The world is in the midst of a historical turning point. The COVID-19 pandemic has effectively halted life as we once knew it, and left the open question, “what will our world look like when ‘normal’ life resumes?” While we don’t have a crystal ball that allows us to peer into the future, history has given us a template on what to expect.
Past pandemics have shaped politics, crashed economies, purred revolutions and produced other profound societal transformations. In the 14th century, the bubonic plague killed more than 60 percent of Europe’s population – a dramatic population decline that actually improved living standards for the survivors and marked the decline in serfdom. After the 1918 Spanish Flu, governments across the world realized the importance of researching infectious disease outbreaks, which led to dramatic improvements in global health care systems. Yet, in both of these instances, the beginnings of these changes were seeded before either outbreak began. Pandemics do not change the direction of world history but instead, they help accelerate it.
To determine what our world will look like post-COVID-19, we need to consider how our society was already changing. Long before the COVID-19 overtook the global conversation, technology was already causing major changes to the ways in which we live and work.
Like the steam engine and printing press before it, artificial intelligence (AI) is becoming a revolutionary technology that is predicted to propel our economy and profoundly change the way we live and work. Whenever a new development has the promise to make such a profound impact, people usually react with a mix of fear, excitement and uncertainty. The notion of imminent mass unemployment because of automation once troubled the nation. In reality, the U.S. has witnessed an unprecedented spike in unemployment – but not from AI and instead as a side-effect of the global outbreak.
How can AI and its sibling, operations research, help with the reopening and recovery of the economy? The National Security Commission on Artificial Intelligence calls for a data driven approach to be used by Governors of States in deciding how to balance public health and economic consequences in deciding which counties and industries to reopen. For example, one of the inputs used by the State of Pennsylvania in its decision making is a set of risk-tiered metrics computed using federal data and public data sources.
As states focus their attention on reopening and recovery, widespread and accurate testing for COVID-19 is a widely accepted benchmark that has to be in place before social distancing measures can be reduced. However, given capacity limits on tests, how should the tests be allocated and deployed taking into account the risk to care providers and vulnerable populations? How can the effectiveness of the testing policy be evaluated? Data driven approaches using AI and operations research techniques originally developed for marketing and political campaigning are being adapted to determine whom to test or which neighborhood to test based on a variety of factors. Examples of these include the risk of bad outcomes if an individual were exposed to the disease (e.g. older populations or vulnerable populations) or the risk they pose to others (e.g. health care workers or first responders) who interact and engage with vulnerable populations. This is the just the sort of capability that will enable policy makers to make smart decisions as pressure mounts to end lockdowns.
As we think beyond the near term and the issue of reopening, the changes in behavior set into motion by the pandemic will require rethinking about the shape of the economy that will emerge after the pandemic and how the historically large number of our fellow citizens who have been unemployed will find a path to economic prosperity.
Before the pandemic, the debate centered around how AI is changing and would change the future of work. Indicators such as the suitability for machine learning were created to predict what the likely impact will be on occupations and industries. Teams consisting of humans and AI’s were discussed as ways of augmenting human intelligence and enabling improved efficiency and effectiveness. However, a very different set of causal drivers related to the COVID-19 crisis are resulting in a review of these debates about the future of work. For example, a shift in focus to social distancing is causing organizations to completely rethink their workflow. Here, the capacity of people to work remotely using a technology such as Zoom or the pairing of humans with AI (e.g. a worker teamed with a robot to manage inventory in a warehouse) will permit a technology-enabled workflow reengineering that was not contemplated prior to the crisis. China and other countries recovering earlier than the U.S. from the crisis are providing some examples of how work is being reengineered in factories using a combination of process mapping and “time and motion” type approaches to ensure that people are able to work but at a safe distance. The same tools we are using to fight this pandemic will motivate the tools and innovations that will emerge as essential mainstays in our future workplaces.
However, considerable thought and attention will be required to deploy these capabilities since there is significant variation in the technology enablement opportunity when it comes to remote work. A recent study by scholars at the University of Chicago discovered that nationally about 33% of the jobs can be done online. Yet, this varies widely between regions. Rather than focus on averages, using data drive approaches, businesses and policy makers should focus on sectors and occupations that are more amenable to this transformation. This will identify opportunities where technology can assist people in doing work safely and thereby enabling reopening of the economy. These include areas such as education where hybrid models of education designed to meet the needs of in person and remote students, as well as faculty, are being designed to achieve outstanding learning experiences while ensuring public safety.
Some of these innovations that are being introduced will no doubt transform how work is reorganized after the crisis. As history shows, COVID-19 is likely to further reinforce the profound impact technology and AI have on our daily life. As long as we can collectively prepare for and embrace this reality – all indicators point to a promising future.
About the Author
Ramayya Krishnan, Ph.D., is the W. W. Cooper and Ruth F. Cooper Professor of Management Science and Information Systems at Heinz College and the Department of Engineering and Public Policy at Carnegie Mellon University. He is also the 2019 president of INFORMS, the largest international association of operations research and analytics professionals.
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