Why do we need India-centric AI?
To understand the likely impact of India-centric AI, one needs to appreciate the country’s linguistic, cultural and political diversity. Historically, India’s DNA has been so heterogeneous that extracting clear perspectives and actionable insights to address past issues and current challenges and moving towards our vision as a country would be impossible without harnessing the power of AI. The scope for AI-focused innovation is tremendous, given India’s status as one of the fastest-growing economies with the second-largest population globally. India’s digitisation journey and the introduction of the Aadhaar system in 2010 – the largest biometric identity project in the world – has opened up new venues for AI and data analytics. The interlinking of Aadhaar with banking systems, the PDS and several other transaction systems allows greater visibility, insights and metrics that can be used to bring about improvements. Besides using these to raise the quality of life of citizens while alleviating disparities, AI can support more proactive planning and formulation of policies and roadmaps. Industry experts concur a trigger and economic growth spurt, opining that “AI can help create almost 20 million jobs in India by 2025 and add up to $957 billion to the Indian economy by 2035.” The current state of AI in India The Indian government, having recently announced the “AI for All” strategy, is more driven than ever to nurture core AI skills to future-proof the workforce. This self-learning program looks to raise awareness levels about AI for every Indian citizen, be it a school student or a senior citizen. It targets meeting the demands of a rapidly emerging job market and presenting opportunities to reimagine how industries like farming, healthcare, banking, education, etc., can use technology. A few years prior, in 2018, the government had also increased its funding for research, training and skilling in emerging technologies by 100% compared to 2017. The booming interest has been reflected in the mushrooming of boutique startups across the country, as well. With a combined value of $555 million, it is more than double of the previous year’s figure of $215 million. Interestingly, analytics-driven products and services contribute a little over 64% of this market -clocking over $355 million. In parallel, the larger enterprises are also taking quantum leaps to deliver AI solutions. Understandably, a large number of them use AI solutions to improve efficiency, scalability, and security across their existing products and services. Current challenges of making India AI-centric There is no doubt that AI is a catalyst for societal progress through digital inclusion. And in a country as diverse as India, this can set the country on an accelerated journey toward socio-economic progress. However, India’s socio, linguistic and political diversity also means more complex data models that can be gainfully deployed within this landscape. For example, NLP models would have to adapt to text/language changes within just a span of a few miles! And this is just the tip of the iceberg as far as the challenges are concerned. Let’s look at a few of them: The deployment and usage of AI have been (and continues to be) severely fragmented without a transparent roadmap or clear KPIs to measure success. One of the reasons is the lack of a governing body or a panel of experts to regulate, oversee and track the implementation of socio-economic AI projects at a national level. But there’s no avoiding this challenge, considering that the implications of AI policy-making on Indian societies may be irreversible.The demand-supply divide in India for AI skills is huge. The government initiatives such as Startup India and the boom in AI-focused startups have only contributed to extending this divide. The pace of getting a trained workforce to cater to the needs of the industry is accelerating, but unable to keep up with the growth trajectory that the industry finds itself in. Large, traditionally run institutions are also embracing AI-driven practices having witnessed the competitive advantage it brings to the businesses. This has added to the scarcity that one faces in finding good quality talent to serve today’s demand.The lack of data maturity is a serious roadblock to establishing India-centric AI initiatives, especially with quite a few region-focused datasets currently unavailable. There is also a parity issue with quite a few industry giants having access to large amounts of data as compared to the government, let alone startups. There is also the added challenge of data quality and a single source of truth that one can use for AI model development.Even the fiercest AI advocates would admit that its security challenges are nowhere close to being resolved. There is a need for security and compliance governance protocols to be region-specific so that unique requirements are met, and yet there is a generalisability that is required to rationalise these models at the national level. There is also a lot of ongoing debate at a global level on defining the boundaries that ethical AI practices will need to lean on. Given India’s diversity, this is a challenge that has been magnified many times over. Niche areas where AI is making an impact Farming The role of AI in modern agricultural practises has been transformational – this is significant given that more than half the population of India depends on farming to earn a living. In 2019-2020 alone, over $1 billion was raised to fuel agriculture-food