Insight

Skills as a new currency

The changing nature of work demands matching skills to tasks.

Mike Mather

Mike Mather

Principal, Human Capital Advisory, KPMG US

+1 973-912-6679

Robin Rasmussen

Robin Rasmussen

Principal, Human Capital Advisory , KPMG US

+1 415-608-1139

The world of work has changed. This shift started before the pandemic with the rise of digitization and automation, and the magnitude of change has only accelerated since.

It used to be standard practice to see the organization through the lens of jobs. Jobs were the currency of organization design. Since then, things have become much more fluid and more complex. Skills are constantly evolving, and this is one reason we’re seeing less use of traditional competence frameworks. In our Rise of the Humans series, we discussed how artificial intelligence (AI) and machine learning have an impact on tasks rather than whole jobs. It’s the “arms and legs” of jobs that are automated or augmented. For example, in the insurance industry, manual validation of simple insurance claims can be conducted by chatbot and robotic process automation, freeing customer service staff to focus on more complex claims. This automation then changes the nature of the skills required by that job and results in skills further evolving constantly.

According to World Economic Forum, the shelf life of a skill is five years. Some argue it’s less than that. In this changing world, organizations need to reconsider how they look at the makeup of the workforce and start seeing employees through the lens of skills and capabilities. Not only jobs. In that way, organizations can more proactively manage these frequent changes and ensure their workforce is prepared.

How is this done?

Technology and analytics providers (such as, but not limited to, Degreed, Simply Get Results, and Workday Skills Cloud) have developed technologies to help organizations understand the capabilities and skills of their organization. Through machine learning, skills and experiences are collected and analyzed to create skill “ontologies.” This is a method of categorizing and mapping out the responsibilities, skills, certifications, and experience needed for a specific job. Skills are both explicit (e.g. described in a specification for a job) and inferred by virtue of what the employee does, how they do it and the methods used.

Skills ontology: a categorization of skills to build a common language of skills and their relationships between one another.

Modern skills ontologies don’t rely on employees inputting their skills into the core system of record. They can collect and analyze this information by sifting through many sources of data such as job descriptions, organization network mapping, and performance data. These tools allow for the ever-changing nature of jobs, roles, and organization because they’re always on and updating themselves using machine learning.

How can this be applied?

A major application of these tools is to support “talent marketplaces” for people to promote their skills and for others to define what they need to fulfill jobs and tasks. Coupled with learning initiatives, this approach to skills makes it easier to find talent throughout the organization, identify skills gaps, connect employees to projects based on skill requirements and empower employees to identify development opportunities.

L'Oréal’s Isabelle Minneci, Global Vice President of HR (Luxe Division), notes that her team is constantly evaluating which skills of the future will be necessary and which roles may require new skills to remain relevant. “We shape our learning program to make sure we can upskill and reskill our staff, so they move up in this transformation.” That includes reinforcing soft skills training for leaders and managers, as well as deploying more training around data science, digital trends, and eCommerce. “All of these skills will be absolutely key to our organization,” Minneci affirms.

Looking ahead

The technologies that help infer and document people’s skills continue to evolve to embrace more machine learning capabilities. For example, based on an understanding of the skills in sales teams, along with data on their relative sales performance; skills ontologies can recommend which teams are deficient in certain skills that are linked to higher performance. Technically, that organization would then be able to form the “perfect” sales team (at least according to AI providing a prescriptive recommendation).

Then, in time, they may see fully dynamic digital twins1 of the workforce with sophisticated AI built-in. They would then be able to subject the digital twin of the workforce to different strategic "what-if" scenarios and it would give them an indication of the consequences of the scenario so they can trial it before implementing it for real.

We don’t believe that this world is far away, and some Pathfinders are already finding their way into it.

Footnotes

  1. Digital twin – digital version of something exists in real life (i.e. flight simulator)


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