“How much productivity can GenAI unlock in India? The AIdea of India” – This is an in-depth exploration of GenAI’s current state in Indian enterprises and key trends shaping its future, brought out by EY for the year 2025.
Because my interests are in the healthcare vertical, I culled out a few takeaways from the report that predict what and how GenAI will do transform healthcare services in India. The analysts who worked on developing the report observe that as these solutions mature and more use cases emerge, GenAI has the potential to transform India’s healthcare sector by enhancing efficiency, improving accessibility, and delivering better patient outcomes.
The report makes it clear that GenAI is emerging as a transformative force, enabling innovations that drive financial inclusion, healthcare access, and educational outreach through localized, cost-effective solutions. The possibilities are vast and promise of profound changes on the horizon.
Broadly, the report indicates that AI promises to potentially uplift billions out of poverty by optimizing resource distribution and revolutionizing industries like agriculture and clean energy. AI might strengthen governance by enhancing public services and reducing corruption. Finally, AI can democratize knowledge access and redefine meaningful human contributions in education and work, ensuring an inclusive future where technology enriches, rather than replaces, human purpose.
The report says in 2024, global investment in GenAI surged, driven by tech giants like Google, OpenAI and Microsoft. Record-breaking funding rounds and open-source contributions from Meta and others intensified competition, while advancements in hardware, such as Nvidia’s Blackwell platform, provided the computational power to support increasingly sophisticated models. Emerging trends, such as Agentic AI and synthetic data generation, expanded AI’s capabilities, enabling autonomous, multi-step tasks and addressing data scarcity. SLMs offered cost-effective solutions for smaller enterprises. Despite concerns like overfitting and model collapse, GenAI’s strides in reasoning, multimodality and adaptability cemented its position as a key driver of innovation and productivity across sectors.
A listicle of key takeaways from the report:
- GenAI is reshaping traditional healthcare models and enhancing outcomes across the industry, from personalized treatment plans and automated diagnostics to virtual health assistants and drug discovery.
- In the domain of healthcare, AI could accelerate breakthroughs in biology, enabling the rapid development of cures for diseases like cancer and Alzheimer’s while extending human lifespans.
- In neuroscience, it offers hope for understanding and treating mental illnesses such as depression and schizophrenia while also enhancing human cognition and emotional well-being.
- GenAI will address resource shortages by enabling remote diagnostics, analyzing longitudinal health data, and delivering personalized care recommendations. These advancements promise to redefine accessibility, empowering communities and creating a more inclusive digital ecosystem across India.
- AI-driven diagnostics, predictive health analytics, telemedicine with AI-driven triage AI-based mobile health diagnostics, offline AI health consultations, regional language support for healthcare apps AI-driven virtual consultations with specialists, medical imaging analysis mobile-based telemedicine with basic AI for symptom checks, local language support for rural health workers AI-led drug discovery, personalized medicine, genetic analysis for tailored treatments Low-cost AI solutions for public health issues, predictive analytics for vaccine distribution in rural areas.
- GenAI is poised to revolutionize India’s healthcare sector, driving productivity and improving patient outcomes enables doctors to identify the next best action for patients during consultations.
- GenAI tools are increasingly being deployed in healthcare organizations, either at pilot or full implementation stages, to address critical needs in clinical assistance and revenue and finance processes. One prominent use case is the medical documentation co-pilot, which helps hospitals create and manage patient records more efficiently. This automation reduces time spent on documentation, allowing healthcare professionals to focus more on patient care.
- Another application is in revenue cycle management, where GenAI solutions streamline administrative processes, enhance accuracy, and lower operational costs. GenAI also plays a significant role in improving patient engagement and healthcare delivery through health bots. These chatbots facilitate consistent patient interactions and experience, automate routine inquiries, and support operational efficiency.
The report also cites a few examples of GenAI-based product releases:
■ Healthians launched WelliO, a personal health chatbot, to provide users with tailored health insights and recommendations. WelliO interacts with users to answer health-related questions and suggest relevant tests, making healthcare guidance more accessible. Healthians also rolled out an AI-powered IVR Bot, enhancing customer service by efficiently managing calls and resolving issues in real time. By harnessing AI in these areas, Healthians aims to transform the healthcare experience and make healthcare more accessible and user-centric.
