Skills Recency Comes to Workforce Management Systems
As we speak, deskless and hourly workers are experiencing massive change — and workforce management (WFM) technology is evolving to keep up. The most impactful change on the horizon is that tech can now offer employers a more complete picture — including information about worker skills — to get the most from their people and optimize decision making.
Many executives and industry thought leaders have argued that “skills intelligence,” the information you have about a given employee’s skills and proficiencies, is the most important part of workforce planning and deployment. These insights impact — or should impact — almost every talent decision. But according to prominent industry research, many organizations are still lagging behind when it comes to improving the employee experience or delivering internal mobility, often due to lacking the relevant data or knowing how to apply it.
In this post, we take a look at the value of using skills information for everything from job matching for new hires to suggesting ideal shifts for employees to agile workforce planning, and how tech is making it more practical than ever.
Skills Recency Comes to Workforce Management Systems
Moving from skills intelligence to skills recency
Skills intelligence can be described as the available, actionable knowledge you have about an employee’s skills and competencies. Managers can leverage this information to help make life better for their workers day-to-day, while simultaneously helping plan their long-term career development. Imagine redirecting a candidate to a better fitting job before an interview even happens, or proactively suggesting a career path that they may never have considered otherwise, based on the applicable skills they’ve accrued. Managers might determine which team members are best suited for coaching others, or who would be best scheduled on a shift known for unruly customers.
It’s easy to see how with perfect access and accuracy, skills intelligence would be incredibly impactful for any kind of people management decision, but this data isn’t always so easy to come by. Furthermore, IBM Research found that “the half-life of a skill is about five years, and with more technical skills, it’s just two and a half years. Since employee skills will inevitably degrade over time, experts are pointing to the notion of “skills recency” as a more meaningful target for people leaders to prioritize. Below, we explore some crucial ways that WFM and human capital management (HCM) connect to this concept. But first, let’s examine what this perspective shift means.
Skills recency, much like skills intelligence, starts with a solid understanding of baseline skills, needs, and gaps. Ideally, we’ll view skills through the lens of how long an employee has been using them and how frequently. Then, we expand to include aspects such as proficiency level to be considered a viable resource, likely ramp-up time to reach that level, skills adjacencies and equivalencies, relative importance of skills to different roles and projects, and even skills capabilities that may be present but invisible (e.g., because they’re not associated with one’s current or previous jobs).
Overlaid onto all of this are behavioral competencies or “soft skills,” which more organizations are attempting to largely treat as if they were tangible skills. Examples include the ability to readily adapt to change, deal with ambiguity or major stress, or be an effective people connector or change champion. It can be difficult to apply a recency perspective to behavior competencies, but they’re no less important to consider. When we pull together all of these elements and nuances and combine them — typically with the help of artificial intelligence (AI) and machine learning (ML) — we elevate our understanding from traditional skills intelligence to the level of “skills science.”
Technology enabling a skills recency perspective
Below are five “skills recency” use cases to explore with current or potential HCM software vendors. By taking advantage of the most meaningful advances in AI and machine learning, you can drive much more effective workforce management.
- Determine whether the length of time an employee has used a skill is considered by the system in terms of total duration or continuous duration, as the latter should carry much more weight in determining suitability for a job or shift.
- Consider the proficiency level needed to be considered a viable resource in a new role. When combined with a recency indicator, this ‘intel’ can be quite valuable.
- Supported by AI/ML, you can determine whether employee ramp-up time in a new job or role, especially one requiring new skills, is below or above average.
- Look for the ability to extract or “scrape” certain skills off a resume along with the years those skills were in use to help form a “skills and recency baseline.” Then, with the help of AI, relate these skills to others through adjacencies and equivalencies.
- Maintain information about which skills or competencies are trending up or becoming more important to your organization, which can be hugely helpful in improving organizational agility – especially valuable during times of great change and uncertainty.
In an ideal HCM/WFM solution, you’ll be empowered to pull all this data together to provide guidance, like the UKG Skills Recency Index™, one holistic view of skills that factors in usage and frequency for a given employee — providing managers a data-driven, at-a-glance measure of employee eligibility for a shift or a new role. It can also advance learning initiatives to ensure certain skills remain ‘evergreen’, being continuously refreshed and measured to ensure optimal skills coverage across the organization.
Without skills intelligence, or better yet, skills science, workforce planning and initiatives are based more on guesswork — and when they pay off, maybe some serendipity as well. The newly emerging skills recency set of capabilities within HCM solutions is a great on-ramp for many organizations looking to transition to not only a more sophisticated and effective approach to workforce management, but also looking to amplify their employee experience efforts at a time when worker engagement levels are less than ideal.
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