The future of Blended Workforce

This blog post was featured in the April issue of HR Strategy & Planning Excellence.
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Before the “information age” of today’s contemporary society, we saw the birth of mass production with the Industrial Revolution of the late-19th and early-20th centuries. This advent in manufacturing brought with it Henry Ford’s creation of the assembly line: the first sign of what would eventually (and inevitably) evolve into the workforce of today — a blend of human workers and machine learning. 
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Over the past century since Ford’s invention, the global workforce has steadily grown to more broadly adopt aspects of machine learning and artificial intelligence (AI) in tandem with the factor of human capital, eventually creating what we today call the “information age” of the 21st-century. In today’s world, incorporating AI and machine learning with a human workforce is as commonplace as the smartphones we carry around in our pockets, but this also raises the question of what the global blended workforce of human employees and AI will look like in the years to come.
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AI has been replacing humans for decades
AI has been replacing humans for decades by replacing human workers with machines and AI is nothing new. One interesting trend I have witnessed is the rise in the number of major global firms hiring increasing amounts of software engineers in order to program machines and AI to fulfill jobs once performed by human workers, with everything from complex financial formulas to infrastructure development being performed by a blended workforce. In fact, an estimated 400,000 jobs in U.S. factories replaced human workers with machines between 1990–2007. With the onset of the COVID-19 pandemic last year, another estimated 40 million jobs in the U.S. were lost, with more than 40% of those jobs expected to never return and/or have the potential to be filled — to some capacity — by AI.

Just as every major economic shift in history has helped flesh out the “persona” of its time (e.g., the Industrial Revolution of the late 1800s or the “information age” of the early 2000s), these economic pivots inadvertently create new social dynamics that influence the ways businesses and their teams operate. It is my firm belief that, over the coming years, we will steadily watch as the “information age” of today’s world and workforce evolves into the “automation age” for one simple reason: trends. In particular, trends regarding venture capital (VC) investments.

For example, Elon Musk founded Tesla in order to create a line of electric vehicles (EVs) that are more affordable and accessible to the general public. Despite entering an extremely saturated vehicle market in the U.S., Tesla has gone on to receive over $20 billion in VC investments since its founding in early 2003. Tesla is also a prime example of a company that has focused on hiring a greater amount of software engineers and developers in order to continue improving upon the software used in their EV fleets.

While EVs are not yet as commonplace as traditional gas-powered vehicles, the example of Tesla in this case also raises an additional question regarding the evolution of the role of car mechanics: by the time EVs are commonplace, or even outnumber traditional gas-powered vehicles, how effective will a mechanic be if they are not up-to-date on how to best service an EV if they do not evolve their skill set?
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Humans must adapt to automation
​Humans must adapt to automation to make a connection between this example and the realm of finance, think of derivative options in asset trading. In this case, the mechanic’s career (or even the future of a major vehicle manufacturer such as Ford) is the derivative option, and the mechanic’s skill set (or the vehicle manufacturer’s ability to adapt to new trends) is their asset. 

If the mechanic does not evolve their skill set, their options (i.e., career) will begin to expire. This means that the mechanic will have to remain up-to-date on trends regarding EV service and maintenance and adopt accordingly. For a major vehicle manufacturer, this means that they will not only have to remain updated on EV trends, but also formulate their business strategy to include hiring additional software engineers in order to create their own proprietary software for their future EV fleets.

The example of Tesla and EVs is only one of potentially hundreds or more, but the point is this: as more companies and global firms adopt more aspects of automation in their workforce, the workforce itself simultaneously has to adapt in order to remain relevant alongside increased automation. The best way that today’s blended workforce can do this is to stay as updated as possible on the trends of VC investments made over the next 3-5 years, make note of how much VC is being funneled into which industries and companies, and why.

Human beings are naturally resistant to change, but along with death and taxes, change is one of the only certainties everyone will face throughout their life for better or worse. But in the case of this article, fighting against that change — especially as a company or business — will likely make your career irrelevant in the long run.

There are plugins coming out for tools such as GPT-3 for Figma, a prototyping wire-frame, where anybody can type in what type of screen he or she wants, and that screen will automatically create a user interface. The future of the world will largely revolve around machine learning, and this may change most UX designers to become graphic designers. It looks like GPT-3 for Figma will be coming out sometime next year, and as of now, it looks like the tool will be a commercial subscription. We posted a video, but this video is credit to Jordan Singer, and this tool is not yet available to the public, but it's interesting to imagine what the future holds of the economy, due to automation. Lastly, we do believe that this is the very definition of disruptive innovation.

Most data scientists may not be leveraging the proper models for the optimal business solution. If your firm uses python, a general purpose programming language, we highly suggest you leverage scikit-learn, a machine learning library in python. Likewise, the algorithm cheat sheet below should ensure that the proper models are being used for the proper business problems with the correct amount of data. Does your data science team know this graph below?  Lastly, we pasted an image of a datacamp graph that provides source code for machine learning tutorials.