Automation has been gradually transforming the workplace for years (think Excel spreadsheets or chatbots). As artificial intelligence (AI), machine learning and deep learning systems that can learn from each other become more prevalent and smarter (think Alexa or IBM Watson), they continue to replace more manual, repetitive job tasks. Consequently, automation and robots are changing more jobs globally at breakneck speed.
A McKinsey Global Institute report suggests that between 400 million to 800 million jobs worldwide will be lost due to automation by 2030. The report claims that the U.S. could lose between 16 to 54 million jobs by 2030. The pace at which robots are entering our workforce is staggering. Oxford Economics expects robots and automation to replace 20 million (8.5%) global manufacturing jobs by 2030.
Keep in mind that these predictions came before anyone predicted the Covid-19 pandemic or its impact on our workforce. The pandemic has made the need for digital transformation and automation more urgent as the critical need to work from home, physical distancing and contactless become the new normal.
The pandemic has supercharged digital transformation and the future of work.
While many of us are at home making difficult adjustments to our personal lives to contend with the pandemic — companies, governments, federal agencies, academia, policy-makers and public and private health and safety organizations around the globe are placing their digital transformation plans on the fast track to prepare for the “future of work.”
As a result of the pandemic, the “future of work” is upon us now, and there’s no time for stagnation in this great time of change. Businesses are either stepping into the next digital era now, or they’re being left behind.
While workforce automation job loss statistics paint a doomsday picture of robots quickly replacing workers, it’s left many thinking that robots will one day completely replace the need for humans in the workplace. However, experts predict just the opposite. The path toward digital transformation is shining bright with the emergence of new jobs, new job types and elevated job roles. And it’s no surprise the demand is coming from AI.
AI is replacing jobs while creating new ones and new job roles — profound benefits to the global economy await their arrival.
According to the World Economic Forum’s Future of Jobs Report 2018, 133 million new jobs will be created by AI by the end of this year (2020). Gartner reports that the business value created by AI will reach $3.9 trillion in 2022. While IDC recently predicted that global AI spending will reach an estimated $97.9 billion by 2023. Either way, investment in AI will be a prominent market trend. IDC also predicts the number of software robots (i.e., digital workers) entering the workforce will increase globally by 50% next year. These digital workers and new AI jobs represent the new division of labor between humans and machines (and our evolving co-dependency that I wrote about last year).
The rapid rise of robots means that we’re witnessing a major shift in the types of jobs that make up our workforce — while many jobs and job roles are being eliminated, new ones are being created, and the vast majority of them are designed to improve AI or to use the results of AI (i.e., perform judgment). These new jobs will focus on more complex problems requiring higher-level critical thinking and analytical skills, and they will be needed across industries. New job type examples can be found in LinkedIn’s 2020 Emerging Jobs Report, which placed “Artificial Intelligence Specialist” as the number one emerging job, holding strong with a 74% annual growth over the past four years.
AI will always need humans — here’s why.
Machine learning and deep learning (the subsets of AI) require training data and models to learn and improve on their actions and predictions through the use of applied algorithms and inferences. Machine learning still requires human training — after all, we’re just getting started. Conversely, deep learning uses artificial neural networks with representation learning.
Without getting too technical, this means deep learning can learn with or without human intervention — a reality that strikes fear among even the most technically savvy. But it also places emphasis on why humans are still needed in the workforce. Machines are great at making predictions, but humans are still needed to train the machines because machines are not good at making judgments.
Humans are still needed to address some of the most critical issues facing AI: removing bias and evangelizing ethics. As we head into the next digital era, today’s workforce needs humans now more than ever because only with a sufficient amount of the highest quality data (fed by humans to machines) can AI improve accuracy and deliver on the promise of abundant economic and societal benefits.