tech startups in India. It has helped farmers generate steadier income by managing healthier crops, reducing the damage caused by pests, tracking soil and crop conditions, improving the supply chain, eliminating unsafe or repetitive manual labour, and more. Healthcare Indian healthcare systems come with their own set of challenges – from accessibility and availability to quality and poor awareness levels. But each one represents a window of opportunity for AI to be a harbinger of change. For instance, AI-enabled platforms can extend healthcare services to low-income or rural areas, train doctors and nurses, address communication gaps between patients and clinicians, etc. Government-funded projects like NITI Aayog and the National Digital Health Blueprint have also highlighted the need for digital transformation in the healthcare system. BFSI The pandemic has accelerated the impact of AI on the BFSI industry in India, with several key processes undergoing digital transformation. The mandatory push for contactless remote banking experience has infused a new culture of innovation in mission-critical back-end and front-end operations. A recent PwC-FICCI survey showed that the banking industry has the country’s highest AI maturity index – leading to the deployment of the top AI use cases. The survey also predicted that Indian banks would see “potential cost savings up to $447 billion by 2023.” E-commerce The Indian e-commerce industry has already witnessed big numbers thanks to AI-based strategies, particularly marketing. However, for retail brands, capturing market share is among the toughest worldwide – with customer behaviour being driven by diverse values and expectations. Using AI and ML technologies backed by data science would be easier to tap into multiple demographics without losing the context of messaging. Manufacturing Traditionally, the manufacturing industry has been running with expensive and time-consuming manually driven processes. But, slowly, more companies realise the impact of AI-powered automation on manufacturing use cases like assembly line production, inventory management, testing and quality assurance, etc. While still at a nascent stage, AR and VR technologies also see adoption in this sector in use cases like prototyping and troubleshooting. Three crucial data milestones to achieve in India’s AI journey Unbiased data distribution Forming India-centric datasets starts with a unified framework across the country so that no region is left uncovered. This framework needs to integrate with other systems/data repositories securely and seamlessly. Even private companies can share relevant datasets with government institutions to facilitate strategy and policy-making. Localised data ownership In today’s high-risk data landscape, transferring ownership of India-centric information to companies in other countries can lead to compliance and regulatory problems. Therefore, especially when dealing with the healthcare or public administration industries, it is highly advised to maintain data control within the country’s borders. Data ethics and privacy Data-centric solutions that work towards improving human lives require a thorough understanding of personal and non-personal data, matters of privacy and infringement on others. The responsible aspect of managing this information takes the challenges beyond the realms of deployment of a mathematical solution. Building an AI mindset that raises difficult questions about ethics, policy, and law and ensures sustainable solutions with minimised risks and negative impact is key. Plus, data privacy should continue to be a hot button topic, with an uncompromising stance on safeguarding the personal information of Indian citizens. Final thoughts India faces a catch-22 situation with one side of the country still holding to its age-old traditions and practices. The other side embraces technology change, be it using UPI transfers, QR codes, or even the Aarogya Setu app. But sheer size and diversity of languages, cultures, and politics dictate that AI will neither fail to find areas to cause a profound impact nor face fewer challenges while implementing it. As mentioned earlier, the thriving startup growth adds a lot of fuel to AI’s momentum. From just ten unicorns in India in 2018, we have grown to 38. This number is expected to increase to 62 by 2025. In 2020, AI-based Indian startups received over $835 million in funding and were propelling growth few countries can compete with. AI is a key vehicle to ring in the dawn of a new era for India – an India which, despite the diversity and complex landscape, leads the way in the effective adoption of AI.
The scope for AI-focused innovation is tremendous, given India’s status as one of the fastest-growing economies with the second-largest population globally.
The scope for AI-focused innovation is tremendous, given India’s status as one of the fastest-growing economies with the second-largest population globally.
["AI (Artificial Intelligence)", "AI Impacts"]
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["AI", "ML", "NLP", "data science", "analytics", "RAG", "R", "Go", "Scala", "Git"]
Lakshmi Vaideeswaran works as a Vice President - Analytics Consulting at Tiger Analytics. As a Client Partner, Lakshmi ensures all of Tiger’s clients achieve value from their engagement with us and help establish us as a strategic partner in our clients’ AI &amp; analytics journey. Lakshmi comes with 30 years of work experience, and her core strength is technology development and commercialization.