To aid leaders in navigating the pandemic, this note addresses the opportunities to leverage AI today, and the challenges in and approaches for harnessing AI to provide answers and foresight needed in the months ahead. In enlisting AI to plot a path out of the crisis, organizations face three inter-related questions: how has the crisis affected business as usual; what is the relationship between AI and business value; and where will AI flourish in the months to come?


Business Analytics Institute: Dr. Lee Schlenker, Principal
FutureGrasp: Dr. Thomas A. Campbell, Founder & CEO
FutureGrasp: Jon Fetzer, Executive Advisor


One major consequence of the COVID-19 pandemic is the difficulty that organizational decision-makers have in keeping their products, services, and ideas relevant, while simultaneously planning for the foreseeable future. Despite the impressive amount of near real-time social and economic data available today, perceptions of risk, uncertainty and ambiguity have hindered the ability to act decisively in plotting a course out of the pandemic. In theory, artificial intelligence (AI) is ideally suited to analyzing data and assisting business leaders in predicting consumer behavior and evaluating concrete courses of action. To aid leaders in navigating the pandemic, this note addresses the opportunities to leverage AI today, and the challenges in and approaches for harnessing AI to provide answers and foresight needed in the months ahead. In enlisting AI to plot a path out of the crisis, organizations face three inter-related questions: how has the crisis affected business as usual; what is the relationship between AI and business value; and where will AI flourish in the months to come?

The Effects of the Pandemic

The COVID-19 pandemic began with a failure to predict the severity of the novel coronavirus and its implications. We are only today beginning to understand that the pandemic has morphed into successive waves of economic, social, and political calamities. Crises are not revolutions but tipping points in which unseen events take center stage in shaping the future. Returning to “business as usual” is nothing more than a form of wishful thinking that ignores the structural changes in consumer and organizational behavior inherent in each crisis.  When focusing on AI, we must identify which use cases will be prominent in the years to come, and where the opportunities are to harness machine intelligence to help us make better decisions moving forward. We have already witnessed several structural changes that will shape business practices in the foreseeable future. Below we offer several example industry sectors that benefit from AI.

Manufacturing. In manufacturing, the need for more robust and agile supply chains has never been more evident. For the first time in modern manufacturing history, demand, supply, and workforce availability are affected simultaneously. Major global manufacturers are experiencing disruptions across their supply chains of raw materials and components; their ecosystems have proven too geographically disparate and too siloed to meet current market expectations. Social distancing and employee safety measures have put an additional level of pressure on manufacturers as considerable portions of the workforce have been unavailable on-site. Economic lockdowns have resulted in income loss for large segments of the population, with both minorities and the underprivileged being proportionately harder hit.

In the short-term, manufacturers seek ways to quickly ensure continuity and introduce flexibility. In the long-term, national governments promote economic isolationism to revive manufacturing segments that are considered critical for national resilience and sustainability. Therefore, when faced with these competing market pressures, corporate stakeholders are lobbying heavily for fully owned rather than loosely coupled ecosystems.

Banking, Financial Services, and Insurance (BFSI). In the BFSI sector, successive waves of liquidity and solvency challenges have provided a stiff challenge to central banks attempting to keep the financial system afloat.  Faced with the realities of the lockdown, both retail and financial banks have been challenged to maintain supervisory and compliance processes that were never designed for remote work. Auditability and anti-fraud collaboration are more important than ever as traditional banks and new entrants diversify their financial services across increasingly diverse ecosystems.

Individual managers have struggled to meet revenue and customer expectations as both business activity and interest rates drop, leading to a probable increase in unsecured debt. Changing customer expectations and behavior concerning real-time payments, cash-less transactions, and contact-less customer support have already impacted business activity across the sector. The BFSI sector has been challenged to offer a transformative vision to their stakeholders – focusing on sustainability, transparency, and meaningfulness coming out of the pandemic.

