Bain & Company and HealthQuad, launched their joint report ‘AI in Indian Healthcare Delivery’ today. The report finds that artificial intelligence (AI) capabilities have advanced rapidly in recent years, and India’s healthcare infrastructure is well-positioned to accelerate the adoption and scale of AI compared with earlier waves of digital technologies. Government initiatives, rising EMR penetration, deployment of private capital, a thriving start-up ecosystem and clinician acceptance are strengthening the enabling environment. Adoption in hospitals, however, remains nascent and uneven. The majority of providers are still running AI pilots, with meaningful scale limited to operational use cases, and only a handful of providers are expanding into more clinical applications.
This gap, in tandem with a step-change in AI’s technological capabilities, particularly in healthcare, opens up previously unchartered territories for exponential growth. Newer generative and agentic systems can increasingly execute multistep workflows with limited supervision, while the amount of expert-level work AI can complete autonomously has been doubling every six to nine months since 2023.
For providers, this creates an opportunity to reduce the administrative burden on doctors, nurses, and other healthcare professionals, giving them more time to focus on patient care and higher-value clinical work.
Today, however, most providers are testing AI in controlled settings rather than deploying at scale. Early adoption is translating first into operational and workflow applications where implementation is relatively easier, and benefits can be measured more quickly. Clinical AI adoption is emerging primarily among more mature providers, though the focus remains on support tools rather than autonomous decision-making. The velocity of change in AI technology currently exceeds that of most healthcare organizations. As hospitals build the data foundations and digital infrastructure needed to scale, AI adoption in the provider space is poised to accelerate and the gap between early movers and the rest will widen quickly.
Dhruv Sukhrani, Head of Bain & Company’s Healthcare & Life Sciences practice in India, said, “AI adoption in Indian healthcare is still early, but the conditions for it to scale are strengthening quickly.
The technology itself has advanced significantly; the harder question now is how providers redesign workflows, manage change and build trust among doctors and nurses. This is increasingly a business transformation challenge, not simply a technology challenge. Providers will need to focus on the highest-value use cases, build the data and organisational capabilities to scale them, and embed AI into clinical workflows with the right governance and human oversight. The next phase of adoption will be shaped by providers’ ability to bring value, deployability and trust together—not simply by access to more advanced AI models”
As AI capabilities advance rapidly, India is strengthening the foundations needed for adoption, with tangible progress beginning to emerge, with tangible progress beginning to emerge. Frontier models now match or outperform pre-licensed medical professionals in some controlled clinical reasoning tests. At the same time, these capabilities are becoming more accessible: the cost of frontier AI models has fallen by approximately 92% since 2023. India, meanwhile, has made progress in building the foundations AI needs.
But three enablers will determine how quickly these foundations translate into healthcare AI at scale.
First, data readiness: EMR adoption in India at ~35% remains well below the US and UK and is concentrated among larger urban hospital chains, while most small- and mid-sized hospitals continue to rely heavily on paper records. Second, regulatory clarity: India’s framework for adaptive and autonomous clinical AI is still evolving, particularly around accountability, data governance and clinical validation. Third, locally applied talent: India has deep AI capabilities, but much of that talent is currently directed toward global markets. Indian start-ups are already building across the patient journey: pre-visit and access, diagnostics and inpatient treatment, and post discharge care. As the ecosystem matures, the winners are likely to be those that go beyond the AI model itself: solving for clinical validation, workflow integration, local data and clinician trust.
Namit Chugh, Director – HealthQuad said: “Healthcare in India has always been constrained by scarcity of clinicians leading to enormous variation in access and outcomes. AI can potentially change that equation by being not just an efficiency lever, but a capacity multiplier. AI capabilities are advancing exponentially, with performance increasingly matching or exceeding medical experts across selected tasks. The report brings a clinician-first lens across the patient journey, which is closely aligned with the investment thesis of HealthQuad’s Fund III, which is focused on backing healthcare innovators using technology and AI to address critical gaps across care delivery. The fund’s recent investment in LifeSigns, an AI-powered remote patient monitoring platform, is an example of this thesis in action. Our role is to back founders solving problems across the complex patient journey and help take those solutions from a point of value to a meaningful platform.”
The next opportunity is to move from operational gains to more connected patient care.
As AI moves deeper into clinical workflows, integration, data readiness, and trust become more significant constraints, with providers typically requiring human oversight. Looking ahead, the report identifies significant headroom in a few areas. Remote patient monitoring, operating theatre and ICU optimization, and post-discharge chronic disease management remain whitespaces. For start-ups, providers without strong in-house technology capabilities represent a major opportunity. Because data and EMR readiness remain key constraints for these providers, demand is shifting toward integrated platforms that combine AI with the underlying data infrastructure needed to deploy it.
For hospitals, capturing value from AI means treating it as a business transformation, not an IT project. This requires anchoring AI to clinically owned outcomes, sequencing adoption carefully, building both specialist AI capability and broad organizational fluency, and governing clinical AI on an ongoing basis rather than through a one-time sign-off.
On the other hand, for founders, building durable healthcare AI companies will require solving real clinical problems and earning trust to scale. The most important steps: validate the problem in clinical settings before building; land with a focused solution before expanding into a platform; treat workflow integration as core to the product, not an afterthought; and build validation, explainability, and patient safety in from the outset.
Ultimately, scaling AI in Indian healthcare will depend on bringing value, deployability and trust together, while building the data, workflow and clinical foundations required for adoption.