AI in India: History and Evolution

Connecting the dots between the initial footprints of AI in India, its rise & adaptation in various domestic sectors, and road ahead to become the world leader in the artificial intelligence race.

14 min readMay 2, 2021

Every decade seems to have its technological breakthroughs. In 2021, we’re now in the next phase, the next industrial revolution. The advancement in artificial intelligence and machine learning (AI/ML) technologies is bringing a dramatic shift in the world of technology where it can be applied for more productivity and success to simplify the system.

According to a Barclays analysts report published in Itihaasa Research and Digital 2018, if human productivity was 100 units in 1765, it has increased to 3,000 units today.

In fact, it has doubled in just the last five decades, and this steep increase coincides with the Adoption of information technology — personal computers, software, internet, e-mail, mobile communications and more.

The emergence of AI has been a radical technological advancement impacting every area and not just technology. The AI-based application has already touched people’s lives in ways that are often not fully perceived. It has led to the convergence of the physical, digital, and biological domains.

As the fastest growing economy with the world’s second-largest population, India has a significant stake in the AI revolution. In this article, we’re set out to explore the potential of artificial intelligence in India with three main objectives:

1. To understand the footprints of artificial intelligence in India

2. To assess the rise in AI adaptation, key use-cases and growth in the Indian ecosystem

3. To evaluate India’s preparedness for the AI revolution against the global scenario

AI History and Evolution in India — An Overview

Everyone seems to be talking about artificial intelligence’s potential and how it can transform the world, leading to the next industrial revolution. But, unlike one might have presumed, AI is not new. Astonishingly, Greek, Roman, Indian and Chinese mythologies mention artificially intelligent beings in their scriptures. However, modern AI can be traced back to 1936, when Turing designed the first machine that used an algorithm. Artificial Intelligence as a science was formally introduced at the Dartmouth conference in 1956, where John McCarthy coined the term ‘Artificial intelligence’.

In India’s case, the first program on AI in India was conducted as early as the 1960s by Professor H.N. Mahabala at the Indian Institute of Technology (IIT ), Kanpur. However, research in AI took off in 1986 when the Government of India launched the Knowledge-Based Computing Systems (KBCS) program in conjunction with the United Nations Development Program as part of its Indian Fifth Generation Computer Systems (FGCS) research programme. Indian scientists have undertaken several projects since then, such as the project on Machine Translation for Indian Languages by IIT Kanpur; Optical Character Recognition project by ISI Kolkata; flight-scheduling expert system, Sarani, developed by CDAC, Mumbai; a speech synthesis system developed for Indian railways by TIFR; an image-processing facility developed by IISc using AI and vision techniques.

The Rise in India AI’s Adaptation and Growth

Since the early 90s, India’s IT and ITeS services sector has been of tremendous importance to its economy, eventually growing to account for 7.7% of India’s GDP in 2016. Being the fastest growing economy globally with the world’s second-largest population, India has a significant stake in the AI revolution. Recognizing AI’s potential to transform economies and India’s need to strategize its approach, the government has mandated India’s national think tank, the NITI Aayog, to establish the National Program on AI in 2018 — to guide the R&D work in AI-enabled technologies. Under the program, NITI Aayog has adopted a three-pronged approach — undertaking exploratory proof-of-concept AI projects in various areas, crafting a national strategy for building a vibrant AI ecosystem in India and collaborating with various experts and stakeholders. Starting in 2018, NITI Aayog has partnered with several leading AI technology players to implement AI projects in critical areas such as agriculture, health, education, finance, e-commerce, smart cities and infrastructure.

The Current Scenario

With the wide range of benefits that AI offers, along with the evolving national strategy for AI, India witnessed the highest increase in the adoption of Artificial Intelligence-driven technologies in 2020 compared to the US, UK, and Japan, according to a report by PwC India. Following the outbreak of coronavirus, India reported a 45% increase in the use of Artificial Intelligence, the highest among the major economies like the USA(35%), Japan(28%) and the UK (23%).

To leverage the transformative technologies while ensuring social and inclusive growth in line with the government’s development philosophy, India has joined the ‘Global Partnership on Artificial Intelligence (GPAI)’ as a founding member to support the responsible and human-centric development and use of Artificial Intelligence. It would bridge the gap between theory and implementation of AI by supporting cutting-edge research and applied activities on AI-related priorities.