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Energy. The pandemic has heavily impacted multiple segments of the electricity value chain, including primary energy acquisition, generation, transmission, and retail and customer services. The application of stay-at-home measures strongly correlates with marked drops in consumption. Europe has recorded a significant collapse in electricity prices. Distribution System Operators have delayed planned infrastructure projects, and regular maintenance and field work have been held to a minimum. Trickle-down effects are felt throughout the value chain, resulting in a substantial decline in the procurement of goods and services throughout the supply chain. 

The renewable energy sector has been noticeably hard hit by transportation restrictions given China’s predominant role in the production of clean energy technologies. Government and national regulatory agencies have advised consumers to delay payment of utility bills. Defaults on payments may well have a multiplier effect on the long-term financial capacity of energy distribution grids. There are signs of latency, if not behavioral change, in consumer habits in countries coming out of lockdown that must be addressed in predicting the future of electricity production and distribution networks.

Cross-Sector. Overall, the economic realities of the last few months have challenged management and production norms. In operations, management has been forced to imagine new scenarios of remote working to complement if not replace factory and office work. The primacy of the division of labor and line management has been questioned as organizations have had to rely more on informal networks and teamwork. In distribution the sacrosanct principles of “just-in-time” have given way to concerns of provisioning “just-in-case” scenarios to better address the needs of decision-making under uncertainty.

The distinction between the real and digital economies have become increasing blurred as businesses transform online commerce strategies to envision a “contact-less” economy. The human and ecological costs of the crisis have led some to question established practices of maximizing short-term returns of investment when compromising future well-being. The value proposition of information technology may well be aimed not just at refreshing dated management practices, but reinventing commerce and industry after the crisis.

AI and Business Value

The business value of AI resides in its ability to address business challenges. Via machine learning, AI can be used to mimic and exceed the capacities of certain human capabilities, create new services, learn from experience, and influence human interactions. The business value of AI comes from increasing direct or indirect revenues, generating cost savings, improving capital usage, optimizing organizational activities, or realigning an organization’s product or service offering. Metrics can be captured within a process, between processes, or in inventing new forms of engagement between human and physical agents. AI’s impact on markets and organizations can be measured in terms of efficiency, effectiveness, utilization, or innovation.

Of special interest in business is the impact of AI on organizational decision-making. Decision-making processes involve intertwined activities of perception, prediction, evaluation, and insight. To navigate the COVID-19 pandemic, leaders must consider the traditional foundations of business value in their organizations, the specific nature of market disruptions, and the social and emotional factors that motivate organizational stakeholders. The decision-making process is composed of inter-related activities: the perception of the data, the use of heuristics and algorithms in the prediction of a solution, the metrics used to evaluate the solution, and the insight we gain in dealing with future challenges and opportunities. The optimum balance between human and machine intelligence depends upon the context in which the organizational challenges arise, the methodologies used to qualify the data, the scenarios developed to address the business problems or opportunities, and the actions undertaken to capture value.

The value of AI is in neither the hardware nor the software, but in its ability to improve the quality of decision-making. In each industry and market there are multiple decision-making environments that depend on an organization’s understanding of both the causes and consequences of business problems: simple environments in which both the causes and consequences are well known; complicated environments in which the cases and consequences are knowable; and complex environments in which the causes or consequences require reformulating the challenges at hand.

The Future of AI Post-COVID19

Although the market for internet technologies has largely prospered during the pandemic, there are clear winners and losers in the domain of AI. Funding for AI-intensive applications in transportation, travel and accommodation platforms has wilted under the lockdown due to the risks and uncertainty of evolving consumer behavior.  Inversely, investments in workplace technologies, online communications and entertainment have done measurably better for many of the same reasons. The use of AI in accelerating the reach and the impact of digital transformation in general, and of supply chain models in particular, is expected to grow rapidly as business decision-makers focus on organization agility, resilience, and business continuity. Training to understand the challenges and opportunities of AI, especially in remote work scenarios, will also be an area of intensive focus in the immediate future.

