Artificial Intelligence and the post-COVID-19 World

Back in 2002, the SARS outbreak caused an unprecedented demand for online shopping, which concomitantly brought e-commerce giant Alibaba Group to the forefront. The Great Recession of 2008 directed brands such as Starbucks and American Express to opt into digital operating models that not only helped them thrive through the crisis but gain greater shareholder value as well!

On similar grounds, the COVID-19 pandemic is now dramatically altering consumer behavior on a global scale. Individuals who preferred to buy their supplies in person are now going for cashless order payments thanks to fintech platforms. Students are pursuing their studies through virtual classrooms and ed-tech applications. Employees are staying connected with their teams through messaging and video conferencing applications. The list goes on. And although such instances aren’t new stories, the mere magnitude to which they are being practiced simply cannot be ignored.

Tech giants like Apple, Amazon, Google, Facebook, and Alibaba are already leading the way with their extensive experience in digital automation and data analytics and inspiring entrepreneurs across the world. While implementation is the easier nut to crack, the challenge is to ensure scalability. Once that is dealt with, navigating and adjusting to uncertain supply and demand, allocating workforce, and adapting to changes in consumer priorities will be half the concern it is at present.

Source: CB Insights

Dealing with the ongoing crisis

It is safe to say that the very socio-economic dynamics of the world have begun metamorphosizing with the adoption of AI technologies in both product manufacturing and service spheres, directly or indirectly. The backward linkages of cloud computing and big data have already given us a glimpse of the Fourth Industrial Revolution. With the COVID-19 crisis at hand, the dependency on artificial intelligence is projected to only grow from here on. It is by using big data that governments have effectively been performing contact tracing. 3D printers have made the mass production of PPE kits possible at local levels.

As factories are reopening across the globe, reality is hitting hard: the entire supply chain logistics is facing a shortage of migrant workers, social distancing is being strictly induced to avoid a second wave of virus, not to mention an increased concern for health at workplaces. Under such circumstances, automation comes off as a major solution, although it may lead to possible reallocation and reskilling of human resources. To keep up with the ever-changing demand patterns, product and service providers have to embrace the pros of cutting-edge technology.

E-commerce brands like Amazon are ramping up fulfillment capacities to maintain their vast customer base. Movies are now being launched digitally on platforms like Netflix and Amazon Prime since multiplexes are shut down. Closure of gymnasiums has lead fitness brand CureFit to provide extensive digital experiences across fitness, nutrition, and mental health. Walmart launched its very own touch-free payment system Walmart Pay to alleviate the spread of the virus when customers shop in-store. Such processes automation also improves the efficiency of finance processes like procure-to-pay and order-to-cash, which in turn reduces the risk of non-compliance.

Innovating healthcare

Did you know that an artificially intelligent platform named BlueDot had picked up an anomaly and registered a cluster of unusual pneumonia cases in Wuhan, China nine days before the World Health Organization released its statement regarding the emergence of the novel coronavirus SARS-CoV-2? BlueDot uses natural language processing and machine learning techniques to track, locate, and report the spread of infectious diseases. That’s AI in the healthcare domain. With more data at our fingertips, it is easier to predict problems as well as come up with solutions. As opposed to the lengthy trial-and-error process of developing new drugs, AI can significantly speed up R&D in pharmaceutical industries.

Integrating AI from research to implementation

Demand-supply dynamics

AI-driven tools are continuously learning and adapting while dealing with large volumes of data. They can detect underlying patterns, enabling computers to make complex decisions, predict human behavior, and even recognize images and human speech. AI’s machine learning and advanced data analytics capabilities help unearth emerging trends and identify changes in consumer preferences. AI-enabled platforms will also allow companies to better simulate work environments and create on-demand labor forces. Such ventures significantly impact a company’s costs, revenue, and operating model.

Remote working

Although you cannot expect Artificial Intelligence to magically come up with new ways of working, it can help companies in leveraging innovative ways of engaging its workforce to mitigate disruptions and remain productive. AI-powered organizations will have a more successful remote work situation as they are generally built around modularity and agility, which are rudimentary for a successful business. AI also supports the demand for skilled labor through digital marketplaces. AI-enabled platform Upwork allows freelance professionals and potential employers to connect with each other.

Remote connections are the new normal

Conclusion

The current pandemic scenario should be a driving factor for entrepreneurs to amend their business models as per the new reality. With AI at the core, data is essential for navigating every aspect of such an operating model and providing inputs to a variety of tasks.

But, it is equally important to avoid a “zero-human” mindset. Why? Because humans can ensure that no operating area is left unchecked. Even the most autonomous of applications need human intervention to reach the level of context and expertise that AI alone cannot achieve.

To prevent inferior judgment and unnecessary bias, AI should be augmented through the perspective of human imagination and interpretation. Organizations should encourage their employees to learn more about implementing AI in design algorithms, integration processes, and judiciously monitor their outputs. A truly effective AI-system contributes to strategic decision-making only after taking second-order connotations into account.

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