Key Use Cases Of AI in India:

Adoption of AI by various sectors have been influenced by, among other factors, technical and regulatory challenges, but commercial implications have been the most significant determinant. While technical feasibility, availability of structured data, regulatory barriers, privacy considerations, ethical issues, preference for human relationship have all played their roles in determining the readiness of a sector for large scale AI adoption; compelling business use cases (e.g. improved efficiency, accuracy, speed, forecasting and accurate decision making) that lead to a direct impact on revenue and profitability have been the biggest driver for companies to pursue accelerated Adoption of AI. According to a report by McKinsey Global Institute’s AI adoption and use survey, sectors leading the AI adoption today also intend to grow their investment in AI the most, thus further reinforcing the varying degrees of AI adoption across sectors.

NITI Aayog has evaluated various sectors impacted by AI and has made a conscious decision to focus on a select set of sectors where only private sector-led initiatives will not achieve desired societal outcomes. To drive the maximum adaptation and growth through AI-enabled means, Healthcare, Agriculture, Education (preparing tomorrow’s generation to leverage the global AI revolution), and Smart Cities and Infrastructure (solving India’s rapidly urbanizing population) are among sectors with significant focus.

Here are the critical use-cases categorized based on the sectors:

Healthcare: Healthcare is one of India’s most dynamic yet challenging sectors and has been growing at a Compound Annual Growth Rate (CAGR) of 16 per cent since 2011, as reported by FICCI-KPMG 2016. The healthcare workforce required in India is likely to double to 7.4 million by 2022 from 3.6 million in 2013. This sector’s importance goes far beyond its economic value; it is pivotal for the nation’s well-being and progress. The key AI-enabled use cases are:

NITI Aayog has been working with Microsoft & Forus Health and rolled out technology to detect diabetic retinopathy. 3Nethra, a digital wide-field imagining system developed by Forus Health, is a portable device that can screen for a common eye problem. The 3Nethra device can get AI-powered insights even when working at eye checkup camps in remote areas with nil or intermittent connectivity to the cloud. The resultant technology solution also solves quality issues with image capture and systems checks in place to evaluate the usability of the image captured.
Platforms such as OnliDoc and Lybrate are also using AI methods to provide virtual assistance and diagnostics remotely. OnliDoc uses AI for symptom checking and treatment selection.
Chatbots are increasingly being used as conversational agents for interaction with patients. The online platform mfine, for example, handles more than 15,000 cases per month — approximately the number of patients handled by Manipal Hospitals, one of Bangalore’s largest conventional hospital groups. Several large hospitals now use chatbots to schedule appointments, converse, and collect basic details and symptoms before handing over a case to a doctor.
Healthi, a digital health and wellness startup in Bangalore, uses predictive analytics, personalization algorithms and ML to deliver personalized health suggestions. Similarly, Manipal Hospitals uses IBM Watson for Oncology, a cognitive computing platform, to help physicians discover personalized cancer care options.

2. Agriculture: India has come a long way from being categorized as purely an agricultural economy, yet according to a report by NITI Aayog, agriculture and the allied sector still accounts for 49% of India’s workforce, 16% of the country’s gross domestic product (GDP), and ensures food security to roughly 1.3 billion people. Thus, AI will have a significant global impact on agricultural productivity at all levels of the value chain.

Markets and Markets Research’s estimate valued AI in agriculture to be USD432 million in 2016 and expects it to grow at the rate of 22.5% CAGR to be valued at USD2.6 billion by 2025.

The key AI-enabled use cases are:

AI-enabled technologies are pioneering digital agricultural application to help farmers cope with climate change. An AI-powered sowing app has developed by ICRISAT, which utilizes weather models and data on local crop yield and rainfall to more accurately predict and advise local farmers on when they should plant their seeds.
An AI-based flood forecasting model implemented in Bihar is now being expanded to cover India’s whole to ensure that around 200 million people get alerts and warnings 48 hours earlier about impending floods.
Many AgriTech startups have also joined the race to revolutionize India’s agriculture sector with AI’s power. For example, Intello Labs uses image-recognition software to monitor crops and predict farm yields. Another example is Aibono, India’s first AI-powered fresh-food aggregator platform, which helps stabilize crop yield. At the same time, Trithi Robotics is changing the way agriculture is done by allowing farmers to monitor crops in real-time and provide precise soil analysis.