Several use scenarios for AI in the foreseeable future can be clearly identified. Faced with the cost of labor in re-localizing production in mature markets, the use of limited AI in enabling automation and reskilling the workforce will be major priorities for commerce and industry. Greater attention will be given to credit worthiness and fraud detection as concerns over bad debt intensify. The use of blockchain technologies will become increasingly a center of attention as concerns grow over the auditability and traceability of products and services.

Prediction of both supply and demand will require the proliferation and evolution of algorithms that account for current shifts in consumer behavior in complex adaptive systems.  The contact-less economy will create profitable opportunities for both data science and machine intelligence as new forms of production, consumption, and competition arise.  Machine intelligence will play an increasing role in the construction, personalization, and evaluation of digitally based experiences. Finally, the future of AI will depend upon the tradeoffs of acceptable data practices in negotiating the proper balance between individual liberties and the needs for accountability in surveillance capitalism.

How will producers and consumers leverage data and algorithms coming out of the COVID-19 pandemic? Beyond applications of limited AI outlined above, we can expect the interdependence of human and machine intelligence to draw far greater attention. There will be no return to “business as usual” as one global crisis leads into another, but rather an increasing awareness of the need to account for risk, uncertainty, and ambiguity inherent in complex decision environments. The interface between AI’s probabilistic inferences and the convictions of emotional, interpersonal, and naturalistic intelligence will need to be weighted and aligned.  Solving the challenges of the pandemic will not be accomplished by making machines smarter, but by helping consumers and managers make better decisions for plotting a path moving forward.


Key takeaways are noted here to maximize AI’s benefits to decision-makers in manufacturing, banking and finance, energy, and public sectors. First, AI is all about learning from context, and organizations need to explore how the social, economic, and political conditions are changing the foundations of markets and industries in times of crisis. Although “business as usual” is a losing strategy, there have been clear winners and losers in AI that can be identified to facilitate an organization’s transition to the “new normal.” Second, AI alone is not the answer. Organizations need to explore how co-development of the hybrid human plus AI system can improve the activities of perception, prediction, evaluation, and insight inherent in decision-making. Ultimately, mastering decision-making under uncertainty by leveraging the synergies of collaborative intelligence will provide a foundation for organizational success during and post-COVID19. Business Analytics Institute and FutureGrasp stand ready to assist in this journey.


Further Reading

Campbell, T.A., et al., Globalization following Coronavirus: Business as Usual or a New Normal?

Garuccio, A., Post-pandemic banking. 6 drivers shaping the future of finance

Golbin, I., Responsible AI is even more essential during a crisis

Grossman, G., After coronavirus, AI could be central to our new normal

Heaven, W.D., Our weird behavior during the pandemic is messing with AI models

IAE, Sustainable Recovery

Kroupenev, A., What Will Manufacturing’s New Normal Be After COVID-19?

Mylenka, T., et al., Impact of Covid-19 on the global energy sector

Sneader, K., et al., Beyond coronavirus: The path to the next normal


Business Analytics Institute leverages data science to improve managerial decision-making. BAI offers training, coaching, and mentoring programs for high potential managers and engineers to learn how to transform data into impactful decisions for their organizations and their customers. BAI Principals and Associates constitute a group of international practitioners and specialists with a proven track record in both industry and academia; they have led several dozen missions over the last two decades in finance, health sciences, manufacturing, telecommunications, and public service.

FutureGrasp is a global research and advisory group dedicated to capturing and comprehending emerging technologies, especially artificial intelligence (AI). Through rigorous data and trends analyses, accompanied by expert writings and briefs on emerging technologies and their implications, FutureGrasp enables its clients to lead in the fast-paced world of technology and its policy implications. Founded by a former senior US Government official – with an experienced team of other former senior US Government leaders, top university faculty, and successful serial entrepreneurs – FutureGrasp works with the private sector (Fortune 500 and startups), governments (US and foreign), and IGOs (United Nations).