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What is Skills Gap Analysis and 4 Ways to Address it with AI

The widening skills gap in 2021 is set to grow larger as the nature of jobs changes

What is Skills Gap Analysis and 4 Ways to Address it with AI

The widening skills gap in 2021 is set to grow larger as the nature of jobs changes. We explore how you can measure this hap and address it with AI.

The skills gap across industries has reached a tipping point after the pandemic. According to a recent study, about half of  U.S. workers say they will need to acquire new skills within the next year to continue their current jobs. Many are concerned that their existing skillsets do not allow them to capitalize on potential job opportunities.   

But it’s not just job seekers who are affected by the growing skills divide, it’s crucial for organizations to address the skill shortage exacerbated by COVID-19. With 69% of employers reporting acute talent shortages in their industries, measuring the skills gap is the first step towards addressing it. 

What is Skills Gap Analysis?

Skills gap analysis is the practice of measuring the skills your organization currently has versus the skills you need. By performing a skills gap analysis, you gain insight into the key areas you need to focus your hiring and training efforts. 

Typical data points for a skills gap analysis include:

  • Employee interviews
  • Performance reviews
  • Surveys 
  • Recruitment dashboards
  • Competitor analysis

Inventorying Your Skills: The First Step to Skills Analysis

In order to determine the skills you need, you first need to access the skills you currently have. Here’s where job analysis data comes into the picture. By inventorying the skills and competencies across different job analyses, you will be able to determine the skills your organization currently has access to. 

For added context, you can reference this data against performance reports for specific roles to understand the quality of skills currently available. This will also help you identify potential micro-gaps and develop relevant interventions through training and development opportunities to address these gaps. 

AI recruitment platforms are another rich data source that you could potentially tap into to understand your organizational skills better. These solutions feature dashboards and analytics suites to help you identify the depth and breadth of your organization’s skills.

4 Ways AI Can Help Organizations Bridge the Skills Gap

At a time when knowledge workers are in high demand, organizations are shifting their focus inwards to enable their employees to build critical skills, reinvent themselves, and explore other roles in the organization.

Build Skills-Focused Talent Pipelines

A talent pipeline is a pool of internal and external candidates who are ready to fill a vacant position. A skills-focused talent pipeline can be developed using an AI hiring solution backed with I/O psychology. Instead of ranking for vanity attributes such as schools, credentials, and internships while assessing candidates, a smart AI solution can help you focus on objective data around skills and competencies. 

This helps you assess the best-fit candidates instead of sifting through a long list of prospective employees.

AI-driven Succession Planning

Employees are bound to separate from the organization eventually. So, it would help if you planned on preparing for a replacement before it happens.

Succession planning doesn’t just relate to identifying the right successors but also requires employers to groom them on the essentials skills to qualify for the role.

AI and machine learning (ML) can help organizations with succession planning in the following ways:

  • Identify the necessary skills and knowledge required to succeed in the leadership role
  • Identify the right successors using data (i.e., without human biases) and create a talent pipeline of future leaders
  • Assess the successors and suggest necessary skills, knowledge, training programs, and courses required to bridge the talent gap

Improve Learning and Development (L&D) Programs

Continuous learning is an essential part of the knowledge economy. Employees have to upgrade themselves to grow in their field of endeavor. Massive open online courses (MOOCs) promised a huge potential to help people grow, but they haven’t been successful in living up to their potential.

AI-based learning solutions assess the talent based on their role, skills, knowledge, interests, behavior, etc., presenting them with a personalized learning experience. ML can analyze the individual learning style and recommend content in the user's most comfortable format.

When an employee moves to a new role, they can take the required courses to improve their skills and knowledge. Rather than following a rigid curriculum, learning assessments understand the learner's knowledge and change the structure of lessons accordingly.

Predictive Analytics to Improve Talent Mobility

The time and resources required to replace a position make employee turnover a serious concern for organizations. Oftentimes, employees leave organizations to pursue better challenges within their fields. By implementing predictive analytics models, employers can identify at-risk employees and address the reasons behind potential turnover.

For instance, employees looking for more meaningful opportunities can be provided with redesigned job roles in alignment with larger business objectives. The talent team can also use performance data to understand what employees are looking for in the new role and help them grow by providing similar career development opportunities.

Predictive analytics can further facilitate internal mobility by identifying what skills employees need to succeed in the new role. Accordingly, necessary L&D programs can be introduced to upskill employees.

Conclusion

While working toward a more proactive paradigm for addressing skills gaps, we can’t lose sight of the importance of retraining and upskilling to fill new gaps as they arise. It’s a complicated problem that will require multiple solutions, and the challenge will continue to shift and grow as the needs of employers, and the demands of consumers, change.

We’re still working toward creative options, but given the importance of talent transparency, it’s likely that AI-powered talent assessment will play an important role and, perhaps more crucially, act as the first step toward a new paradigm for managing skills gaps and skill development.