3.Education: With the world’s second-largest population, with an estimate of over half of it below the age of 25, the significance of a developed education sector is amplified. As the means to collect, clean, store and analysis data increases, AI-enabled technologies should effectively be leveraged to deliver improved education and teaching. Initiatives such as Atal Innovation Mission’s ATL AI-Base Module, the launch INDIAai as the “central hub for everything AI in India and beyond”, planetcode, and the Central Board of Secondary Education’s effort to integrate AI in the school curriculum are some of the many commendable and much-needed steps taken towards AI education in India. Highlighting some of the key AI-enabled use cases are:

In 2020, the “Responsible AI for Youth” programme was launched by The Ministry of Electronics and Information Technology (MeitY), wherein more than 11,000 students from government schools completed the introductory course in AI.
EdTech startup Indian ecosystem has attracted many investors worldwide as it is solving for the next billion users. Few examples of edTech startups using AI to disrupt the education sector are as follow:
Jungroo Learning is a B2B SAAS based EdTech startup that helps educators and organizations understand and chart their student’s journey at a microscopic level through an AI-powered Adaptive Engine.
Chimple Learning is an app from Sutara Learning Foundation, a non-profit organization dedicated to children’s foundational education. The app is designed to gamify each aspect of the learning process for growing children — using AI-powered tech stacks.
Expertrons, a Mumbai-based startup, is the world’s first AI video bot — assisted platform. The AI capabilities at this platform help career aspirants learn from the interview experience of other experts.
TagHive is a Samsung-funded education technology company with headquarters in South Korea and an office in India. The company’s offerings include clicker-based classroom response systems and AI-powered self-assessment solutions. The company offers its clicker solution under the “Class Saathi” brand in India and under the “Class Key” brand elsewhere.
Smartail is an AI-based ed-tech startup focusing on building solutions using NLP, OCR, Deep Learning, and solving niche pain points in the education segment. DeepGrade is an AI-powered platform that helps identify learning gaps and improve learning efficiency by evaluating both handwritten and digital content.

4.Smart Cities & Infrastructure: India is currently amid a surge of urbanization. According to OpenGovAsia, while the population living in urban areas was estimated to be 31% in 2011, recent research on satellite data indicates that this figure is close to 45% today and predicted to rise to up to 60 per cent 2050. Though seen as an essential aspect of a country’s economic growth and a significant step in the overall development of the country, unplanned urbanization presents challenges such as congestion, over pollution, high crime rates, poor living standards, and can potentially put a significant burden on the infrastructure and administrative needs of existing Indian cities. To tackle these challenges, India’s Government has embarked on an ambitious initiative to set up Smart Cities across India to drive economic growth and improve the quality of life by harnessing AI solutions. The key AI-enabled use cases are:

Pune has launched The Pune Street Light Project to set up energy-efficient street lights that can be remotely controlled through a Supervisory Control and Data Acquisition (SCADA) system.
Surat has built a network of more than 600 surveillance cameras enabled at all major locations in the city; as a result, the crime rate has dropped by 27% after the implementation of AI-powered safety systems.
The Kumbh Mela Experiment is another example of AI-powered utility, which developed sophisticated methods and algorithms to help planners and event managers manage huge crowds. Around 1,000 CCTV cameras were installed to monitor movement for advance prediction and response movement.

To Tackle The Pandemic

For the Covid-19 response, MyGov has integrated an AI-enabled Chatbot for ensuring quick and timely communications.
Waston Assistant, an IBM’s AI product, has been deployed by The Indian Council of Medical Research (ICMR) on its portal to enhance the communication of specific queries of frontline staff and data entry operators from various testing and diagnostic facilities across the country.
The government of Kerala has deployed Srishti Robotics’ ‘Nightingale-19 Robot’ to delivers food and medicines in bulk and also allows doctors and other healthcare practitioners to use video interactive technologies to interact with patients.
Chhatrapati Shivaji Maharaj Terminus and Lokmanya Tilak Terminus in Mumbai; Maharashtra is installed with FebriEye is an AI-based thermal screening system for real-time and automated, non-intrusive monitoring to ensure that a person entering does not have a high fever.

Key Challenges in India:

The application of Artificial Intelligence in the present market space is colossal, and a growing number of enterprises are already taking the benefits of implementing AI.

