By Ganesh Nathella
Across healthcare systems globally, the conversation is shifting from digitisation to intelligence. AI is no longer confined to experimentation or isolated pilot programmes; it is increasingly becoming the operating backbone of modern healthcare delivery. Globally, healthcare organisations are moving beyond digitisation toward AI-enabled transformation, where data is continuously translated into action.
So far, AI’s early value creation is concentrated in three areas.
Administrative efficiency: AI-enabled auto-coding of claims and automated patient registration reduce routine manual workload, allowing staff to focus more on patient-facing care rather than paperwork.
Operational optimisation: AI-driven OT scheduling, bed allocation and predictive inventory management are easing bottlenecks, shortening wait times and reducing wastage of consumables and medicines.
Clinical augmentation: AI-assisted screening for X-rays, TB tests, and retinal scans help address specialist shortages by prioritising high-risk cases and enabling earlier detection, while serving as embedded decision support for clinicians.
Collectively, these interventions demonstrate that the value of AI in healthcare lies in augmenting human capability and not by displacing it. For example, an AI-enabled radiology accelerator integrates multimodal imaging models directly into radiologists’ workflows. The system analyses imaging data, prioritises high-risk cases and surfaces abnormal findings earlier in the diagnostic process, helping clinicians manage growing scan volumes while improving early cancer detection outcomes.
From digital front doors to true patient-centricity
Patient-centricity is evolving well beyond appointment portals and chatbots. AI is enabling continuous, personalized engagement across the entire care journey. Post-discharge instructions can now be tailored in local languages to a patient’s medical condition and lifestyle. Follow-up reminders adapt dynamically based on risk profiles. For chronic illnesses such as diabetes or hypertension, AI systems flag deterioration between consultations, prompting proactive outreach from care teams.
Equally important is the ability to simplify complex medical information. AI systems can translate diagnostic reports into patient-friendly language, improving health literacy and reducing unnecessary hospital visits. In remote or underserved regions, AI-enabled care tools can guide patients to the appropriate level of care, preventing avoidable travel and system congestion.
An example of this shift can be seen in how a diabetic research society is integrating AI into its electronic medical record (EMR) systems to support clinical decision-making, patient education and research on local disease patterns. These systems can capture patient health data between visits, giving clinicians and patients a more continuous view of disease management.
Real-time data: The shift from reactive to predictive
The shift to predictive care depends on integrating data from monitors, labs, wearables and electronic health records. When analysed in real time, AI can detect early warning signals such as sepsis risk or chronic disease deterioration, before symptoms become visible, enabling earlier intervention and better outcomes.
This potential depends on strong data foundations, well-prepared datasets and interoperability across systems. It further requires clear governance to ensure accountability, privacy compliance, and leadership alignment around AI-driven transformation.
AI as a workforce multiplier
Healthcare’s workforce shortages are structural. Automated documentation and virtual assistants are reducing administrative burden, while decision-support tools extend specialist expertise at the point of care. When co-designed with clinicians, these systems act as trusted collaborators, augmenting judgment without adding digital fatigue.
A BCG’s 2026 report highlights that the next frontier lies in AI agents capable of orchestrating multi-step workflows, coordinating scheduling, documentation, assessment and patient communication autonomously under human supervision. Such agents move beyond task automation toward end-to-end process management, signaling a shift from tool-based AI to system-level intelligence.
Digitised vs. AI-enabled organisations
The real inflection point in healthcare will not be digital adoption but intelligence adoption.
Organisations that place AI at the centre of their operating model move beyond automation to anticipation, predicting risk, personalising engagement at scale and continuously improving outcomes through data-driven insight.
As AI agents and predictive models mature, the gap between healthcare organisations will widen. Those embedding AI across administrative, operational and clinical domains will achieve scalable, data-led care, while others risk remaining digitised but not transformed.
Ultimately, the future of healthcare will not be defined by who adopted AI first, but by who integrated intelligence most effectively into the fabric of care delivery.
The writer is EVP & GM, healthcare and lifesciences business, Persistent Systems
Disclaimer: The views expressed are the author’s own and do not reflect the official policy or position of Financial Express.