Indian healthcare providers are beginning to move artificial intelligence from pilot projects into routine operations, although adoption remains uneven across administrative and clinical workflows, according to a new report from Bain & Company and HealthQuad.
The report, AI in Indian Healthcare Delivery, finds that operational applications are scaling faster than clinical use cases, as hospitals look to reduce administrative workloads and improve workforce productivity.
AI adoption remains relatively nascent across the provider sector, with most organisations still testing technologies in controlled environments.
Meaningful deployment at scale is currently concentrated in operational and workflow applications, where implementation is easier and benefits can be measured more quickly.
Clinical AI adoption is emerging primarily among more digitally mature healthcare providers and remains focused largely on decision-support tools rather than autonomous clinical decision-making.
The report argues that India is increasingly well-positioned to accelerate healthcare AI adoption as government initiatives, rising electronic medical record penetration, private capital, a growing startup ecosystem and clinician acceptance strengthen the underlying environment.
However, the pace of technological development is currently moving faster than healthcare organisations can adapt.
New generative and agentic AI systems are increasingly capable of performing multistep workflows with limited supervision, while the amount of expert-level work that AI can complete autonomously has been doubling every six to nine months since 2023, according to the report.
For healthcare providers, these capabilities could help reduce administrative burdens on doctors, nurses and other professionals, freeing more time for patient care and higher-value clinical work.
“AI adoption in Indian healthcare is still early, but the conditions for it to scale are strengthening quickly,” said Dhruv Sukhrani, Head of Bain & Company’s Healthcare & Life Sciences practice in India.
“The technology itself has advanced significantly; the harder question now is how providers redesign workflows, manage change and build trust among doctors and nurses.”
He added that AI adoption is increasingly becoming a business transformation challenge rather than simply a technology project.
Providers will need to identify high-value use cases, strengthen data and organisational capabilities and embed AI into clinical workflows with appropriate governance and human oversight.
Data readiness remains one of the most significant barriers.
Electronic medical record adoption in India currently stands at approximately 35%, significantly below levels in markets such as the United States and United Kingdom.
Adoption is also concentrated among larger urban hospital chains, while many small and mid-sized hospitals continue to rely heavily on paper records.
Regulatory clarity represents another constraint, particularly around adaptive and autonomous clinical AI, accountability, data governance and clinical validation.
The availability of locally applied AI talent is also an issue, with much of India’s deep technology expertise currently directed towards global markets.
The report argues that successful healthcare AI companies will increasingly need to go beyond the underlying AI model.
Clinical validation, workflow integration, local datasets and clinician trust are likely to become key differentiators as the market matures.
“Healthcare in India has always been constrained by scarcity of clinicians leading to enormous variation in access and outcomes,” said Namit Chugh, Director at HealthQuad.
“AI can potentially change that equation by being not just an efficiency lever, but a capacity multiplier.”
Indian startups are already developing solutions across the patient journey, from pre-visit access and diagnostics to inpatient care and post-discharge management.
The report identifies remote patient monitoring, operating theatre and intensive care unit optimisation, and post-discharge chronic disease management as areas with significant room for further development.
Healthcare providers without strong in-house technology capabilities may represent another major opportunity for startups.
Because EMR and data readiness remain barriers for these organisations, demand is expected to shift towards integrated platforms combining AI applications with the underlying digital infrastructure required to deploy them.
For hospitals, Bain and HealthQuad recommend treating AI as a broader organisational transformation rather than an isolated IT initiative.
This includes tying deployment to clinically owned outcomes, carefully sequencing adoption, developing specialist AI capabilities alongside broader organisational literacy, and maintaining continuous governance over clinical AI systems.
For healthcare startups, the report recommends validating the clinical problem before building, starting with focused solutions before expanding into broader platforms, treating workflow integration as a core product requirement and incorporating explainability, validation and patient safety from the outset.
Ultimately, the next phase of healthcare AI adoption in India is expected to depend on how effectively providers combine value, deployability and trust.
As digital infrastructure improves and AI capabilities continue to advance, the gap between organisations able to integrate AI into care delivery and those remaining at the pilot stage could widen rapidly.
