According to a report by the World Economic Forum, by 2025, the global talent shortage could reach a staggering 85.2 million workers, leading to a potential loss of $8.5 trillion in unrealized annual revenues. Industries like healthcare, technology, manufacturing, construction, transport and logistics are particularly affected, with employers struggling to find employees who possess the required skills to fill crucial roles. Adaptive microlearning can provide tangible benefits in reducing training initial and recurrent time and increasing workforce efficiency.

In today’s rapidly evolving job market, one of the most significant challenges faced by companies across various sectors is the shortage of qualified staff. This shortage exacerbates the need for efficient onboarding and continuous training processes. The traditional model of employee induction and recurrent training is often time-consuming and one-size-fits-all. It treats all employees similarly, regardless of their existing knowledge and skills. This is where adaptive learning technology comes into play as a solution that can significantly shorten the learning curve and anchor and retain crucial competencies year-round. This enhances productivity and reduces the time it takes to get and keep employees proficient.

The Impact of Adaptive Learning on Training Efficiency

The Drillster adaptive microlearning tool is an example of how adaptive learning can be applied to critical employee training. It uses a didactic AI-based algorithm to continuously assess an employee’s proficiency in different areas and fix any competence gaps immediately. This ensures that no time is wasted on redundant learning, and employees focus automatically on their personal development areas.

One of the most compelling benefits of this approach is the reduction in both initial and yearly recurrent time. Studies and practical applications have shown that adaptive learning can reduce learning time by anywhere from 40 to 50%! In an industry where every minute counts, this saved time can be redirected towards actual work, thereby boosting productivity and reducing the pressure on already overstretched teams.

In many sectors, a fixed number of hours are spent each year on recurrent training, both in the classroom or using digital formats like webinars or e-learning. But reinforcement learning after 12 months can be too quick for some employees, and too late for others. After all, the forgetting curve is an individual decline of formerly acquired knowledge and skills. Adopting adaptive learning tools with the option to calculate the right reinforcement moment for each employee and each competence, can further reduce the training time while ensuring that each employee has the right competencies at the right time to perform on the job. 

Conclusion

By implementing adaptive learning, companies can make the most of their limited workforce, ensuring that their employees spend less time in (unnecessary) training and more time contributing to the organization’s success. In a world where time is a valuable resource, adaptive learning offers a clear path to optimizing both employee development and operational efficiency.

This article provides a compelling case for the adoption of adaptive learning in sectors facing significant labour shortages, demonstrating how tools like Drillster can provide tangible benefits in reducing training time and increasing workforce efficiency.