For the last few years, much of the conversation around AI in healthcare has centred on chatbots and symptom checkers. I understand why. They are visible, easy to demonstrate, and easy to explain. But they represent only the most obvious layer of what AI can do.
The more consequential shift begins when AI starts influencing decisions. In healthcare, and particularly in health insurance, those decisions carry real consequences. Should a claim be reviewed again? Does a policy exclusion genuinely apply? Is a delay necessary, or is it simply creating friction for a patient who needs support?
These are not back-office questions. They determine whether health insurance delivers on its fundamental promise when people need it most. This is where healthtech needs to move from simply processing information to building genuine health intelligence.
One national market, wildly different realities
India makes this challenge particularly complex. We are building healthcare systems at an enormous scale, but the realities across patients, hospitals, geographies and levels of access can be dramatically different.
That matters for AI. A claims model trained on narrow or unrepresentative data may perform impressively in a controlled environment and behave very differently when deployed across India’s actual healthcare ecosystem.
The government has recognised this challenge. In February 2026, the Ministry of Health and Family Welfare launched SAHI the Strategy for Artificial Intelligence in Healthcare for India, alongside BODH, a platform designed to evaluate health AI models against real-world data while protecting the underlying datasets. The framework places emphasis on governance, data stewardship, validation and monitoring before AI systems are deployed at scale.
That is the right direction. In healthcare, a wrong AI recommendation is not simply a technical error. It can delay treatment, create financial stress or undermine trust at precisely the moment a patient needs certainty.
Speed isn’t the win. Better decisions are
There is an obvious opportunity for AI to make claims processing faster. It can extract information from medical records, identify inconsistencies, summarise clinical documentation and help claims teams prioritise cases that need closer attention.
But speed cannot be the end goal. A system that approves or rejects claims faster is not necessarily making the insurance experience better. If anything, faster decisions without transparency can simply move the trust problem further downstream.
The real opportunity is to improve the quality of the decision itself, making it clearer, more consistent and traceable.
If an AI system flags a claim for review, the person reviewing it should understand why. If it identifies a possible policy exclusion, that conclusion should be traceable to the underlying medical and policy information. And when the system is uncertain, uncertainty should influence the next step rather than disappear behind a confidence score.
That is what responsible decision-making with AI should look like.
Three things claim AI needs to get right
-
Evidence before scale. Claims models need to be tested against diverse Indian data and evaluated on real-world outcomes, not just performance on clean benchmarks. BODH is an important step because it creates a framework to evaluate health AI before it is deployed more widely.
-
Humans remain accountable. AI can help claims teams identify patterns, flag risks, and reduce repetitive manual work. But when a decision can materially affect a patient’s health or finances, there must be clear human accountability. AI should strengthen the judgement of the claims professional, not make responsibility harder to locate.
-
Technology has to fit the workflow. The most sophisticated AI model will not create value if claims teams, hospitals, or policyholders cannot use it effectively. The industry’s challenge is therefore no longer just model capability. It is implementation, interoperability, adoption, and trust.
This is why the next phase of healthtech will not simply be about building smarter models. It will be about embedding intelligence into the systems through which healthcare decisions are actually made.
The real race
The next healthcare AI revolution will not be won by whoever builds the most impressive chatbot. It will be won by organisations that can use AI to make better decisions at scale, while keeping those decisions transparent, evidence-based, and accountable. India has already started building the digital foundations for this future through the Ayushman Bharat Digital Mission, which is designed to build a digital health ecosystem and enable interoperable digital health infrastructure. SAHI now adds a national framework for thinking about how AI should be developed and deployed responsibly.
The opportunity for the healthcare and insurance industry is to take these principles from policy documents into everyday workflows that policyholders can actually trust.
Because the question is no longer whether AI can process a claim.
The real question is whether we can build AI that helps get the decision right, for the right reason, for the right patient and knows when a human needs to make the final call.
That is where health intelligence will create its real value. And that is where the next healthcare AI revolution will be won.