As the world adapts to the rapid ascendancy of artificial intelligence (AI), the Indian industry stands at the brink of a transformative era. With robust economic growth on the horizon, India is poised for unprecedented progress and innovation. Nomura projects the Indian economy to surge at an average rate of 7 per cent over the next five years, significantly outpacing the International Monetary Fund’s global growth forecast of 3.2 per cent for 2024. India’s hosting of G20 and the Global Partnership on AI annual meetings in 2023 has solidified a favourable geopolitical climate. India Inc stands in an advantageous position.
India’s AI market is projected to reach $17 billion by 2027, growing at an annualised rate of 25-35 per cent between 2024 and 2027, according to Nasscom. Deloitte’s report, “Generative AI in Asia Pacific: Young Employees Lead as Employers Play Catch-Up,” underscores India’s exceptional response to Generative AI (GenAI). With a large number of students and employees actively engaging with GenAI, India leads among 13 Asia Pacific countries in both use and adoption. The report positions India as a front-runner in embracing the GenAI wave. Recognising this, the government has committed a five-year budget of Rs 10,372 crore for the India AI Mission.
Just as General Electric led the transformation of the electricity and power industry in the early 20th century, and companies like Ford and General Motors spearheaded the automobile industry’s revolution, India Inc holds the potential to drive significant transformation across sectors. By embracing these imperatives, the Indian AI ecosystem can position itself not just as a participant but as a trailblazer, driving inclusive economic growth and innovation for the benefit of its domestic economy and the world.
Indian industries require a tailored approach that aligns AI capabilities with specific sectoral goals. India Inc should map out sectoral challenges, opportunities, and ambitions, complemented with staying abreast of global AI advancements and developing capabilities to drive innovation. The transformative sparks of this journey are already visible in multiple domains.
Consider the logistics sector, which was plagued by inefficiencies in India a decade ago. Traditional AI brought in efficiencies from automation, optimisation, and basic forecasting based on historical data. PandoAI’s roaring success, for example, has consolidated supply chain data residing in multiple silos within and outside the enterprise to offer valuable analytics and services utilised by several Fortune 500 companies. According to the National Council of Applied Economic Research, logistics costs constitute 7.8-8.9 per cent of GDP in 2021-22. Now, with the strategic integration of GenAI, the logistics sector could also uncover hidden valuable patterns, predict disruptions, and design innovative solutions. This can potentially propel the Indian economy towards setting a global benchmark in the sector. The key to unlocking AI’s transformative potential lies in tailored solutions mapped to each industry’s unique needs.
Indian businesses must prioritise R&D. Substantial investment in core compute capabilities and talent will be the cornerstones for developing a successful AI ecosystem in India. Despite generating 20 per cent of the world’s data, India hosts only 2 per cent of data centres — current computing infrastructure represents less than 2 per cent of global capacity — posing a critical bottleneck to technological advancement.
The central government is enhancing computational capabilities, with plans to procure 10,000 graphics processing units (GPUs) within the next 18 to 24 months. India’s National Semiconductor Mission aims to build a domestic chip industry, supported by over $10 billion in production-linked incentives. While these investments will boost processing power, they are not sufficient. The industry must also invest in this domain to meet the growing demands.
Investment must extend beyond infrastructure to encompass talent development and skilling. In 2023, hiring of AI talent in India increased by 16.8 per cent, highlighting a growing emphasis on AI capabilities within the workforce. Despite a significant number of Indian-origin leaders in the global AI workforce, the majority work for international companies. Initiatives like FutureSkills PRIME, a partnership between industry leaders and the government, are essential and should be supported and expanded.
Establishing and adhering to trustworthy AI standards is crucial for building trust and ensuring sustainability. This commitment will lay the foundation for AI’s widespread acceptance and safe operation. The AI era introduces challenges such as bias, data security, and ethical use, which are analogous to issues of cybersecurity and data privacy in the digital age. Addressing these challenges through robust governance and clear regulatory frameworks will foster trust among consumers, partners, and stakeholders. Moreover, with its economic and geopolitical position, India Inc has the likely potential to influence global standards and policies, aligning AI development with social good.
To operationalise this ambition, several essential steps must be taken. First, it is imperative to develop robust AI governance frameworks within companies that address ethical concerns, data security, and bias. Second, transparency in AI algorithms and decision-making processes must remain a priority for each firm. Third, promoting inclusive AI development by engaging diverse perspectives should be prioritised. Finally, investing in ethical AI research through collaborations with academic and research institutions should be institutionalised.
Today, a commitment to strategic vision, robust investment, and trustworthy AI practices is imperative. The convergence of government support and industry involvement has set the stage for India to not just participate but lead in the global AI arena. This is India’s moment to harness the transformative power of AI, inspiring a new era of economic prosperity.
The writer is G20 Sherpa for India. Views are personal
