Without the critical infrastructural prerequisites, AI’s role in development would remain stunted
India has made remarkable strides in Artificial Intelligence (AI), revolutionising sectors like healthcare, agriculture, finance, and education. With government initiatives such as the National Strategy for AI and a thriving startup ecosystem, AI development is rapidly advancing. India is positioning itself as a global leader in the AI ecosystem. Yet, before rolling out AI solutions at scale, particularly in vital sectors like agriculture, several critical prerequisites must be met. First and foremost, robust digital infrastructure must be established across rural areas to ensure seamless data collection and analysis. This means ensuring widespread internet connectivity, providing access to smart devices, and equipping individuals with the necessary training to use AI tools effectively.
Without this foundational infrastructure, the deployment of AI in agriculture would be both impractical and inequitable. The challenge lies not just in developing advanced technologies but in creating an environment where these innovations can be utilised by all, ensuring that the benefits of AI are broadly shared and not confined to a privileged few. Only by addressing these infrastructural and educational gaps can India fully leverage AI to revolutionise key sectors and achieve true progress.
India's approach to AI development signifies a serious and strategic commitment to unlocking the potential of AI for economic prosperity and societal progress. The government's acknowledgment of AI as a fundamental driver of advancement is evident in its notable impact thus far.
India's approach to AI development signifies a serious and strategic commitment to unlocking the potential of AI for economic prosperity and societal progress.
The economic implications of India's focus on AI are profound. Projections indicate that AI could significantly contribute to the Indian economy, potentially reaching US$ 967 billion by 2035 and adding US$ 450-500 billion to India’s GDP by 2025. This would mark a substantial stride towards achieving the nation's ambitious US$ 5 trillion GDP target, with AI playing a pivotal role in achieving 10 percent of this goal. India’s technology industry has witnessed over 15 percent growth in AI/ML jobs in the past year, with AI engineer positions expanding at an impressive 67 percent year-on-year. This surge includes the development of proprietary AI and Generative AI platforms, automation tools, data analytics solutions, and tailored AI applications for specific industry verticals such as healthcare, banking and finance, and retail.
India's endeavours in AI have garnered global recognition. The country's appointment as the council chair of the global partnership on AI (GPAI) reflects its increasing influence and prominence in the field. India's achievements in various AI metrics, including ranking first in AI Skill Penetration and GitHub AI Projects, solidify its position as a key player in the global AI landscape. India's proactive approach to AI development, backed by strategic initiatives and a clear vision, highlights its dedication to using AI for inclusive growth and technological progress. For example, beyond the National Strategy for AI, the Ministry of Electronics and Information Technology (MeitY) launched the National AI Portal in 2020. This portal acts as a central repository for AI-related information, resources, and news. It stands as a collaborative platform designed to share AI developments and innovations within the country. India's focus on AI development is evident, with a clear intention to ensure that its benefits reach everyone. This raises the question: How can this be planned to ensure maximum benefit for the population?
The government's role is to focus on its development goals and empower its citizens. Therefore, advocating for a utilitarian approach to AI development in India is neither new nor controversial. The best action, according to this, would be the one that results in the greatest good for the greatest number of people. But what does this imply in terms of AI development? Several viewpoints need to be taken into account, with a major approach involving assistance to the largest employment sector in India: agriculture. It appears that a significant issue facing Indian agriculture is low productivity, stemming from outdated farming practices and inadequate access to modern technology. Addressing this issue is paramount. However, before considering advanced solutions like precision agriculture, market forecasting, and smart irrigation, it is crucial to first focus on improving access to modern technology.
