders may envision a model derived entirely from quantitative data, qualitative inputs may be necessary to gain a truly holistic view. Such blended models could also help focus any discussions or debates about workload on the precise inputs, as opposed to relying on a leader’s gut instinct.
Step 3: Assess the capabilities required to meet workload demand. From our experience serving government agencies in this area, some have recently tested a pioneering strategy in which each office outlines a set of technical capabilities that is the unique responsibility of that office. Negotiating among offices to determine exactly what this set will include could help mitigate overlap and help officials stay on top of technological advances or national circumstances that may impact requirements. Other potential benefits of greater coordination include helping to identify new roles and new jobs, enabling leaders to gain greater clarity on exactly what talent is required. For example, by identifying the exact capabilities needed for an IT support role, an agency could update the job description, rather than rely on an old, possibly dated one. Limiting the set of technical capabilities to 150 or less could lend greater support to agency-wide decision making. And once the list is agreed upon, the organization can ask leaders to assess the current levels of expertise and the state of readiness for each capability, which can also be rated to highlight the more important ones. Many agencies choose to do this on a biannual basis, with a clear process coordinated by a headquarter-based individual in charge of strategic priorities (rather than relying on HR).
Step 4: Identify how many people are actually working for the organization—and in what capacity. Personnel gaps in government are often filled by contractors or people on loan from other agencies. Yet workforce planning processes do not always assess total team size to include such external employees who augment staff. Interim team support is often necessary—and even healthy2—because workloads can fluctuate, and sometimes outside expertise is needed. By assessing the whole team and what they do, workforce plans could help analyze the costs associated with bringing in contractors versus seeking internal support, and assess the risks of outsourcing critical knowledge or core roles.
Step 5: Review supply–demand gaps and make decisions. Pair estimates of workload demand and volatility to inform decisions. The next step is to review where the workload is likely to grow or shrink relative to the capabilities required, and to identify the teams that will be most affected by those shifts. A central coordinating group could conduct this review, and then make the team or office leaders aware of the results and their implications (a masked output that only includes the specific team’s results could help avoid competitive responses across groups). Then comments and additional context can be gathered during a follow-up period (for efficiency, this is ideally a narrow window) to allow for corrections. Finally, the coordinating group could include a full suite of options leaders could consider for addressing any mismatches between workload and capabilities—upskilling/reskilling to hiring to “renting” talent (via contractors).
Make hiring decisions through a clear, mutually agreed-upon process. Though hiring decisions may seem relatively straightforward—expand teams where an increase in workload is likely to endure, or tap external support when a spike in workload is likely to be temporary—the stakes surrounding government hiring decisions are extremely high. Some commitments can last decades. They can impact people’s careers, which argues for a hiring process that is clear and transparent to office managers. Decisions could be based on memos where each team lead has had a chance to weigh in, and then office/team leads can be notified afterward. Or decisions could be announced in a large group meeting where there are opportunities to offer comments in real time.
In some cases, hiring decisions may be only the first step. The agency leader might need to make the case for realigning positions in the upcoming budget cycle (ensuring the decision is data driven could bolster the case for change). HR and finance teams may also need to rally around the decision to translate it into action (and, therefore, may need to be part of the decision-making process). For example, in some cases, one funding stream may be associated exclusively with materials costs, while another is more flexible.
Hard choices, pressing priorities
Strategic workforce allocation involves hard choices—especially when hiring leeway is often dictated by legislative bodies. But a thoughtful, data-driven, methodological approach to strategically align supply to demand could help agencies to better deliver on their priorities and missions. One government agency that we supported in building a data-driven approach was able to explain—for the first time—what was driving its workload growth. Moreover, the agency learned that it actually needed five times fewer new positions than what it had requested previously using a plus-up mentality.
From agency leaders to midlevel managers, down to frontline workers writing justifications for additional positions, almost no one we’ve talked to is satisfied with the legacy government workforce planning processes in place. New processes won’t be perfected overnight, but government entities that are starting to move toward a new data-driven approach are starting to see success.
