India runs one of the most complex digital systems on Earth, quietly processing around 20 billion transactions every month. That reality makes the country a living stress test for artificial intelligence, not at the lab level, but where it actually counts: across institutions, workflows, and everyday life.
The AI Boom Is Real. Adoption, Less So.
Money is not the problem. Investment in AI has exploded, pouring into chips, data centers, foundation models, and software layers at a pace that would have seemed wild just a few years ago.
New models appear constantly. Budgets keep growing.
And yet, across governments, companies, and social systems, the same uneasy conclusion keeps surfacing. AI works. But using it at scale is proving far harder than building it.
Electricity existed decades before factories reorganized themselves around electric motors. Early adopters simply replaced steam engines with electric ones and saw limited gains. Productivity jumped only when workflows were rethought from the ground up.
AI is hitting a similar wall.
The limitation now is not technical performance. It’s whether institutions and organizations are ready to absorb such a general-purpose technology without breaking trust, accountability, or daily operations.
Institutions vs Organizations: The Real Bottleneck
To understand why AI adoption stalls, it helps to separate two ideas that often get blurred.
Institutions are the rules of the game. They include standards, incentives, laws, oversight, and accountability systems. Their job is to reduce uncertainty and make new behavior safe, predictable, and trusted.
Organizations are players inside those rules. They change workflows, retrain staff, and adjust decision-making once the environment allows it.
AI adoption fails when institutions lag.
Without clarity on liability, data governance, bias, and responsibility, organizations hesitate. Not because they dislike innovation, but because risk becomes unmanageable.
India’s experience with population-scale digital systems shows this tension clearly.
India’s Digital Stack Offers a Rare Lens
India is not new to operating at national scale. Its digital public infrastructure already supports identity, payments, education, and service delivery for more than a billion people.
At the center of that system sits Aadhaar, the country’s digital identity framework that reaches over 1.4 billion residents. It became foundational not because of clever code alone, but because institutions evolved alongside it.
One of the architects of that effort, Shankar Maruwada, now CEO and co-founder of the EkStep Foundation, argues that AI faces a similar inflection point.
Maruwada was part of the founding team of Aadhaar and helped drive its adoption nationwide while working at the Unique Identification Authority of India. His work since has focused on population-scale digital systems with governments and institutions, including the MIT Media Lab.
His argument is blunt: technology is no longer the constraint. Readiness is.
Scale Changes the Rules Entirely
India processes about 20 billion digital transactions every month across payments, authentication, welfare, and services. At that scale, even small design flaws amplify quickly.
AI systems trained or deployed in such environments cannot rely on informal fixes or after-the-fact corrections. Errors ripple fast. Bias compounds. Trust evaporates.
This is why India is not just another market for AI. It is a proving ground.
If AI systems can be embedded safely into India’s institutional frameworks, they can likely work anywhere. If they fail here, they will struggle elsewhere too.
That’s what makes India such a revealing test case.
Lessons From an Older Industrial Shift
Germany pioneered industrial chemistry. But it was the United States that spread it widely by embedding chemical thinking into manufacturing, food production, and consumer goods. The shift required more than labs.
Institutions evolved. Education systems changed. The discipline of chemical engineering emerged and connected invention with daily production.
AI now needs its own version of that moment. Not just better models, but new norms around decision authority, human oversight, and accountability.
India’s experience suggests that these shifts must happen together, or not at all.
Workforce Reality, Not Just Automation Dreams
One of the most underestimated challenges in AI adoption is workforce transformation.
AI does not simply replace tasks. It reshapes how decisions are made, who is responsible, and how errors are handled. That unsettles hierarchies fast.
Angela Chitkara, founder of the US–India Corridor and faculty member at Columbia University School of International and Public Affairs, has worked closely on governance and workforce issues tied to Aadhaar and similar systems.
Her view is pragmatic. Large systems only change when people trust them enough to use them under pressure.
That trust is institutional, not emotional.
Employees need clarity. Citizens need recourse. Managers need explainability. Without those, AI remains a demo, not a tool.
Why Many AI Pilots Stall
Across sectors, the pattern repeats.
A pilot launches. Results look promising. Scaling begins. Then hesitation sets in.
Because edge cases explode. Accountability gets murky. Teams argue over who owns outcomes when AI-assisted decisions go wrong.
In smaller systems, workarounds exist. At India’s scale, they don’t.
That’s why AI adoption there is less about speed and more about sequencing. Identity came first. Payments followed. Open data standards evolved. Only then did transaction volume surge.
AI will likely follow the same path.
India as a Global Signal
The rest of the world is watching, whether it admits it or not.
If India manages to embed AI into public services, education systems, and market infrastructure while maintaining trust, it sends a powerful signal. AI can work in messy, diverse, high-volume environments.
If it stumbles, the lesson is equally clear.
AI is not a plug-in. It’s a structural shift.
The uncomfortable truth is that invention has outrun absorption. Models are ready. Institutions are not.
India’s scale makes that mismatch impossible to ignore.
The Real Test Ahead
AI’s future will not be decided by benchmark scores or headline investments. It will be decided in places where systems meet people at scale.
India sits right at that intersection.
Its digital infrastructure already handles volumes that would overwhelm most countries. Its governance challenges are real. Its diversity is vast.
As AI moves from possibility to practice, India may answer the question everyone else is quietly asking: can this technology truly become part of how societies function, or will it stay stuck on the sidelines?
