What are your top five skills?
Can you rattle them off quickly, or did it take you a moment to think about them?
Now, what about your closest two or three peers at work? What are their strongest skills too?
Simple though these questions might initially sound, they’re actually quite hard to answer, aren’t they? But in doing so I feel they help illustrate one very important point: Often we talk about skills, but it tends to be through a theoretical lens. Far less talked about are skills as drivers of business value; or what even skills are needed most and where these skills actually come form. But these are exactly the sorts of questions we should be asking about skills – and to do this, it’s my view that HRDs all need to be better at analysing them. To achieve this, they need to start looking at what skills they have inside their organization, and then bring AI to the table:
The one thing I hear more and more is that the search for new skills shouldn’t be something that happens amongst the external jobs market. Instead it should be happening internally.
There’s a pile of research that tells us why it’s important to hire from within and leverage existing talent before looking outside the business. But the reason HRDs don’t do this so much is because they often have more data about the skills of external candidates than they do of their internal employees. This situation has to change. Every time we choose to hire externally, we miss a chance to re-engage and reinvigorate the high-quality talent within our walls.
One solution, of course, is to harness the power of artificial intelligence (AI) and machine learning.
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 the data analyzed by human subject matter experts and by an AI algorithm.
The results were stark. While 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 meant they were actionable.
This same concept can be applied to skills data. Humans weren’t meant to consume, analyze, and make decisions based on large quantities of unstructured information. But this is exactly what algorithms are designed to do. Algorithms help to identify the skills of a workforce at a granular level, providing the answers to questions such as the ones we used to begin this article. Even more impressive is the fact these answers are available in seconds, rather than after days of consideration. 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. We also open the door to a work experience that is more meaningful, tailored, and targeted to the strengths of the individual as well.
The bottom line is that skills have always held inherent value, it’s just that employers haven’t had a mechanism to measure, synthesize, and act on skill data in the past. With the increasing availability of plentiful skills 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.
One thing machine learning wasn’t designed for, however, is seeing 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.
However, our research also shows that the number one 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 than they really have. 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.
The good news is that layering-in AI helps to keep this 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.
Now that we’ve established a foundation, it’s easy to start seeing opportunities to use algorithms 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:
At one U.S.-based technology firm, its 20,000+ staff develop intelligent technologies for computers, vehicles, and other advanced systems. It wanted a smarter way to take its static skills data and bring it to life. The company’s HR technology architect specifically pointed out the value of the Workday Skills Cloud 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.
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 to me the important role of using smart technology to enable the workforce. While our research shows that six in ten 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. This guides the workforce towards the skills necessary for long term business growth and success.
Ferring Pharmaceuticals is a global medical research organization with more than 6,500 employees. The company uses Workday Skills Cloud to bring internal gigs and career options to life through a talent marketplace. This allows managers to share needs and workers to opt into opportunities when they fit their skills and interests. The AI within this system “sees” each worker, in terms of what skills they have, and what 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.
With a workforce of 1,000+ staff that was rapidly growing, one industry-leading retailer leverages machine learning for skills insights. It needed clarity around 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 by over 160% (moving from 28% to 73%). This meant key business decisions and problem solving activities could be made with more accurate and timely skills data rather than guesswork.
Over the past few years, more organizations have started to look more intently at their skills data. When these companies needed to ramp-down on the workforce during Covid-19 shutdowns, many realized they didn’t have the skill data to determine who they needed to keep.
Fast forward to today, and employers are looking for ways to measure and understand their skills so they can move forward with confidence.
The one thing that unites each of the stories above though, is that each business had a champion from the HR, talent, or learning team that decided to step up and make skills a priority. It’s this that makes the difference.
We live in a time where workers want their employer to acknowledge the skills (both technical and human), that they bring to the table. They also want to see some sort of career path based on those capabilities. 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?