According to the research firm Markets and Markets report “Artificial Intelligence Market by Technology ‘’, predicts the AI market would cross USD 16.06 billion by 2022, growing at a healthy CAGR of 62.9% from 2016 to 2022. Despite these staggering numbers, India could do far more to adopt and integrate AI technologies to live up to its full potential.

Even though AI is optimizing system-operating models and transforming business processes for enterprises and organizations worldwide, the number of business units and MSMEs utilizing such modern tech tools, particularly in India, is relatively low. Since every organization is different and will approach and experience AI adaptation differently, NITI Aayog, in its National Strategy for AI Discussion Paper, along with some others, has emphasized the following vital challenges in the AI-enabled Indian ecosystem:

Lack of enabling data ecosystem
The cost of failure in adopting AI in the business process is much higher in India than in the west. Hence, the lack of room for innovative experimentations.
The low intensity of AI research
Core research in fundamental technologies
Transforming core research into market applications
Inadequate availability of AI researchers in the field of AI/ML innovation.
Ai-based application has mainly been driven by the private sector instead of the government sector and primarily been focused on consumer goods. The policymakers need to design policies to enable smooth Adoption in every sector.
The government should consider public and private funding models for AI research.
Improper incentivization to adopt AI in the business process despite existing potential possibilities.
Unclear privacy, security and ethical regulations

Global Scenario versus Indian Landscape

Countries worldwide are trying to adopt, develop, and integrate AI-enabled technologies for virtually every segment of most industries, making it truly revolutionary. Many nations are racing to achieve a global innovation advantage in artificial intelligence (AI) because they understand that AI is a foundational technology that can boost competitiveness, increase productivity, protect national security, and help solve societal challenges. The following four factors can determine the world leader in the artificial intelligence race:

Early Adoption of latest technologies by industries

According to a Boston Consulting Group report, the top three early adopters of AI among the 12 countries surveyed are the United States of America at 25%, followed by China at 23% and India at 19%. This percentage also points to the fact that the US lead in the early adopters’ race is due to the extensive availability of Artificial Intelligence-based technologies.

AI-related research publications

Having the first-mover advantage, The United States has historically been the leader in AI-related research publications — having to accumulate the highest number of publications in the last couple of decades. In contrast, China has amplified AI-related research publications in recent years by over 120% compared to that of the United States of merely 70%. Looking at some numbers, China published a total of 102,161 AI-based papers; the United States has published 74,386, while India (being third in the list) just able to produce 23,398 AI-based research papers.

Source: Nature.com

Investment & Funding

With an eye to the potential benefits of AI-based technologies, the US National Science Foundation (NSF) announced in August 2020 that it is establishing five new institutes focused on different topics, each led by a different university, and each to receive $20 million over five years. With funding from other major US agencies, including the Defense Advanced Research Projects Agency and National Institutes of Health, the US government expected to spend almost $5 billion on unclassified AI research in 2020. China has released a three-step program in 2017 and defined its goal to become an AI leader worth $150 billion by 2030. The Chinese government has documented Beijing AI Principles and also made investments in AI-focused technology research parks. Even with other countries emphasizing AI research, such as India, Singapore, and Japan, the race comes down to China versus the United States.

Strategy and Policy Making

AI models and technological advancements heavily depend on the massive data sets on which it trains its system. China and India naturally get in the driving seat as they are the world’s most and second-most populated counties on the globe, respectively. Apart from the massive data sets a country generates, laws governing the access of these data sets also play a crucial role in producing very sophisticated AI as a consequence. With the strict laws in Europe and the United States, and India yet to advance in its policies around AI, China holds a clear advantage in becoming the world leader as it has few data protections and massive access to varied and diverse pieces of data.

In the global vs India AI debate, India is no longer a provider of outsourced service to the world. The fastest-growing economy, accompanied by the second-largest population, comes with its own sets of advantages. India has the potential to become a digital leader in its own right, keeping up pace with the likes of the United States & China and consciously driving AI-enabled growth.

Conclusion

The possibilities of Artificial Intelligence is so vast that the full spectrum of its opportunities is difficult to fully comprehend. In India, most of the traction is in the form of AI pilot projects from the government in few specified sectors and the emergence of AI-based startups in the top tier cities — indicating grassroots level adoption. While India may be late to wake up to the AI revolution, the next five years will be a time to set both the pace of innovation and the trend of AI adoption within the country.