What are your top five skills? Can you rattle those off quickly, or did it take you a moment to think about it? What about your closest two or three peers at work — what skills do you think are their strongest?
This exercise is one that I often use to help us realize the difficulty (and importance) of being able to clearly measure the skills in a workforce.
Skills as a discussion point have been explored and examined, but often through a theoretical lens. But it’s important to dive into skills as a driver of business value through a more practical perspective, offering examples and ideas to help you tackle this challenging — but valuable — area of talent.
Skills As a Currency
Currency is best understood as a medium of exchange that can be used among many people. Skills fit that definition since employers are willing to trade money in exchange for the skills that new hires bring to the table. However, we’re increasingly hearing from enterprise talent leaders that the search for new skills isn’t happening in the external market — it’s happening internally.
There’s a host of research and data that point to why it’s important to hire from within and leverage existing talent before looking outside the business, but one of the reasons we’ve identified that companies don’t look internally is because they have more skill data on external candidates than they do internal employees.
This situation has to change, because every time we choose to hire externally, we miss a chance to re-engage and reinvigorate the high-quality talent within our walls.
That’s where artificial intelligence (AI) and machine learning come into play. These algorithms help to identify the skills of the workforce at a granular level, providing the answers to questions such as the ones we used to begin this article. Even more impressive, those answers are available in seconds, rather than after days of consideration.
That may sound like a lofty goal, but employers are already making strides forward using these technologies. The senior manager of HR solutions and transformation at Ferring Pharmaceuticals said, “We can now democratize project and learning opportunities, so everyone in the company can benefit from them.”
Bottom line: Skills have always held inherent value, but employers haven’t had a mechanism to measure, synthesize, and act on skill data in the past. With the increasing availability of plentiful skill data, machine learning algorithms, and a business case for reskilling to meet changing organizational demands, the time is right to tap into skills as a valuable resource for organizational growth and a means to adapt to change.
Bringing AI to Bear on Skills Data
A couple of years ago, our team ran an experiment to see if humans or AI were better at analyzing HR data. We gathered 1,000 worker responses to open text questions in a survey and then had that data analyzed by human subject matter experts and an algorithm. The results were a stark contrast: While the humans could pull out a few key themes at a high level, the algorithm could pinpoint the specific challenges faced by female engineers or salespeople working in the Midwest. In other words, the outputs could be highly granular and specific, which means they were actionable.
This same concept applies to skill data. Humans weren’t meant to consume, analyze, and make decisions based on large quantities of unstructured information. Algorithms? That’s what they were designed to do.
What machine learning wasn’t designed for, however, is the bigger picture. Humans have an edge in what our research team calls the human skills of work: creativity, collaboration, curiosity, compassion, and critical thinking. Those allow us to think through challenges, determine new solutions, and attack problems with a different perspective.
By using machine learning to analyze skill data, we unlock the opportunity to leverage the full breadth and depth of skills that the workforce offers, but we also open the door to a work experience that is more meaningful, tailored, and targeted to the strengths of the individual, as well.
Our research shows that the No. 1 way employers evaluate the skills of their people is through manager observations. While this isn’t inherently bad, it does open up the opportunity for bias to creep in.
For example, if a manager and direct report get along really well, the leader may assume that the employee has more skills. Alternatively, if they sometimes challenge each other when it comes to ideas and decision-making, it may be assumed that the employee has fewer skills or lower proficiency.
Layering in AI helps to keep that human bias from overshadowing someone’s career opportunities, performance appraisals, pay raises, and other aspects of employment tied to skill evaluations. That’s what makes the unbiased nature of an algorithm so important.
Practical Use Cases for Applying Algorithms to Skills Data
Now that we’ve established a foundation, it’s easy to start seeing opportunities to use an algorithm for skills analysis. Each of the following stories highlights a real company using algorithms and intelligent technology to support their talent and business objectives with a key focus on skills.
1. Supporting Talent Matching for Faster Hiring
At one U.S.-based technology firm, the organization’s 20,000+ staff develop intelligent technologies for computers, vehicles, and other advanced systems, so it’s no surprise that the company wanted a smarter way to take its static skill data and bring it to life.
The company’s HR technology architect specifically pointed out the value of technology as it pertains to reviewing a candidate’s experience and education and suggesting roles for them to speed up the recruiting process. This matching serves the recruiting team with a faster process, but it also allows candidates to see a more personalized and tailored experience, as well.
2. Enabling Organizational Agility
In a discussion with the chief learning officer for DXC, a consulting and business services firm with a global workforce of 130,000+ staff, he explained the important role of using smart technology to enable the workforce.
While our research shows that 6 in 10 workers do not get any direction on the skills they should be developing from their leaders, DXC uses technology to curate a targeted list of skills-related content in their learning systems to guide the workforce towards the skills necessary for long term business growth and success.
3. Democratizing Career Opportunities
Ferring Pharmaceuticals is a global medical research organization with more than 6,500 employees. The company uses technology to bring internal gigs and career options to life through a talent marketplace, which allows managers to share needs and workers to opt into those opportunities when they fit their skills and interests.
The AI within the system “sees” each worker, which skills they have, and which related skills they might need to acquire. It can then offer up relevant gigs as they arise, giving the workforce more control. This puts more power in the hands of the employees by serving them curated options based on their existing skills.
4. Innovation and Rapid Problem-Solving
With a workforce of 1,000+ staff, one industry-leading retailer leverages machine learning for skill insights. The company’s leadership ran into a hurdle as the company was growing, realizing that they needed clarity into the skills of the workforce if they were going to tap into the right person with the right skill at the right time.
By implementing a machine learning-based skills technology, the company was able to increase its skills clarity from 28% to 73%. This meant that key business decisions and problem-solving activities could be made with more accurate and timely skills data, not guesswork. This goes beyond just solving a talent issue to enabling faster business innovation, growth, and adaptability.
It’s Time to Step Up Our Game
Over the past year, more organizations than ever before have started looking intently at their skills data. When these companies needed to ramp down on the workforce during Covid shutdowns, many realized they didn’t have the skill data to determine who they needed to keep.
In addition, new technologies and automation are changing the fundamental skills people need at work, driving a reskilling revolution. Employers are looking for ways to measure and understand their skills so they can move forward with confidence. In each of the stories highlighted above, the business had a champion from the HR, talent, or learning team that decided to step up and make skills a priority.
The truth is that we as employers truly desire a way to connect more deeply with our people, offer them meaningful work, and understand how to leverage their best talents and abilities. On the other hand, workers want their employer to acknowledge the strengths (both technical and human skills) that they bring to the table, and they want to see some sort of career path based on those capabilities.
In a highly competitive hiring market, every company is looking for smarter ways to hire, engage, and retain their people. What better way is there than truly seeing the strengths that each member of your candidate and workforce population has, and then finding ways to recognize, emphasize, and prioritize those skills?