New Delhi, September 22, 2026: Artificial intelligence is steadily becoming part of India’s healthcare transformation, with hospitals expected to increase their investment in AI technologies over the coming years despite difficulties in taking experimental projects into routine use.

A recent CII-PwC report indicates that healthcare organisations in India are showing strong interest in AI, but the technology is still at an early stage of large-scale implementation. The findings are based on a survey of 28 senior healthcare leaders conducted in August 2026. According to the report, 93% of respondents believe artificial intelligence can improve efficiency in healthcare, while 64% said their organisations had already tested AI applications through pilot projects. Only 11% reported that such applications had progressed into production environments.

Strong Interest, Limited Large-Scale Deployment

The difference between experimentation and full implementation highlights one of the central challenges facing India’s healthcare technology sector.

Hospitals are exploring AI for a wide range of activities, including patient communication, appointment management, remote monitoring, medical imaging, clinical decision support, documentation, claims processing and supply-chain operations.

However, successfully testing an AI system does not automatically mean that a hospital can integrate it into everyday operations. Healthcare institutions need reliable data, appropriate digital infrastructure, trained employees and clearly defined responsibilities before an AI solution can be used at scale.

The CII-PwC findings suggest that these practical issues are currently more significant obstacles than funding alone.

Data Quality Emerges as a Major Challenge

Data has emerged as one of the most important factors determining whether healthcare AI projects can move beyond the pilot stage.

The CII-PwC survey found that 36% of respondents identified data quality as the biggest reason AI deployments become stalled. Change management was identified by 21%, while clinician adoption accounted for 14%. Only 7% pointed to budget limitations as the primary obstacle.

Healthcare AI systems often depend on large amounts of accurate and properly structured information. If patient records are incomplete, inconsistent or stored across disconnected systems, an AI application may struggle to produce dependable results.

This makes digital infrastructure and data governance important parts of the AI adoption process rather than secondary technical considerations.

AI Could Reduce Administrative Pressure

One of the most immediate opportunities for AI in hospitals is reducing repetitive administrative work.

AI-powered systems can potentially assist with documentation, appointment scheduling, discharge planning, patient communication and other routine processes. By automating selected tasks, hospitals could allow doctors, nurses and other healthcare workers to spend more time on activities that require direct human involvement.

A separate Bain & Company and HealthQuad report has also highlighted operational applications as an important early area of AI adoption in Indian healthcare. According to that report, AI deployment is currently moving faster in administrative and operational functions than in many clinical applications.

Medical Imaging and Clinical Decision Support

AI is also being evaluated in areas closer to clinical care.

Medical imaging and diagnostic applications have attracted considerable interest because AI systems can analyse large quantities of medical information and potentially assist healthcare professionals in identifying patterns.

Clinical decision-support systems are another area being explored. However, these technologies are generally intended to support healthcare professionals rather than independently replace clinical judgment.

The wider adoption of clinical AI will require appropriate validation, oversight, accountability and integration into existing medical workflows.

Smaller Hospitals Face Different Challenges

AI adoption is unlikely to progress at the same pace across India’s healthcare system.

Large hospital chains in major cities generally have greater access to digital infrastructure, electronic medical records and technology teams. Smaller and mid-sized hospitals may face greater difficulties because of limited digitalisation and fragmented records.

A Bain & HealthQuad report estimated that electronic medical record adoption in India remains around 35%, with adoption concentrated among larger urban providers.

This means that expanding healthcare AI will require more than simply purchasing new software. Hospitals also need the underlying digital systems required to supply reliable information to those technologies.

Government Initiatives Add Momentum

India has also been developing a policy and infrastructure framework around responsible health-related AI.

Earlier in 2026, the Ministry of Health and Family Welfare launched the Strategy for Artificial Intelligence in Healthcare for India (SAHI) and the Benchmarking Open Data Platform for Health AI (BODH). The initiatives were presented as efforts to encourage a responsible, transparent and people-centred AI ecosystem in healthcare.

Such initiatives could contribute to the development of common standards, better data practices and more responsible use of AI technologies.

From Technology Experiment to Healthcare Infrastructure

The latest industry findings suggest that India’s healthcare AI story is moving beyond simple experimentation. Hospitals are increasingly evaluating where AI can produce measurable improvements, but converting successful pilots into permanent systems remains difficult.

Experts and industry reports point toward several requirements for the next phase: stronger data platforms, effective governance, workforce training, clinical validation and clear measurements of outcomes.

The CII-PwC report also emphasises that hospitals should establish measurable objectives and ownership before attempting to scale an AI application.

For India’s healthcare sector, the coming years could therefore be less about whether hospitals are interested in artificial intelligence and more about how effectively they can integrate it into real-world healthcare operations.

As investment increases, the focus is likely to shift from AI demonstrations and pilot programmes toward technologies that can operate reliably within hospitals while maintaining appropriate clinical oversight, data protection and patient trust.