Inside Makers Lab At Tech Mahindra: Building India’s AI Future

India is entering a decisive phase in its artificial intelligence journey, transitioning from rapid experimentation to structured execution at both national and enterprise scale. The acceleration of AI adoption in 2024 and 2025 brought valuable clarity: scaling intelligence across real systems requires more than models and momentum. It demands alignment between policy and platforms, talent and infrastructure, and ambition and accountability.

In parallel, the Government of India has sharpened its AI ambition through the IndiaAI Mission, signaling intent to invest in foundational models, compute infrastructure, and ecosystem collaboration.
The IndiaAI Mission and forums such as the AI Impact Summit 2026 reflect this structural shift. The focus is no longer on demonstrating that AI works. It is on ensuring that it works responsibly, sustainably, and at scale.

Against this backdrop, Tech Mahindra’s Makers Lab offers a useful lens. As an applied innovation and research hub operating at enterprise scale, it sits at the intersection of national ambition and real-world execution.

In a recent leadership conversation with Nikhil Malhotra, these themes surfaced repeatedly. The discussion reflected on what the last two years have taught enterprises, where the IndiaAI Mission stands today, and what India must get right to move from adoption to global AI leadership.
 

What 2024–25 Taught Enterprises About AI At Scale?

By late 2025, nearly 47% of organizations reported having multiple generative AI use cases live in their business workflows. Across industries, AI systems were increasingly embedded into operational and decision-making environments rather than sitting on the periphery.

That transition exposed recurring challenges. Many organizations found that AI outcomes were shaped more by fundamentals such as data readiness, system integration, and governance maturity. The belief that AI alone could unlock transformation gave way to a more grounded reality.

Responsible AI also became harder to abstract. As AI began influencing real-world decisions, questions around explainability, transparency, and accountability moved from theory into practice, often revealing gaps that were not visible during experimentation.

At the same time, AI architecture began shifting. Intelligence moved closer to the edge, and services became increasingly inseparable from the software that delivered them. Success was no longer defined by experimentation speed but by reliability and integration at scale.
For India’s enterprises, the takeaway is clear. The era of experimentation is giving way to the era of accountability. Deployment success is no longer measured by the number of pilots launched but by the number of systems sustained.

Inside The IndiaAI Mission: Enterprise Participation And Progress

The IndiaAI Mission signals a shift in how the country is approaching artificial intelligence. Rather than focusing narrowly on applications, the emphasis is moving toward building foundational capability that can scale across sectors and populations.

At the center of this effort is diffusion. AI is being framed not only as a technological advancement but as an enabler that must work across India’s linguistic, educational, and economic diversity. This requires models, data, and systems that reflect local context rather than relying solely on global abstractions.

Anchored around the pillars of people, planet, and progress, the IndiaAI Mission signals that scale must now be purposeful. The emphasis is not on showcasing capability but on embedding AI in priority sectors such as education, healthcare, telecommunications, mining, and public services, with built-in accountability.

Enterprises have a distinct role in this phase. Beyond adoption, they are increasingly positioned as bridges between policy intent, academic research, and real-world execution. That role spans talent development, applied research, and the translation of emerging capabilities into usable systems.

What stands out is the deliberate narrowing of scope. Instead of broad demonstrations, the focus is on targeted use cases, foundational layers, and long-term capability building. Progress, in this framing, is measured less by speed and more by readiness to scale responsibly.

One Lever That Will Shape 2026 And Beyond

Policy frameworks are evolving. Talent pipelines are expanding. Compute capacity is being strengthened. Yet the one lever that will ultimately determine India’s position in the global AI landscape is the research and development discipline.

The transition from adoption to leadership cannot occur without original research capacity.
India’s enterprises have demonstrated strength in scaling global technologies. The coming phase demands greater investment in originating innovation. Foundational model development, algorithm efficiency improvements, domain-specific architecture, and sovereign dataset optimization require sustained R&D focus.

The global AI environment is moving rapidly. Breakthroughs in model efficiency and architecture optimization have already demonstrated how quickly competitive advantages can shift. Nations that invest in long-horizon research build structural resilience; those that rely solely on integration risk remaining downstream.

Importantly, research investment may not deliver quarterly returns, but it surely delivers compounding advantage over time.

The implication for 2026 is clear: progress will depend on how effectively these levers move in alignment, with research depth serving as the bridge between policy ambition, talent capability, and infrastructure scale.

 

From Adoption To Leadership: What India Must Get Right Next

As India moves toward becoming a global AI leader, the role of large enterprises shifts from merely supportive to structural, serving as the connective tissue between ambition and execution.

One critical responsibility lies in accountability. As AI systems are deployed across industries and society, enterprises employ and influence a significant portion of the AI-enabled workforce. Building awareness around responsible outcomes, risks, and accountability becomes as important as building technical capability itself.

A key challenge lies in the middle of the innovation lifecycle. While academic ideas continue to emerge, many fail to move from experimentation to real-world application. Enterprises must engage earlier to bridge this gap; without that support, promising innovations often remain confined to labs and startups.

Underlying both is the question of research commitment. Foundational research rarely delivers immediate returns, yet it remains essential for long-term leadership. Corporate-backed research environments provide the space to experiment, fail, and iterate, turning ambition into durable capability rather than short-term output.

India’s path to AI leadership depends not only on adoption or scale but on how deliberately enterprises embrace responsibility across accountability, applied innovation, and sustained research.

Perspective And Forward View

India’s AI future will be shaped not by isolated breakthroughs but by consistent, responsible execution at scale.

Moving from AI adoption to global leadership requires alignment across policy direction, platform maturity, talent development, and enterprise commitment. It requires long-term research investment and disciplined deployment.

The IndiaAI Mission reflects this shift toward long-term thinking. So do the enterprise-led efforts that focus on accountability, applied research, and bridging the gap between ideas and impact. Together, they signal a shift from fragmented experimentation toward more deliberate capability-building.

As AI becomes embedded into systems, services, and everyday decision-making, progress will be measured not by speed alone but by whether ambition translates into outcomes that are sustainable, contextual, and trusted.

The next chapter of India’s AI story will be shaped by how effectively these efforts converge.