In today’s rapidly evolving job market, a unique trend is emerging: while core skills remain relatively stable, the ways in which they are applied are undergoing significant transformations.   

For instance, the legal sector is witnessing new tech-oriented roles such as Open Source Compliance, and the world of science is embracing positions like Bioethicists.  While rooted in traditional disciplines, these roles require a modernized application of foundational knowledge.  A parallel can be drawn to the field of Machine Learning.  Here, there’s a noticeable shift in preferences from classification models to transformer models, even as the discipline grows.  This evolving landscape poses dual implications for businesses and educators.  On one hand, there’s a potential pitfall: employers might prioritize hiring based on trendy keywords without fully grasping the underlying competencies.  On the other, it opens up an exciting avenue for our workforce’s targeted reskilling and upskilling, preparing them for the future while building on their existing expertise. 

After consulting with labor market economists and AI professionals, it’s evident that AI’s influence on the job market varies across job categories.  For clarity, I’ve categorized these into: Disrupted Jobs, Collaborative Automation Jobs, and Innovative Jobs.  

Disrupted Jobs include roles like office support or basic customer service, which face significant automation threats.  Collaborative Automation Jobs, akin to DevOps roles, blend human skills with automated workflows, reminiscent of McKinsey’s ‘midpoint automation’.  Meanwhile, Innovative Jobs represent emerging roles like Gen AI Architect and Open Source Compliance, signaling the birth of new career paths in the AI era. 

The following is a plot of the diverse growth trajectory that a company may experience.  (disrupted jobs will decline, collaborative Automation jobs will grow at a slower rate, and innovation jobs will grow at a brisker pace.)

A crucial aspect to consider in any workload is the prevalence of suboptimalities in its management of Automation.  As a result, Collaborative Automation jobs need to be approached cautiously and not expected entirely to be replaced by AI.   To illustrate, let’s delve into the code-writing.  Interestingly, in the coding process, the actual act of writing the code constitutes a mere 10% of the overall effort.  The bulk of the work— a staggering 90%— revolves around ideation, brainstorming, refining, and managing the changes and iterations that come with the development process.  Such insights bring forth an essential understanding: while Automation holds transformative potential, it’s imperative not to overstate its capacity.  We must recognize and respect the intricate human-centric nuances that dominate many work processes.  (this graphic is adapted from a Planview presentation and not a study done by Draup.)

A Google leader observed, “The AI to manage AI is insanely human.”  This quote is something we should remember as Workforce Planners, Recruiters, and L&D Leaders 

Here is a simple table from a Talent Acquisition standpoint that may be helpful