Why AI inclusion matters more than AI innovation

• by Mitul Thapliyal
• Feb 4, 2026
• 4 min

Trust, governance, and real-world impact define India’s AI moment. Success depends on human-centric design, local relevance, and accountable systems as AI scales beyond infrastructure. These systems translate people, planet, and progress into everyday outcomes across the Global South.

India stands at a one-of-a-kind threshold. Unlike traditional tech discourse, the upcoming AI Impact Summit 2026 is built on a well-rounded framework of seven interconnected chakras, which range from human capital to social empowerment. While computing capacity and data are vital, they are merely the starting point. The true test of inclusion and trust will occur only when artificial intelligence (AI) solutions hit the ground on scale. This is the same test that defined Aadhaar and the Unified Payments Interface (UPI). Now, success hinges on getting the fundamental components right and deliberately designing applications that translate the principles of people, planet, and progress into tangible global action.

Aadhaar and UPI did not succeed because they were technically perfect. Both systems faced early setbacks and public skepticism. They proved that large-scale digital systems could earn legitimacy if they improve everyday outcomes. They must also survive failure through strong governance. Aadhaar reduced leakages in welfare delivery and enhanced people’s experience in regular activities, such as banking and telecom. UPI removed friction in payments. People learned to trust these systems because they failed predictably and could be corrected without permanent exclusion.

India’s AI systems will face the same test but with higher stakes. Traditional digital systems are deterministic. The system denies access if a name, demographic details, or some other parameter does not match exactly. Much of the digital exclusion in India emerged when rigid rules collided with messy lives at the point of transaction. AI changes this dynamic because it is probabilistic. It can assess likelihood and context rather than demand perfect matches. This capability allows AI to function as an exception management layer. In theory, this makes AI a powerful tool to reduce exclusion.

This potential remains conditional on localised relevance and strong governance. Systems trained primarily on data from the Global North often struggle with local contexts and languages. In sectors, such as agriculture, a poorly translated advisory can lead to harmful instructions. For example, a system that translates content from English to Hindi might tell a farmer to “bury” a seed rather than “sow.”

Such errors quickly break fragile trust because a farmer knows that seeds are never buried. In a local context, burial may carry an altogether different meaning associated with finality rather than growth. Users at the margins view technology through the lens of rational risk management. In their world, a nonsensical instruction is a signal of total system unreliability rather than a minor glitch. When the stakes involve livelihoods and food security, even a single such alien output can cause users to abandon the technology permanently in favor of human intermediaries they know.

India’s readiness must be measured by institutional capacity, not infrastructure alone. Currently, the global community, especially the Global South, lacks sector-specific regulations, validation, and certification standards for AI solutions in critical areas, such as health, education, agriculture, and finance. This gap is untenable when AI mediates access to food and identity. The Global South risks a new era of digital imperialism without data sovereignty, where a few powers hold all control.

Full automation or intelligence in public systems is Utopian. A human in the loop is essential to inclusion. AI should support decisions and flag anomalies. Final accountability must remain human for at least the next decade for vulnerable populations. AI holds immediate value when it strengthens frontline workers, which includes banking correspondents, frontline health workers, and agricultural extension workers, who already command social trust. When these actors use AI tools, inclusion improves without forcing direct adoption on those least comfortable with it. Trust flows through people before it flows through the machine.

The global significance of this approach cannot be overstated. The UN Governing AI for Humanity report (2024) states that high-income countries are likely to see a 70% acceleration in AI discoveries during the next three years. For the Global South, that figure is only 30%, with a maturity gap that could take 10 years to close. India provides a blueprint to navigate this gap without falling into new forms of digital dependency. Success will not be defined at summits or in benchmarks. It will be decided quietly in clinics, ration shops, and farms. India’s AI moment will be remembered as a breakthrough only if its systems become as dependable as the human-centric processes they seek to support. Inclusion and trust will decide whether this decade is a breakthrough or a missed opportunity.

India has the opportunity to lead the Global South in the responsible and inclusive adoption of AI. The nation has done this before when it built world-class digital public infrastructure (DPI) at home, then made it a global movement by proactively sharing lessons and technology with the world. Will India be able to repeat the story in AI?

This was first published in “Hindustan Times” on 3rd February 2026.