■ Apollo 24|7, digital platform of Apollo Hospitals group, worked with Google Cloud to build a Clinical Intelligence Engine (CIE) using Google Cloud’s Vertex AI and GenAI models. Healthcare providers in India are leveraging GenAI for workforce efficiency, predictive analysis, clinical decision support and supply chain management GenAI is expected to boost productivity by 30% to 32% by 2030, largely driven by non-clinical operations, clinical operations in addition to sales, branding and community outreach Despite challenges like data sensitivity and legacy systems, The CIE service leverages data from Apollo Hospitals and LLMs from Google Cloud to create a proprietary solution in which all patient data is kept securely within the hospitals’ systems.
■ Cleveland Clinic leveraged AI-powered predictive analytics in cardiology to enhance early detection of cardiovascular diseases. This approach improved risk assessment accuracy, enabled earlier interventions, reduced healthcare costs, and successfully identified patients requiring advanced care.
■ Johns Hopkins Medicine implemented the AI-powered Targeted Real-Time Early Warning System (TREWS) to detect sepsis early by analyzing real-time patient data within its EHR system. This innovation improved early detection, reduced sepsis-related mortality, shortened hospital stays, and enhanced clinical workflows, setting a benchmark for AI in critical care. GenAI adoption in India’s healthcare sector is accelerating as providers modernize their systems, enhance diagnostics, and boost operational efficiency. It is being used to tackle critical challenges like increasing diagnostic precision, advancing telemedicine, and extending healthcare access in rural areas.
One of the case studies in the report needs a mention
AI-powered disease surveillance by Wadhwani AI: Wadhwani AI has partnered with the Government of India to deploy an AI-powered media scanning solution for event-based disease surveillance. As part of the Integrated Disease Surveillance Programme (IDSP), this system automates the detection of disease outbreaks by monitoring over 100,000 media outlets across multiple languages. The AI system continuously collects and analyzes data from news reports and digital media to identify potential outbreak signals. This information is integrated into the Integrated Health Information Platform (IHIP), enabling public health officials to take timely action. Alerts generated by the system are shared with Central Surveillance Units and State Units, eventually reaching District and Block Units to facilitate on-ground responses. This solution has significantly reduced the time required to detect potential outbreaks, strengthening India’s ability to effectively respond to public health emergencies.
In addition, finance process automation powered by GenAI enables predictive analytics for financial planning, minimizes errors, and supports better decision-making, resulting in greater operational efficiency and cost savings. Together, these tools are transforming healthcare operations, making them more effective and resource-efficient.
GenAI in the Indian Healthcare sector will have significant impact across hospital management operations, enablement of doctors and nurses (who comprise approximately 45% of hospital staff) and overall patient experience by enabling seamless data availability. Our study of 450-plus roles indicates a potential productivity impact of 30% to 32% by 2030 across the healthcare sector. For business leaders, embracing GenAI is not just an option; it is essential for driving innovation and achieving a significant productivity improvement.
Challenges and way ahead
Despite its transformative potential, GenAI adoption in healthcare faces several challenges. Data sensitivity and privacy concerns make it difficult to integrate GenAI solutions while ensuring compliance with strict regulations. Many healthcare providers rely on legacy systems like HIS and EMR, which are not designed for GenAI applications, necessitating infrastructure modernization for large-scale adoption.
Additionally, there is a shortage of skilled professionals trained to implement and manage these technologies, highlighting the need for investment in training and upskilling. Regulatory and ethical considerations further complicate adoption, requiring robust governance frameworks to ensure the responsible use of GenAI. Limited pilot adoption and scalability of GenAI solutions also hinder widespread implementation, emphasizing the importance of expanding successful pilots and demonstrating clear ROI.
To address these barriers, healthcare organizations must build strong datasets through systems like EMR and HIS for seamless data integration and invest in modern infrastructure and AI talent development. Establishing responsible AI practices and fostering collaboration with technology providers, start-ups, and regulators will be critical to scaling innovations.