The government recognises that agriculture requires innovation and support, and AI could play a crucial role in this. However, the challenge lies in the lack of up-to-date official statistics. While there are numbers available, they are often outdated. There are instances of emerging technologies like AI being used in agriculture, but when the numbers are revealed, it becomes clear how small the adoption rate is compared to the actual number of agricultural workers in the country. For instance, the last agriculture census from 2015-16 indicated that India has 146.45 million “operational holdings.” Meanwhile, the Pradhan Mantri-Kisan Samman Nidhi (PM-Kisan) scheme reported 110.94 million beneficiaries who received their INR 2,000 income support instalment for April-July 2021. Additionally, the National Statistical Office's Situation Assessment of Agricultural Households (SAAH) report for 2018-19 estimated that there are 93.09 million “agricultural households” in the country. These disparate figures, spanning from over 90 million to nearly 150 million farmers, underscore a significant lack of clarity and consistency in data on Indian agriculture, complicating any attempt to accurately grasp the sector's dynamics. This ambiguity hampers our ability to develop informed, effective policies tailored to the agricultural landscape's realities.
The government recognises that agriculture requires innovation and support, and AI could play a crucial role in this. However, the challenge lies in the lack of up-to-date official statistics. While there are numbers available, they are often outdated.
For instance, there are AI solutions to help small banana growers quickly detect diseases or pests, thereby preventing widespread outbreaks. The tool has demonstrated a 90 percent successful detection rate for significant diseases and common pests. It has been tested in countries such as Colombia, the Democratic Republic of the Congo, India, Benin, China, and Uganda. However, it remains unclear if this tool has been widely tested and made available to the masses in India.
Likewise, in farm management, there are AI solutions like SmartFarm that collect weather and field information, monitor potential risks, and capture precise details such as location, farm size, farmer information, and crop specifics from the pre-harvest stage. These tools use AI/ML-based predictive solutions to gather historical crop data throughout their growth cycle. However, it remains unclear if these tools have been widely tested and made available to the masses in India. Despite their potential, they are certainly not accessible to a significant number of farmers in the country.
The pattern above is emblematic of AI-based solutions in India—there are innovative tools, but they are often only in the testing phase or available to a select few. Given the scale of India’s agricultural employment and the persistent problems faced by farmers, scaling these solutions becomes a top priority. However, this is a challenging task due to the sheer magnitude of the agricultural sector and the complexities involved.
Where do we go from here? Often, AI is suggested as a solution for all problems, but a more pragmatic and realistic approach is necessary. This does not mean AI is not a solution for these issues—it certainly can be—but perhaps not at the current stage in India’s growth story, considering the country’s infrastructural and population challenges. The key to implementing AI solutions for agriculture at scale lies in two main factors. In principle, it is about access and affordability; in practice, it involves increasing internet penetration and ensuring the accessibility and affordability of devices capable of running these AI solutions.
AI solutions are undeniably remarkable, but the unique challenges posed by India's vast population and significant income inequality render these solutions inaccessible to many.
AI solutions are undeniably remarkable, but the unique challenges posed by India's vast population and significant income inequality render these solutions inaccessible to many. Thus, any forward-looking strategy must conscientiously address these critical prerequisites. However, what about the problem itself? It is paramount to understand the magnitude of the issue, which necessitates obtaining reasonably accurate data on the state of agriculture in India. Furthermore, comprehending the boundaries of AI's capabilities is essential. AI represents merely one among many potential solutions. What is needed are pragmatic, tangible implementations rather than predominantly theoretical propositions.
India has, indeed, undertaken several commendable initiatives that have benefited a large number of people, positioning itself as a pioneer in AI development globally. However, as it strives to achieve its development goals, it is essential to assess the extent to which emerging technologies are being utilised. Reliable and up-to-date data, along with affordable and efficient internet and infrastructure, takes priority over the appeal of new solutions offered by emerging technologies. They also serve as the backbone for such solutions. This understanding is crucial for addressing the root of the problem effectively and ensuring that AI solutions can be implemented at scale.
Sauradeep Bag is an Associate Fellow at the Observer Research Foundation
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Sauradeep is an Associate Fellow at the Centre for Security, Strategy, and Technology at the Observer Research Foundation. His experience spans the startup ecosystem, impact ...
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