India’s Finance Minister recently unveiled the Union Budget 2026, outlining major priorities for the country’s economic and technological growth. For years, India has been the place where global companies-built teams. Rarely the place where AI platforms lived, where compute decisions were anchored, or where long-running systems were truly owned. 

What it did instead was more important: it doubled down on policy continuity, infrastructure depth, and long-term operating certainty. That matters, because it mirrors exactly what we’ve been seeing on the ground. 

Over the past few years, we’ve worked closely with global organizations building and evolving their Global Capability Centers (GCCs). And a clear pattern kept emerging. 

The most forward-looking leaders were no longer asking: 
“How do we scale delivery from India?” 

They were asking: 
“How do we build AI platforms here that we can own for the next decade?” 

1. From Talent Base to AI Execution Geography 

Budget 2026 strengthens four shifts that were already underway. 

Where AI systems live start to matter more than where teams sit. 

India is no longer being viewed only as a talent location. We’re seeing enterprises bring training workloads, production systems, and AI operations into India. 

The strategic question has shifted from workforce placement to long-term AI system residency and execution. 

Increasingly, this execution is moving beyond traditional metros. Tier-2 ecosystems are emerging as viable AI execution hubs, offering lower attrition, more stable operating costs, and the ability to plan long-term AI ownership without constant organizational churn. 

2. Policy Certainty as the Real Differentiator 

AI ambition scales only when uncertainty is removed. 

In our experience, large AI programs don’t stall because of lack of vision. They stall because of regulatory and operational uncertainty. 

Multi-year clarity around taxation, compliance, and data-centre operations allows leaders to plan in decades, not quarters. Budget 2026 reinforces exactly that mindset. 

3. AI Infrastructure Is Becoming Real Infrastructure 

Stability, not experimentation, is what enables scale. 

When power, connectivity, cooling, and compute mature together, AI economics change. 
Costs stabilize. Governance becomes practical. Scale becomes predictable. 

This is how serious AI ecosystems form, not through pilots alone, but through infrastructure stability. 

4. GCCs Are Evolving Beyond Delivery 

From cost centres to long-term AI capability hubs. The most effective GCCs we work with today are no longer measured by cost efficiency alone. 

They: 

• Own AI platforms 

• Build data products 

• Run copilots and decision systems 

• Retain institutional and architectural knowledge 

India’s Union Budget 2026 gives these centres the policy runway to be built as enduring AI capability hubs, not short-cycle execution arms. Many of the most durable GCC designs we see today are intentionally anchored outside traditional metros optimizing stability, retention, and long-term system ownership. 

Personally, this Budget felt less like a turning point and more like validation. 

We have already begun investing in helping organizations design GCCs for durability: 
tighter integration between engineering and AI operations, deeper ownership of platforms, and resilience by design. 

Budget 2026 tells us this approach isn’t early. It’s aligned. 

For leaders who stayed on the course through uncertainty and continued to invest with intent, the environment ahead looks more predictable and more enabling. 

The opportunity now isn’t just to grow faster. It’s to build systems that last. 

What are you rethinking about where your AI systems truly belong?