How talent intelligence solves the employee redeployment problem
- 7 Min Read
New AI-based platforms provide crucial insights into current employee skillsets, allowing organizations to flexibly deploy workers into areas experiencing labor shortages.
Our organizations have a dire need for talent intelligence. According to the U.S. Chamber of Commerce, organizations in 2023 are facing unprecedented challenges trying to find enough workers to fill open jobs. Right now, the latest data shows that we have 9.8 million job openings in the U.S., but only 5.9 million unemployed workers.
In other words, there are plenty of jobs but not enough workers to fill them. The U.S. Chamber suggested that if every unemployed person in the country found a job, we would still have around four million open jobs.
What’s the reason for the current labor shortage? In a new paper for the U.S. Chamber, Stephanie Ferguson wrote that at the height of the pandemic, more than 120,000 businesses temporarily closed, and more than 30 million U.S. workers were unemployed. Since then, job openings have steadily increased since January 2020, while unemployment has slowly declined.
Given that these shortages are likely to persist in the face of falling fertility rates, organizations would do well to hang on to the workers they have. However, this can be problematic when business needs change and you aren’t certain your existing workforce can develop the skills to meet fresh challenges. Fortunately, a form of artificial intelligence (AI) called deep learning can help a great deal with issues of redeployment and internal mobility.
Introducing deep learning and talent intelligence
Deep learning is a type of machine learning inspired by the human brain. Deep learning algorithms mimic human conclusions by continuously analyzing data with a given logical structure. This structure encompasses multiple layers of algorithms called neural networks.
Deep learning algorithms can’t predict the future or give answers that a human would agree with 100 percent. Yet they are still extraordinarily valuable. If you’ve ever used a modern search engine and the results have amazed you or if your streaming network of choice recommended you the perfect show to watch, you have experienced the power of deep learning.
The main challenge of deep learning is data – specifically, the massive amount required. The talent space alone needs billions of data points about people, career trajectories, skills, and experiences.
But while AI’s reach was once limited by computing power and the availability of data, that’s no longer the case. Today, global neural nets can identify more than a million skills across the world’s eight billion people.
Using this data, engineers are developing deep learning algorithms to determine the best answers to a defined class of questions. In the case of internal mobility, one question might be: who within my current workforce is qualified now or can we easily train to move to understaffed Department X?
How talent intelligence solves skills shortages
Talent intelligence uses deep learning to define roles and use internal company data as well as external, local, and global data to help decision-makers optimize talent decisions.
Talent intelligence dynamically self-learns until it fully understands the availability, maturity, relevance, learnability, and evolution of skills within specific organizations and the larger market. Its resulting analyses provide leaders with complete visibility into the skills of their existing workforce and the training and hiring required to keep pace with industry developments.
These platforms break down a person’s profile and history into skills, suggesting how the company might redeploy those skills. It can prompt a hiring manager to think, “this may be the job they do well, but is there a way to put their skills to work for us elsewhere?” If they have an open requisition, deep learning can show them if there’s a matching employee within the company – perhaps in a division the manager never thought about.
The technology can encourage curious employees to view internal roles that are a match for their capabilities. Maybe the new role is appropriate now, or perhaps it’s aspirational. If the latter, talent intelligence can guide employees in pursuing coursework or training to prepare sufficiently for that role.
Talent intelligence and redeployment in action
These ideas become clearer with specific examples. So, let’s say an employee sees an internal role open in the finance department. Deep learning sees that their skills are a good match, but they’d be a stronger match if they took a tax-related course unique to that company’s industry. That employee then knows how to move from job A to job B, and even find the right course in the company’s learning management system.
Or let’s suppose there is no open role, but a lower-level designer wants to become a design director in their company. This employee can examine the paths other design employees took to get to the director role, including the skills and characteristics design directors have and the coursework that they have taken. The employee can then connect to the learning management system and sign up for classes to help them move from designer to director.
In other words, deep learning calls out potential by looking at people with similar skills and what they were able to accomplish next in their career. Thanks to a global data set in which you can analyze thousands of people who acquired new skills and were subsequently successful in a new role, you can infer that an internal employee can also learn these skills and do well in this new role.
A final way talent intelligence helps with retention involves flight risks. It can tell you how long an employee has been in a certain role and assess whether that person might be itching to move on. Organizations using talent intelligence don’t have to wait for the exit interview to determine that an employee quit because their career had stalled. They can preempt this by proactively redeploying this employee to meet a new challenge both for themselves and the company.
What you risk when you get internal mobility wrong
We tell employees to drive their own careers, but how can they do that when they don’t understand what it takes to get a more appropriate job in another department or location?
At most organizations, internal mobility is limited to posting jobs in-house (which most people don’t see) before sourcing from the outside. But especially in large organizations where matches across departmental lines are less evident, the path to a desirable internal move is unclear.
This is too bad, because people often quit for new challenges, and sometimes those people who quit might have found those challenges in the form of open jobs in their own companies!
And as we know, turnover is expensive, costing businesses alone nearly $1 trillion annually. According to a 2021 U.S. Bureau of Labor Statistics report, voluntary turnover that year was as high as 25 percent. We often think that turnover costs and hiring costs are one and the same, but there’s more to it. The loss of knowledge and customer relationships hurt too.
Alexandra Levit is a workforce consultant and futurist. Based in the US, she is also a columnist for the Wall Street Journal, co-author of Deep Talent (Kogan Page, 2023) and author of Humanity Works (Kogan Page, 2018). She consults for various organizations including the U.S. government, has spoken at hundreds of organizations across five continents and was recently named to the Thinkers50 Radar List. She is an expert on talent intelligence and recruitment and has helped companies retain talent, fill skills gaps with the use of AI and highlights that talent intelligence and technology can be the answer to solving talent management issues.