The post-pandemic talent landscape is proving interesting for modern businesses.
On the one hand, some big companies are cutting back on their workforces and trying to rein in remote work and other employee-driven requirements. On the other hand, other companies are struggling to fill their talent needs, and millennial and Gen Z employees still aren’t afraid to leap to better opportunities. Plus, artificial intelligence (AI) is upending how organizations look at productivity.
Amid all this, the skills gap is real for many businesses. Qualified talent is difficult to find, and it’s also difficult to train. However, without sufficient skill sets — as well as simply knowing which skill sets are needed for success—companies can become inefficient, and existing employees can suffer burnout and quit, which only exacerbates the problem.
Understanding the skills gap is crucial for productivity, growth, and employee retention. Modern digital learning, complemented by AI technologies, can help.
Assessing Skill Sets
Before a business can address the skills gap within its workforce, it must accurately assess the skills base it already has. This assessment, though, isn’t the easiest task.
Managers, even those who direct small teams, are busy enough without trying to map out the skills of each of their reports completely. Sure, managers may know who’s good at what, but that doesn’t paint the complete picture necessary for defining the skills gap.
True competency mapping, even as it’s evolved over the years, takes time and is an enormous undertaking. Self-assessments can help, but you still rely on employees to find the time to do them. Stretching assessment efforts across the business requires coordination—so much so that companies may feel they’re constantly playing catch-up to get a better view of their skills gaps.
Organizations that manage to map skills still face another challenge: constantly changing skills parameters effectively. Markets change. Solutions used by employees change — demand for certain skills changes. Consider the COVID-19 pandemic and all the new workplace skills — video meetings, remote work policies, safety protocols, digital security measures, and so on — that emerged and became quickly necessary. As a result, pinning down a skills gap isn’t a one-and-done initiative but instead is continuous.
Digital training can offer a more efficient way to assess skills. But before technology can help, an understanding of how employees learn is necessary.
How People Learn and Enhance Skills
In a perfect corporate world, every employee would receive one-on-one skills training personalized to their needs. As impractical as that scenario is, it’s still the best way to deliver the most impact. Instead, organizations are forced to swing in the other direction: live training sessions with dozens or hundreds of employees, three-ring binders full of reading that workers are expected to know, and a one-size-fits-all approach to learning no matter the employee, their role, or their learning style.
Digital learning would seem to answer this conundrum, but sometimes it isn’t. The technology saves companies time and resources, but if they aren’t personalizing training content, going digital might not make much of a difference.
For example, if everyone at a business gets the same digital training module, many employees will become bored by things that don’t apply to them, possibly causing them to tune out the parts that should matter. Or, if an organization is fixated on completion, it might fail to assess the impact of the training and not derive any benefit other than a checked box.
Training to teach or enhance skills requires a focused, personalized approach—organizations simply can’t throw everything out there and hope something sticks. Applied properly, digital training can provide this approach with a big assist from AI.
Most of the AI news that has dominated the technology space over the past several months has centered around ChatGPT and its impressive—though far from perfect—capabilities. ChatGPT’s emergence has overshadowed existing, dedicated AI tools that have been helping specific industries, including the training industry.
Two use cases are already showing AI’s impact. The first application is data analysis. Digital learning creates vast amounts of data that can be used to assess user effectiveness, impact, retention, and progress. However, the data often is so vast that manually extracting actionable insights is too difficult for even the most staffed training teams. Results—and subsequent remediation—are overlooked simply because humans are bound by, well, their human limits.
AI technology gives training teams a way to examine learning data in a fraction of the time and then automatically launch actions based on the data. For example, a dedicated AI training solution can identify certain areas of compliance an employee may be struggling with based on the answers that the employee gave or how long it took them to answer and send follow-up materials, such as learning aids, videos, and micro-learning opportunities, straight to their inbox.
The employee’s manager also can be alerted, and if entire departments are struggling with a concept, the training staff can adjust the strategy to close the learning gap.
A second training application of AI technology allows organizations to better understand their skills gaps. AI can scrape the internet for hundreds, even thousands, of job listing descriptions to see what other companies are seeking from similar roles. Besides showing what organizations value from their employees, this information can reveal what a business might be lacking in its own workforce. Compiling this data is massive, possibly expensive, undertaking for a training staff but is made much easier and less costly with AI.
True Transformation Through Digital Learning
For many companies, hiring out of a skills gap simply isn’t feasible. They need to supplement smart talent acquisition with effective training of current employees to get the most out of their workforce’s potential. Digital learning offers a way to not only assess current skills and identify gaps but also boost those skills and teach new ones.
However, not all digital training strategies are equal. As already stated, training that emphasizes completion over impact and volume of concepts over depth may fail to adequately teach users what they need to thrive in their roles and maintain compliance.
A better approach embraces adaptive learning in which the training software adjusts to the responses and behavior of users during a session in real-time. For example, if an employee is continually struggling with an important concept—as evidenced by their answers to in-training questions — the software can create a new learning path so that they move closer to understanding the concept.
True transformation occurs because employees aren’t getting off the hook for learning, but they aren’t getting shouted at either.
Add AI into this equation, and training departments can see what areas of learning require more attention and adjust their short — and long-term strategies accordingly. Organizations can feel more confident that employees are truly learning — and subsequently applying — the skills necessary to be efficient and effective.
And perhaps most importantly, companies can address their skills shortages from within.
Featured Image Credit: RDNE Stock Project; Pexels; Thank you!