AI's centre of gravity is shifting: India and the rise of real world AI adoption
AI leadership is no longer concentrated in Silicon Valley, and that's a bigger shift than it first appears.
India recently hosted the India AI Impact Summit, bringing together global tech leaders, policymakers, and AI builders to talk about how AI actually gets built, governed, and scaled in the real world. What stood out to me wasn't only who was in the room, it was where the conversation is now happening.
From "what's possible" to "what's deployable"
For a long time, AI strategy was shaped primarily by a handful of tech hubs. The centre of gravity is now expanding toward regions where:
- scale meets real constraints
- adoption matters more than demos
- AI has to work across genuinely diverse populations and use cases
That changes the whole narrative. It becomes less about what's possible in theory and more about what's deployable, governable, and sustainable at scale. Those are very different problems. A demo has to work once, in a controlled setting, in front of an audience. A deployed system has to work continuously, for millions of people, under conditions nobody fully anticipated.
Why this matters through a data and engineering lens
AI systems don't succeed just because the models are powerful. They succeed when the unglamorous parts hold up:
- data pipelines are reliable : the model is only as good as the freshness and integrity of what feeds it
- governance is baked in : not bolted on after an incident, but designed into the system from the start
- the system operates under real-world constraints : patchy connectivity, many languages, uneven data quality, cost ceilings
In many ways, regions like India force AI to grow up faster. When you have to serve enormous scale on real constraints, you can't hide behind a clean benchmark. The pipeline either holds or it doesn't.
The harder, more interesting problem
The next phase of AI won't be defined only by model breakthroughs. It will be defined by where and how those models get applied in reality, and that's a much harder, much more interesting problem to solve.
It's also exactly the problem data engineering exists to solve: making systems reliable, observable, and trustworthy at scale. The model gets the headline. The data platform decides whether it survives contact with the real world.
I'm Yash Agarwal, a Data Engineer II at Amdocs in Pune, India. I write about building reliable, large-scale data platforms and the realities of deploying AI at scale. You can find more of my work on my portfolio or connect with me on LinkedIn.