India is increasingly positioning itself as a major hub for Artificial Intelligence (AI) development, infrastructure and innovation. This ambition was evident earlier this year when policymakers and industry leaders gathered in New Delhi for the India AI Impact Summit. Among the issues discussed was how countries should approach the governance of AI as these technologies become more deeply embedded in economic and social systems.
With more than 900 million internet users and a rapidly expanding digital economy, India is emerging as an important centre for AI innovation. Multinational companies are expanding engineering and data science operations in the country, while domestic technology firms are increasingly integrating AI tools into digital platforms and services. Investment in computing infrastructure, data centres and research capabilities is accelerating as demand for AI applications grows.
Yet the rapid spread of AI technologies is also raising complex governance questions. Automated systems are increasingly used in decisions related to credit, employment, customer services and digital platforms. As these tools influence economic and social outcomes, policymakers are beginning to examine how regulatory frameworks can respond to the risks associated with algorithmic decision-making.
Globally, many regulatory efforts have focused on governing AI systems themselves. Several jurisdictions have introduced frameworks aimed at regulating high-risk applications, particularly those used in sensitive areas such as employment, finance or public services. These rules typically impose requirements related to transparency, risk assessment and human oversight.
However, focusing exclusively on algorithms captures only part of the governance challenge. Artificial intelligence does not exist in isolation. It also depends on infrastructure, computing resources, data systems and specialised talent. As AI adoption expands, the policy environment surrounding these enabling systems is becoming just as important as rules governing the technology itself.
India’s evolving AI ecosystem illustrates this dynamic.
One major driver has been the rapid expansion of Global Capability Centres operated by multinational firms. India now hosts more than 1,500 such centres, many of which conduct advanced work in artificial intelligence, cloud engineering and data analytics. These facilities increasingly function as global research and engineering hubs, supporting the development of machine learning systems and managing large datasets used across international markets.
The growth of these centres has been accompanied by a parallel expansion of digital infrastructure. Modern AI systems require vast computing power and data storage capacity, making high-performance computing facilities and data centres critical components of the technology stack. Several Indian states have introduced policies aimed at attracting investment in this infrastructure. Maharashtra, for example, has incorporated incentives for data centre development within its IT and IT-enabled services policy, including stamp duty exemptions and electricity duty concessions for new facilities.
The scale of infrastructure required to support AI is significant. India’s data centre capacity is projected to increase from roughly 1.4 gigawatts today to nearly 9 gigawatts by 2030. If these projections materialise, such facilities could account for around three percent of the country’s total electricity consumption. Similar concerns about energy usage and environmental impact are already shaping regulatory debates in other jurisdictions.
Another layer is the semiconductor supply chains. Advanced AI systems depend on specialised chips and high-performance processors. Recognising the strategic importance of this sector, India has introduced the India Semiconductor Mission, a national initiative that offers financial incentives for semiconductor fabrication, packaging and manufacturing facilities. Investments announced by companies such as Tata Electronics reflect an effort to develop domestic capacity in a field that remains essential to the global AI industry.
Alongside infrastructure and hardware, human capital remains a defining component. India’s large base of engineers and software professionals has long supported its position as a global technology services hub. Universities and technical institutes are expanding programmes in machine learning, data science and artificial intelligence as demand for specialised skills grows. Regulatory bodies such as the All India Council for Technical Education have also introduced frameworks allowing engineering institutions to launch dedicated programmes in these areas.
AI is also beginning to reshape the technology labour market itself. Several large IT services firms have reported workforce restructuring and slower hiring as generative AI tools automate certain coding and support tasks. For many companies, this shift represents a transition rather than a contraction. Technology firms are investing in workforce reskilling as AI tools reshape traditional software roles. Industry bodies such as NASSCOM report that major technology companies are expanding internal training programmes in AI, data engineering and cloud computing, while global studies by the World Economic Forum indicate that companies across sectors are prioritising reskilling to prepare employees for AI-driven changes in the workplace.
Research institutions form another important pillar. Universities, public laboratories and corporate research centres contribute to advances in machine learning models and applied AI technologies. Sustained investment in research capacity will determine whether countries merely deploy AI systems developed elsewhere or actively participate in shaping their development.
These developments raise an important policy question: How should such rapidly expanding AI ecosystems be governed?
At present, there appears to be no dedicated AI legislation on the immediate horizon in India. In the absence of a comprehensive AI statute, the deployment of AI systems is likely to continue being shaped by a combination of existing regulatory frameworks.
Some of these laws apply directly to digital technologies, including criminal law, consumer protection statutes, intellectual property rules and contract law, which influence how AI-enabled services operate. At the same time, environmental approvals, electricity laws and land acquisition rules influence where computing infrastructure can be built and how it operates. Labour and education policies, including initiatives aimed at workforce reskilling and technical training, also shape the talent pipeline required for AI development.
However, most of these regulatory frameworks were developed long before the emergence of modern AI technologies. As a result, they do not fully address risks associated with automated decision-making, algorithmic bias or the generation of inaccurate outputs by AI models. At the same time, infrastructure, environmental and labour regulations were not designed with the rapid expansion of AI-driven computing infrastructure and digital industries in mind.
These limitations reflect a larger structural issue. Artificial intelligence operates within a complex technological and economic environment that includes computing infrastructure, semiconductor supply chains, research institutions and specialised talent. As the earlier discussion illustrates, the governance challenges posed by AI extend well beyond the behaviour of algorithms themselves.
For India, this suggests that the future of AI regulation may emerge less from a single comprehensive statute and more from a layered policy approach addressing the infrastructure, institutions and talent that supports AI development.
This article is authored by Kalindhi Bhatia, partner, BTG Advaya.
