The United Nations Educational, Scientific and Cultural Organization published an interesting study last year on the impact of AI on skills building. The authors made some interesting and deep points of observation. One key finding is the increasing body of research that indicates technology and globalization are causing a division in the workforce, leading to a concentration of either high-skilled or low-skilled jobs. This phenomenon is often described as ‘polarizing’ the workforce or ‘hollowing out’ the demand for skills that are neither high nor low but intermediate (as discussed by researchers such as Autor in 2010, Bárány & Siegel in 2015, Brown in 2016, and Goos, Manning & Salomons in 2014).

One of the major concerns highlighted in this study is for workers with intermediate skills. The tasks they typically engage in are routine, making them particularly vulnerable to being replaced by technologies like artificial intelligence (AI) and robotics, which offer significant cost savings to employers. However, it’s crucial to recognize that this trend pertains to the current definition of intermediate skills. When analyzing job roles instead of wage percentiles, it becomes evident that intermediate-level occupations persist. This observation underscores the evolving nature of these jobs, as pointed out by McIntosh in 2013. Source: – (McIntosh, S. 2013 Hollowing-out and the Future of the Labour Market. BIS Research Paper No.134. Department for Business Innovation and Skills, London.)

Therefore, the skills demanded by the job market are undoubtedly shifting. However, this does not render intermediate-level education, such as technical and vocational education and training (TVET), irrelevant in the contemporary digital era. The demand for certain skills may change, but intermediate-level skills remain a significant place, especially as they adapt and evolve to meet the new requirements of our increasingly digital world.

During a recent episode of Lex Fridman’s podcast, Jeff Bezos discussed the importance of periodically reevaluating and challenging the metrics used in business. He illustrated this with the concept of ‘paper cuts’ – small, seemingly insignificant customer issues. Bezos pointed out that in the grand scheme of measuring overall customer satisfaction, these minor deficiencies often go unnoticed. However, they can accumulate and significantly impact the customer experience. Therefore, Bezos emphasized the necessity of developing new sets of metrics at regular intervals. This approach ensures that even the smallest customer concerns are addressed and rectified, contributing to a more comprehensive and accurate assessment of customer satisfaction.

Maybe we need to rethink the importance of how we calculate talent pool availability as Recruiters and Workforce Planners.  Here is an equation that we are thinking of at Draup (Any feedback is welcome as we think through this further)

To represent the concept of expanding the talent pool through adjacent intermediate skills mathematically, we can consider a few key variables and their relationships. Let’s define these variables first:

1. Talent Pool Size (TPS): The total number of individuals in the talent pool.
2. Number of Intermediate Skills (NIS): The number of different intermediate skills that may be relevant to the talent.
3. Adjacent Skill Expansion Factor (ASEF): A multiplier representing the extent to which adjacent intermediate skills can expand the talent pool.
4. Digital Literacy Integration (DLI): A factor representing the inclusion of digital skills into the intermediate skill set. (could be a company-specific factor depending on the digital literacy maturity)
5. Skill Enhancement Factor (SEF): A multiplier that reflects the increase in talent pool size due to skill enhancement or upskilling efforts. (Captures skills that may not be captured by immediate adjacency)
6. Soft Skills Integration (SSI): A factor for the inclusion of soft skills like problem-solving, adaptability, etc.

The mathematical representation can be formulated as follows:

New Talent Pool Size (NTPS)=TPS×(1+NIS×ASEF+SSI+SEF)

In this equation:

• 1 in the formula represents the initial talent pool size in the current way we calculate.
• NIS × ASEF accounts for the expansion of the talent pool due to recognizing and developing adjacent skills.
• DLI and SSI are added to the equation to represent the increase in talent pool size due to the integration of digital literacy and soft skills, respectively.
• SEF reflects the increase in talent pool due to skill enhancement efforts like training and upskilling. (Captures skills that may not be captured by immediate adjacency)

This formula provides a simplified way to conceptualize the impact of expanding and enhancing skill sets on the size of the talent pool. In practice, these factors can be more complex and interdependent, and the exact values for ASEF, DLI, SSI, and SEF would need to be determined based on specific organizational or market contexts.

We understand that in numerous situations, having access to information about the directly relevant talent pool is crucial. However, there are specific instances where this type of information can be particularly useful. 

Draup undertook a project focused on reskilling and transitioning individuals from the role of business analyst to data engineer. The typical skill set of a standard Business Analyst possesses the appropriate level of intermediate skills suitable for scaling up. However, targeted training is necessary to effectively transition into a Data Engineer role. As AI initiatives continue to expand, the complexity within the field of data engineering is anticipated to increase, making such targeted skill development increasingly important.