Labor Readiness

We had noted previously that the benefits businesses expect from generative AI range from increasing efficiency and productivity to reducing overall costs and enhancing relationships with clients. We had also noted that the main enabler for any major deployment will be the human factor and that investment in human capital is a necessity for any business considering developing or deploying generative AI systems.

However, surveys show that employees are not able to meet the skills gap in the local labor market or attract the amount of talent they need. In other words, the existing labor market has not kept up with the technological changes and the skills necessary for these technologies, and businesses are having a very hard time meeting their talent demands with the current short supply. This is necessitating employers to turn to internal skills-building activities to close the skills and talent gap.

The bigger question is if the changes will be in place, reducing the pain and drain of the transition. Employers expect almost half of the workers' skills will be disrupted within 5 years, and they also forecast that six in ten workers will require training before 2027. While some may be reskilled and be ready to meet the challenges of the evolving labor landscape, forecasts also show that more than a quarter of the workforce will not have access to the training needed.

Such a gap means that the supply of the workforce does not overlap with the demand of businesses. This can also have cascading societal impacts. The skill gap in the labor market will not be solved by itself, which means that without investment, businesses will keep competing for the same talent pool. This might be an attractive proposition for the small portion of workers with necessary skills, as it would drive their income higher. However, it is neither attractive nor preferable for the industries or society at large. For example, the pandemic has shown us the precarious situation many countries found themselves in with healthcare and education workforce.

A key message from World Economic Forum leaders regarding the impact of AI and generative AI is that investments in industry cannot succeed without equivalent investment in people. Instead of trying to attract the same small talent pool of workers with necessary skills, public and private organizations need to invest in their employees to expand the labor market. In fact, companies leading in AI demonstrate a significant lead in how they approach reskilling and investment in workforce. They tend to invest to expand the pool of AI talent and build employees' related skills across both technical and non-technical jobs.

Skills mismatch can be narrowed down by both the educational system and by employers. While the educational system slowly transforms itself, employers can create more agile responses to skill mismatch and needs. Surveys show that the interest in AI training is similar across industries, whereas there is a difference in age levels. Junior employees seem to show more demand for AI training as they may be more excited about the opportunities and also more concerned about AI automation.

However, reskilling needs to be a cooperative effort too. While almost everyone agrees on the need for labor readiness and the need for reskilling, there may be some mismatch between what employees prioritize versus what employers think are the most important reskilling areas. This is not to say that the differences are incompatible, but to showcase that when organizations are thinking of educational programs, there needs to be a wider and holistic discussion to avoid employee dissatisfaction and discontent.