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ARTIFICIAL INTELLIGENCE AI and India: Why AI product development is an uncharted territory

Jun 17, 2021

When it comes to developing AI solutions, India has witnessed a staggering growth. But it has been losing out to China and the US in the product race, to become the AI garage of the world. The Opinion piece analyses why India has been falling behind and suggest steps to become an AI product manufacturer.

While India is evolving significantly when it comes to technologies like AI and ML, we are barely scratching the surface and are far, far behind our global counterparts. With governments catching up — the US had launched its AI report in 2016 and the UK in 2017 — the 2018 budget underscored the need for India to leverage this technology. However, with just a year to go for Niti Aayog’s short-term target, India remains an AI solution destination. AI products, which contribute to more value-add, remain missing from the economy. Here are a few reasons why we are lagging on the product development front.

The product lag: Compared to software/solution development, developing AI products demands substantial financial investment, more time and big risks. In addition, the product testing cycle and the time invested into product perfection is far longer than what it takes in software building. AI, along with its ancillaries, ML, predictive machining, deep learning, etc, require supercomputers and fast internet, which India has made little progress in. Besides, AI processes massive amounts of data, which requires high-performing set-ups, which need constant updation. Usually, this kind of set-up requires a huge investment.

Despite a growing population consuming more internet and generating data scores, India has realised the value of data but has been incapable of leveraging it. The country is also far behind in terms of research, innovation and patent filing. Most of the talent pool focuses on IT development and not so much on research & development. Industries in India contribute only 14% to research, with universities contributing 86%, leaving a big gap between the ivory tower (academia) and the real market. In recent years, Indian companies have become more liberal towards AI and are taking steps towards becoming the world’s AI ‘garage’. However, the efforts are concentrated towards becoming solutions providers. Hence, more needs to be done.

Invest: While India’s private players and the government have increased investments in AI, we are far behind our competitors. India can only match strides with its competitors with increased investments from both government and private players.

Collaborate: Collaboration with relevant stakeholders, with government intervention acting as a catalyst, can ensure strong foundations and is essential. The hub and spoke model that the Department of Science and Technology has been implementing for blockchain can go a long way in ensuring participation from all fields.

Data: Before embedding data into systems, companies need to educate themselves about data, gather it, organise it and acclimatise themselves to AI/ML algorithms. Democratising data with strict cyber and data laws could take AI implementation to the next level. The government, in recent years, has opened a trove of information for Indian companies, but it still does not view AI as a product. So, there are few incentives to invest in such businesses.

Research and skills: With the proper infrastructure in place, educating, skilling, reskilling and upskilling will be essential to create competence. Courses aligned with educating the upcoming workforce need to be updated and matched to global standards, and gaps in the research ecosystem need to be filled. Incentives in the field of research should be ensured for talent retention within the country.

The steps suggested to make India an AI product manufacturer is a long, incremental process rather than a quick investment-quick return strategy.

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