April 17, 2020
COVID-19 might accelerate or change previous AI adoption strategies
The COVID-19 pandemic may be a force that changes how the economies of the United States, Canada, and members of the European Union all use AI. Because of serious supply chain problems in Asia, many firms will restructure complex supplier networks or make them more redundant. In many cases, this will mean building up their use of AI and machine learning in home-nation plants.
Another change is an acceleration of online retailers’ use of AI. With the virus and extensive confinement to homes, online spending has spiked. To cope with the rise in customers, online businesses will likely turn to cloud computing, AI and machine learning to handle a greater volume of orders and manage supply chains.
The current macroeconomic situation due to COVID-19 includes substantial reductions in GDP growth and trillion dollar increases in debt. The pandemic could also promote a serious call to improve firms’ technological sophistication. With slow growth and low interest rates, economic statistics suggest a slow-growth economy could follow the COVID-19 pandemic. To avoid slowing growth for too long, governments might employ an industrial policy to increase the adoption of AI and related technologies. The object of such targeted spending would be to revive the country’s manufacturing base and reskill employees.
Examples of Specific Changes
Redesigning supply chains is a major place for a pivot from current trends due to COVID-19. Volkswagen has 31 of its 122 plants in China. VW plans to employ AI in its digital factories to achieve a 30 percent productivity gain over the next 6-7 years. These plans may no longer include a quick AI rollout in China. VW is more likely to cut its dependence on Chinese subsystems. As a result, it is more likely to deploy most of its AI-based Volkswagen Industrial Cloud in Germany and Western Europe. This shift away from China would reshape its supplier ecosystem.
If, as a result of COVID-19, VW shifts production of more components to its “mother plant” in Wolfsburg, it could also accelerate the adoption of 3D printing. This would mean VW would construct suppliers’ products at nearby assembly plants rather than shipping them from thousands of miles away. This would require production systems that use AI. Ford, GM, and other automakers would very likely to follow suit.
These trends also might reinforce a broad effort to implement AI-based digital ecosystems where humans operate machinery and manage operations. This would require additional digital skills among factory workers, especially where they need to work with machine learning models and AI. It might also result in a technology transformation of less capital-intensive parts suppliers, where tools including AI and machine learning would increase the suppliers’ importance in producing electric cars. This would change the economics of auto production and put pressure on competitors to ramp up their own digitization plans.
Application of AI to COVID-19 has been hindered both by the lack of historical precedence and by the absence of quality datasets necessary to train AI implementations. The one area of promise so far has been in the use of AI to identify potential connections and promising interventions or treatments by wading through the large number of publications on COVID-19, SARS, and MERS. If this proves successful, one might expect future medical and health researchers to apply similar techniques to cancer research and other related issues.
For online retailers, scaling up processing and supply chains to deal with the surge in online customers will also rely upon AI to analyze sales and logistics. It will also rely upon AI to evaluate issues in supply chains, particularly where there are shortages in supplies of products or produce.
An additional shift might transform entire industries where some firms have followed a strategy of becoming more technologically sophisticated than their competitors. In the biopharma industry, there is a push to employ AI and machine learning to create new drugs in record time. Moderna, a startup outside Boston, has developed a proposed COVID-19 vaccine in 42 days, the first to enter clinical trials. It has benefited from its knowledge of DNA and messenger RNA to create a vaccine that disrupts the virus’s reproduction in the body. COVID-19 might push other pharmaceutical firms to move more rapidly to create digital plants that rely upon AI-based analysis of genomic information; several large pharmas, like Sanofi and Eli Lilly are already moving in this direction.
The COVID-19 pandemic may be a force that changes how the the economies of the United States, Canada, and members of the European Union all use AI. Because of serious supply chain problems in Asia, many firms will restructure complex supplier networks or make them more redundant.
Larger and Longer-Term Shifts
An even larger and longer-term shift might also occur because of COVID-19. Technology-aware firms have learned from Tesla’s success that aggressively pioneering the adoption of technologies like AI can provide them with an opportunity to open a performance gap with their competitors. Tesla has gained a reputation for its rapid adoption of AI and machine learning. This has enhanced Tesla’s digital technology and analytics capabilities. Other firms plan to emulate Tesla’s efforts to create a wider competitive gap between their achievements and competitors that have waited too long to become digital. This opportunity might present itself due to COVID-19. It could offer such firms a chance to embark upon a “survival of the fittest” struggle in a few large, capital intensive sectors of the economy. If they succeed and emerge as winners, they might achieve a dominant position in the auto or pharmaceutical industries. This might severely diminish their competitors’ chances to survive.
COVID-19 might also create a dramatic change in technology policy in the governments of the United States, Canada, and members of the European Union. This might occur as part of a larger pivot to promote higher productivity growth in the economy. A unique national initiative might include an effort by governments to invest in the creation and commercialization of new AI-based technologies. This might be spurred by COVID-19 because the pandemic is likely to have such a dramatic impact on GDP and productivity. To be successful, programs, funds and partnerships would be devoted to helping industries such as autos develop autonomous vehicles or speed the commercialization of AI and quantum computing.
A driving force behind such a major policy shift might be economists’ recognition that COVID-19 has crippled the economy more than expected. With low interest rates likely to continue for three more years or more, GDP growth is expected to be low, around 2 percent based on current bond market data. This implies that productivity growth will be around 2 percent or below. With the addition of trillions of dollars of debt to the economy to fight COVID-19, the recovery could be handicapped by higher debt and low growth. By promoting more innovative technologies, such as AI, the economy might experience a productivity resurgence to 4 percent or more, bringing GDP growth to 4 or 5 percent. This would speed the recovery and very likely promote faster GDP growth.
Such a new generation of public policies to promote technological innovation would mark a real pivot in the economic policy of Western nations. The need to recover from COVID-19 might bring about such a shift.
This analysis was authored by Robert B. Cohen, a Guest Author with the Atlantic Council GeoTech Center as well as Senior Fellow with the Economic Strategy Institute, and by Dr. David Bray, Director for the Atlantic Council’s GeoTech Center.
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