Artificial intelligence is often framed as a breakthrough technology, a sharp leap in capability that promises to change how economies function. In India, it is increasingly being understood in a more consequential way: as infrastructure.
This distinction matters. Technologies create episodic advantages. Infrastructure reshapes systems. The upcoming India AI Impact Summit 2026, to be hosted on 16-20 February 2026, shows a growing recognition that the real value of AI lies not in isolated demonstrations or benchmark performance, but in its ability to operate reliably, affordably, and at a population scale. The Summit’s central theme, “From Action to Impact,” signals a shift from experimentation to execution, from fragmented adoption to coordinated deployment.
For a country of India’s scale and diversity, this approach is both pragmatic and ambitious.
India’s economic trajectory over the next decade will be shaped by its ability to raise productivity across millions of firms, absorb a growing workforce into higher-value employment, and compete more effectively in global markets. Artificial intelligence intersects with all three objectives, provided it is embedded deeply into the economy rather than confined to a few large enterprises or imported platforms.
The starting point is capability creation.
Across sectors such as manufacturing, commerce, logistics, healthcare, and agriculture, decision quality increasingly determines outcomes. Demand forecasting, inventory planning, pricing, quality control, and risk assessment now operate at speeds and levels of complexity that exceed manual systems. AI enables these decisions to be taken continuously, contextually, and at scale.
When deployed systematically, the effects are cumulative. Even modest improvements in forecast accuracy reduce waste, shorten working capital cycles, and improve reliability. At a national level, this translates into stronger export competitiveness. Indian firms become more predictable suppliers, better aligned with global demand patterns, and more resilient to volatility.
This is why India’s focus on building domestic AI capability carries real economic significance. By investing in sovereign compute infrastructure, open datasets, and interoperable platforms, India is lowering the cost of intelligence across the economy. The IndiaAI Mission’s expansion to over 38,000 GPUs, offered at highly subsidised rates to startups, researchers, and enterprises, fundamentally alters access to high-performance computing. What was once a constraint reserved for a narrow set of well-capitalised firms is becoming a shared national resource
The underlying logic is familiar. When digital payments became cheap, interoperable, and ubiquitous through UPI, innovation followed quickly. Thousands of firms were able to build services that would previously have been uneconomical. As the Minister for Electronics and Information Technology, Shri Ashwini Vaishnaw, has repeatedly articulated, “Just as UPI democratized payments, the IndiaAI Mission will democratize intelligence.” That analogy is not rhetorical. It describes the architecture of India’s AI ambition.
As intelligence becomes more widely available, the emphasis shifts from access to application. This is where India’s demographic profile creates a distinct opportunity.
Much of the global debate around AI and employment is shaped by ageing societies concerned about labour substitution. India’s context is different. Here, the central challenge is raising productivity and employment quality for a large and youthful workforce. AI, deployed thoughtfully, acts as a force multiplier rather than a replacement mechanism.
Human-in-the-loop systems, vernacular interfaces, and decision-support tools expand the range of tasks that can be performed effectively by frontline workers, supervisors, and small business owners. This employment effect is reinforced by deliberate policy choices. The planned network of over 500 AI Data Labs in tier 2 and 3 cities is designed not only to support model development, but to anchor high-quality digital work beyond major metros. Data curation, annotation, monitoring, and localisation are not peripheral activities. They are central to ensuring that AI systems remain accurate, inclusive, and trusted over time
Language inclusion further amplifies this effect. India’s linguistic diversity has historically limited participation in the digital economy. National efforts to support dozens of Indian languages through large-scale translation and voice models are expanding the addressable base of both consumers and workers. A voice-first, vernacular internet unlocks new domestic markets while positioning Indian AI systems for export to regions facing similar constraints.
The implications for export-oriented MSMEs are especially significant. India’s long-term growth depends on narrowing the productivity gap between its largest firms and its vast base of small and medium enterprises. AI delivered as proprietary, high-cost software risks widening that gap. AI delivered as Digital Public Infrastructure does the opposite.
Access to shared compute, open datasets through platforms such as AIKosh, and modular AI services allows smaller firms to benefit from advanced forecasting, compliance, and optimisation tools without prohibitive upfront investment. As these capabilities diffuse, MSMEs integrate more effectively into global value chains, improve reliability, and generate more stable employment.
That movement from access to application is already visible in the way Indian consumer businesses are approaching global markets. Companies that were once built primarily for domestic scale are now designing for international demand far earlier in their lifecycle, particularly in markets such as the United States, the UK, Europe, and the Middle East.
What distinguishes those that succeed is not manufacturing capability alone, but the ability to read fragmented demand signals, price dynamically, and manage supply chains across very different consumer and regulatory contexts. Ecommerce and quick commerce platforms, which work closely with digitally native Indian brands, have seen this shift accelerate as data-led and AI-enabled decision systems move from being optional tools to core operating infrastructure.
This shows a broader change in how India’s export competitiveness is being shaped. The country’s traditional strengths in sourcing depth, manufacturing efficiency, and cost structure are increasingly being amplified by digital distribution and intelligence rather than constrained by geography. Indian brands that treat international expansion as an intelligence problem rather than a logistics problem are able to compete with significantly larger incumbents in mature markets. AI plays a critical role in this transition, not as a visible layer, but as the mechanism that allows younger firms to learn faster, adapt continuously, and operate with a level of precision that was previously limited to much larger global players.
India’s approach also carries significance beyond its borders. Many of the structural conditions that define the Indian economy, such as fragmented supply chains, price sensitivity, and linguistic diversity, are shared across the Global South. AI systems designed to operate under Indian constraints are inherently portable. This positions India as a partner and reference point for other developing economies seeking to harness AI without deepening dependence on external platforms.
Governance underpins this entire effort. Trust is a prerequisite for scale. India’s techno-legal approach, embedding consent, accountability, and harm prevention into system architecture rather than relying solely on ex-post regulation, reflects a pragmatic understanding of how AI systems evolve. Frameworks such as the Data Empowerment and Protection Architecture ensure that data flows can support innovation while preserving user agency. The forthcoming Digital India Act reinforces this focus by centring regulation on user harm rather than stifling experimentation
Taken together, these elements reveal a coherent strategy. AI is being positioned not as a luxury for advanced sectors, but as a general-purpose input into economic activity. Its benefits are expected to accrue across regions, income levels, and industries.
The India AI Impact Summit 2026 represents a moment of articulation and alignment. It brings together policy intent, infrastructure readiness, and applied use cases to demonstrate that AI can be operationalised at a population scale. More importantly, it signals India’s ambition to help shape the global AI narrative, shifting the focus from abstract risk to measurable impact.
If this approach succeeds, the outcomes will be visible in familiar metrics: improved export performance, higher employment quality, stronger MSMEs, and greater strategic autonomy in a data-driven world. But they will also be visible in less easily quantified ways: broader participation in the digital economy, increased confidence among small firms, and institutions that make better decisions at scale.
India’s AI story, increasingly, is not about catching up. It is about demonstrating how intelligence, when treated as infrastructure, can support inclusive growth at a scale few countries can attempt.
Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the publication. |
