Introducing The AI Shift by Inc42, our all-new weekly newsletter that delves deep into the world of artificial intelligence, LLMs, big tech giants and the major trends sweeping the Indian startup and tech ecosystem. Here’s the first edition; do send us your feedback and suggestions so we can improve as we go along!

AI has moved decisively beyond pilots and experimentation. For startups today, the question is no longer about whether to use AI, but where its autonomy should stop, and human judgment must take over.

Over the past year, Indian startups have embedded AI across product development, customer support, sales, and internal operations. Development cycles have compressed, costs have fallen sharply, and leaner teams have become the new default mode. In some cases, entire workflows are now run end-to-end by machines.

But as AI systems take on greater responsibility, founders are confronting a hard puzzle. Full automation may scale efficiency, but it also scales risk. When humans are removed entirely from the loop, failures become harder to explain, trust erodes faster, and accountability breaks down. This is why keeping humans in the loop is no longer a choice, even for those who boast of being AI natives.

Human-AI: No Longer Optional

While in theory AI promises frictionless execution, Indian startups are coming to terms with the fact that blind automation introduces new fragilities.

For Ganesh Gopalan, cofounder and CEO of Gnani.ai, the issue runs even deeper. AI systems, he argues, learn patterns, not meaning. “A system can hear words and still get the intent wrong unless someone has shown it through real examples,” he says.

This is where human-in-the-loop becomes indispensable.

Human feedback during training helps refine models, correct drift, and ground AI systems in cultural and linguistic context before they are deployed at scale. Without this layer, models may perform well in controlled tests but struggle in live conversations, particularly in multilingual, high-context markets like India.

Gopalan said that early human involvement here helps prevent costly errors, protects the brand, and strengthens customer trust.

“A simple example illustrates this well. If a customer says ‘network jaa raha hai’, the AI may process it literally, but a human understands that it usually signals a connectivity issue. Capturing such insights during training ensures the model handles similar cases correctly in the future,” he added.

On the other side, humans with AI help amplify the output.

“AI by itself will probably be able to go about 55%. But a human agent in collaboration does 65%,” says Shayak Mazumder, the CEO of Adya.ai. That incremental improvement is not marginal. It is often the difference between an AI system that merely functions and one that users trust.

For startups operating in trust-heavy or regulated sectors, human oversight is fast becoming the control layer that prevents AI from becoming brittle at scale.

AI-Hybrid Teams Power Startups

One of the clearest effects of AI adoption in Indian startups is the compression of teams and hierarchy.

According to Neeti Sharma, the CEO of TeamLease Digital, AI is pushing startups toward leaner, capability-led organisation design.

“Hiring is moving away from volume toward output per employee, with startups favouring smaller, cross-functional teams supported by........

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