Steady rise of AI space in India and the bottlenecks ahead 

AI is still at a fledgling stage in India, but the ecosystem is growing even as there are a host of challenges and trials that need to be addressed and tackled

AI is still at a fledgling stage in India, but the ecosystem is growing even as there are a host of challenges and trials that need to be addressed and tackled 

The Artificial Intelligence (AI) landscape of India is witnessing an unprecedented surge and development. Experts predict that in the coming decades, AI will play a crucial role in the transformation of the country’s digital economy. Companies and businesses have started to unravel the hidden potential of AI by enabling their operations and functions.   

There is scope for fast-pace progression as AI is transformative in nature and companies are steadily realising this. From transforming supply chains globally to being utilised in financial sectors, the range of services that can be improved are immense.  

According to a report by NASSCOM, AI is expected to raise India’s annual growth rate by 1.3% by 2035, amounting to an addition of USD 957 billion, or 15% of current GVA (gross value added) to India’s economy. 

The government of India’s GDP plans FY2026 will see businesses further boom in AI-adoption as India looks to reach greater AI-maturity levels. The digital economy of the country will also be transformed with the rise and development of AI.   

AI Journey of India 

India is now a global player in the AI space. The involvement and development in AI are the drivers behind the changing landscape of AI in the country with more industries getting themselves AI-enabled. 

Moreover, in terms of AI research, India is placed only behind China and the US. In the AI talent and skill space, India is performing extremely well with more than ten AI startups joining the Unicorn Club in 2021. 

The journey of AI in India has taken its time to catch a steady pace of development and growth. Businesses and organizations are in the middle phase of maturity with defined AI strategy. Implementation of relevant AI-cases are also being used to come up with possible solutions.  

Digitalization and AI-adoption in businesses were deemed necessary during the time of pandemic. Operations and functions needed to be more optimized to better serve the consumers and for the frictionless flow of information. These factors came out to be the driving forces behind the digital transformation and AI-adoption in many organizations. Also, there has been expansion of AI from being a backdoor requirement to customer centric ones. 

The key developments is that there has been a more formal approach towards AI-adoption with companies experimenting more with AI PoCs (Proof of Concept). Product development, customer service and IT are among many of the business functions with multiple AI use-cases. 

Investments and budget allocation for the development of AI in organizations have now become a new norm. This is a big development as companies even in 2020 had no budget allocation for AI development. In-house innovation is the key factor behind this move. 

Business with good maturity levels in AI are looking into in-house solutions for AI development rather than sourcing data from elsewhere. These companies are still testing the waters, most of the businesses though look for plug and play solutions for their required purposes.   

Companies have also understood that for effective investment for AI-based purposes, adequate accounting is required to go into sorting Capex and Opex budget allocation. 

Operations and Functions  

India is becoming a global pool for AI talent. Recently, Bangalore ranked fifth in the list of cities with the largest AI talent pool. Multiple GCC’s have invested in AI/ML CoEs in India.  

There is a huge gap in demand and supply with the demand for talent being higher than that of what can be supplied. This is due to the quick progression in the field of AI by businesses. 

Companies are further showing emphasis on better management of finances by setting up PMO (Project Management Office) and focusing on effective project planning. The large scale firms are the ones who are targeting a more efficient way of AI data delivery.  

For this reason and many other, companies rely on contracting for data and other purposes while simultaneously working and building their own internal AI systems and talent. To further support the building of Internal AI systems and talent, government policies are in action. The National Education Policy (2020) emphasizes on AI, this shows the significance of AI and the need to build on it for future advanced requirements.   

According to a report by NASSCOM, 34% firms have designated cross functional AI team structure while 25% rely on contractors. 


Implementation using Data and Technology and its knowledge output 

Data standardization is an aspect that has been focused on by the firms at great lengths. There has been a shift towards building an enterprise wide data lake that can help enhance data flow, readiness and access.  

According to a Nasscom report, 1 out of 2 organizations surveyed have standardization and data readiness at either business unit (BU) or enterprise level.  

Cloud services have also allowed for quick pace progression in scalability and AI implementation across enterprises. This modernization across the various data and technology aspects have also witnessed a shift in data ownership to customers calls for better security norms to store and handle sensitive information of customers.  

Machine Learning (ML) and AI also have been utilized to create AI-powered chat bots with predictive analytics. Other solutions such as deep learning have also been used in fraud and data breach detection among many other uses.  

Although, the knowledge output of India falls short of meeting the global benchmarks for knowledge output.  

A recent study by Brookings Institute shows that India remains out of top 10 nations in terms of knowledge output. 

This is mostly due to the fact that there is very less investment in AI research and innovation in the country. Even though, companies want to chose and prefer AI startup incubation and in-house labs for AI innovation.  

Experts also suggest that more focus should be placed on patents and IP creations. Organizations are not focusing enough on patenting their AI innovations and research. They also suggest that the patenting procedure should be quicker and more efficient. Patent processing in India is 33 per cent longer than the US or China.  

Hurdles in AI-adoption and concerning factors to look into for seamless progression  

There hasn’t been clear and established AI laws or frameworks to govern the companies and firms. AI governance requires dedicated efforts as it’s technical in nature and removing the loopholes will take some time. The rate at which AI is expanding calls for an integrated AI ethical framework. 

Risk management and assessment systems should be in place in organizations. Maturity in AI should go hand-in-hand with the ethical control frameworks.  

Indian government is working on this aspect and there are some key policies that are driving the changes in the ethical frameworks being prepared for AI. From Personal Data Protection (PDP) Bill which aims at creating a balance between the interests of data owner and the data fiduciary when creating economic value from the use of data to NITI Aayog proposing to establish a Council of Ethics and Technology to monitor and govern AI initiatives.  

There are many hurdles that need to be cleared before progressing on to the next phase of AI maturity. There are different impediments in every sector but the reason that tops every list is the problem of barrier in adoption. Experts cite there has been a shift to proof-of-value in addition to proof-of-concept to demonstrate organizational value-addition. The low maturity of external ecosystem of AI still remains a problem, including budget allocation for AI innovations and projects in industrial, automotive and healthcare sectors. 

India is on its path of achieving AI maturity. It is doing so at a steady pace but there are concerns that need to be addressed to create an ecosystem which is guided by AI. For this, India should look into the following below listed factors for a seamless progression in the AI space.  

AI in India is still at an early stage. These starting times are crucial for MSMEs and startups that are looking to match the global benchmarks and levels of operations and functions. MSMEs and startups should pay heed to the changing landscape of AI in India. This is the time to seize the opportunity for MSMEs, at a time when the government too is focusing its GDP plans FY2026 by investing more time and finances into AI, ML and deep learning.