In 2019, India had achieved a stellar rank of 19 in the AI Readiness Index, which gets compiled by Oxford Insights and the International Development Research Centre. It examines 194 countries worldwide, assessing their governance, infrastructure and data, skills and education, and government and public services to peg how well these countries are prepared to manage the potentially transformative impacts of AI. This positive score had put our AI dreams quite high on the mantle with Commerce Minister Piyush Goyal announcing that AI and ML are set to contribute $1trillion to the economy by 2035. 

At present, it seems that the post-Covid scenario is all set to stall our economy by at least a few strong miles. But, projections reveal that the market may be ready to go ballistic with their AI aspirations. User behaviour would complement it as in the midst of social-distancing in a time where people would prefer interacting with automated services rather than conduct face-to-face interactions. Additionally, the global market would need a faster, cheaper manufacturing route to set the supply-chain back in order, thus increasing the importance of AI-led automation.

Several factors would affect AI-driven productivity growth, including labour automation, innovation, and new competition. Micro factors, such as the pace of adoption of AI, and macro factors such as a country’s global connectedness and labour-market structure, would also contribute to the size of the impact.

Benefits

With the IMF predicting India’s growth rate at 1.9% for FY20-21, AI incorporation can help put the economy on a faster recovery track. AI innovations in one sector will have positive consequences in another, as industry sectors are interdependent based on value chain. 

According to McKinsey’s 2018 discussion paper titled ‘Modeling the Impact of AI on the World Economy’, developing countries were set to capture only about 5 to 15 percent net economic benefits from AI adoption, with a larger chunk going to the developed countries. However, the current scenerios and the deep COVID crisis in the western economies could shift this significantly in favour of least comparatively less affected countries like India. 

AI applications would also be a powerful tool to analyze large volumes of data to cull out trends that the markets will follow in the post-Covid period. This can help prioritize investments and resources set to maximum benefit.

Another benefit that can be gained out of the present crisis is job transition. The World Economic Forum (WEF) estimated that the emerging professions resulting from automation could account for 6.1 million jobs globally between 2020 to 2022. A Dell Technologies report meanwhile claims that 85% of jobs in 2030 have not been invented yet. Pandemics will naturally lead to pivots and ultimately new aspirations and expectations from our collective notion of ‘work’, but automation will ultimately lead to fewer disruptions while transitioning from the physical to the digital workspace as a lot of the activities will be built on AI as a fundamental base and hence would prove highly mobile.

What Needs to be Done?

With hanging uncertainty about imminent lockdowns, there is a rampant need to instil trust and induce transparency in AI systems for all stakeholders. Government and civil society need to collaborate on a working framework while negotiating actively on the rights and responsibilities that tag with it. The Singapore government in January this year announced its intention of releasing the second edition of the Model AI Governance Framework. The first edition of the framework went into details over the algorithm audits and the “human-over-the-loop” approach, which explains the human’s supervisory role in AI-augmented decision-making. It also delves into the other factors that organizations take into account, such as the nature and reversibility of harm; and operational feasibility to determine the level of human involvement in an organization’s decision-making process around AI. 

An India-specific approach paper on decision making will have to break down the technical complexities and educate the firms and users about the applications and implications of AI in everyday life. The major recommendations of the National Strategy on Artificial Intelligence paper by NITI Aayog, included setting up Centres of Research Excellence (CORE)- focused on fundamental research; and International Centres on Transformational AI (ICTAI)- focused on applied research. NITI has its work cut out in identification of potential cases for AI usage, onboarding of sub-domain experts and preparing data for AI analysis. NITI is also developing the National Data and Analytics Platform where AI tools will be leveraged to deliver a platform that will democratize access to public government data through a user-friendly interface

Increasing automation to boost productivity and simultaneously a major rehaul of reskilling and upskilling requirements within the Indian workforce need to be seeded to complement AI incorporation. The talent demand-supply gap in AI and Big Data Analytics is expected to grow from 62,000 to 140,000 over the next 3 years according to a report by the World Economic Forum. This would be reflected in all future educational and skill development measures. 

Challenges

A rapid integration of AI may lead to heavy gaps surfacing between firms, employees and markets, both nationally and globally. 

It can also lead to widening the skills gap. According to a 2019 study by Edtech company, Great Learning, the average work experience of AI professionals in India is 7.2 years, while only 29 percent of AI professionals have more than 10 years of work experience. Should India accelerate at a rate that leaves behind firms with much less resources behind as the giants race to the finish line, the gaps will crack the overall market and lead to huge skill and pay disparity.

According to the McKinsey discussion paper, the total absorption level of AI by companies was projected to reach about 50 percent by 2030. However, the post-Covid world might see an exponential spike leaving worries about the rapid automation anxiety that a lot of the stakeholders would have to deal with. It is hence pertinent to focus on a strong thematic approach on AI and work on an integrated framework which can be implemented in a phased manner, while allowing a fair feedback loop to ensure success.