As companies across sectors push to scale artificial intelligence, healthcare is taking a more cautious route. Unlike retail or banking, where AI is largely used to improve customer experience or automate workflows, healthcare operates in a far more sensitive environment where every decision can directly affect patient outcomes. That difference is shaping how healthcare enterprises approach AI adoption today.
In a recent interaction with the Financial Express B2B, Prashanti Bodugum, Head of Evernorth Health Services India, spoke about why healthcare organizations are prioritizing trust, compliance, and data security over rapid AI deployment, and how India’s GCC ecosystem is evolving alongside this shift.
The conversation comes at a time when global investment in healthcare AI is growing rapidly. Industry estimates suggest the AI healthcare market could cross USD 500 billion by 2033, driven by growing demand for automation, predictive analytics, and operational efficiency across healthcare systems.
Why Healthcare Cannot Move at the Same Pace as Other Industries
For years, sectors like financial services and retail have led technology adoption because the risks of experimentation are relatively lower. Healthcare, however, does not have that flexibility.
According to Bodugum, healthcare organizations cannot simply move AI projects from pilot to production without carefully evaluating data quality, governance, security, and compliance requirements. The industry still relies heavily on “human-in-the-loop” models where AI assists decision-making rather than replacing it entirely.
The reason is straightforward: healthcare decisions are tied to human lives.
This has made healthcare enterprises far more measured in how they test and deploy AI systems. Many organizations are also choosing to build AI models on proprietary and in-house datasets instead of exposing sensitive patient data to external platforms. The focus is on creating secure, compliant, and reliable AI environments before scaling adoption.
AI Is Finding Space in Operational Workflows
Even with a cautious approach, AI adoption is beginning to create a measurable impact in healthcare operations.
Administrative areas such as workflow management and prior authorization are emerging as some of the earliest use cases. These are functions where AI can reduce repetitive tasks, improve efficiency, and shorten processing time without significantly increasing clinical risk.
Bodugum noted that organizations are already seeing operational improvements in some AI-enabled workflows. However, functions such as claims processing still require careful oversight because they involve complex regulatory and patient-related considerations.
This reflects a broader reality within healthcare AI adoption today: enterprises are more comfortable using AI to reduce operational friction than allowing it to make independent decisions in high-risk environments.
India’s GCCs Are Entering a New Phase
The discussion also highlighted how India’s Global Capability Centers are gradually shifting beyond their traditional execution-focused role.
For years, GCCs were primarily associated with scale, operational delivery, and cost efficiency. But AI is beginning to change those expectations.
As routine technology tasks become increasingly automated, companies are placing greater importance on domain expertise alongside technical skills. In healthcare especially, understanding claims systems, pharmacy operations, compliance frameworks, and patient journeys is becoming just as important as writing code.
This is pushing GCCs toward a more strategic role within enterprises.
Instead of functioning only as delivery centers, GCCs are increasingly expected to contribute to business decisions, innovation priorities, and digital transformation initiatives. The emphasis is moving from pure execution to solving industry-specific problems with technology.
Personalization Remains a Larger Long-Term Opportunity
One area where healthcare still significantly trails industries like banking and retail is personalization.
Consumers today receive real-time financial insights, shopping recommendations, and tailored digital experiences almost instantly. Healthcare, on the other hand, remains fragmented, reactive, and dependent on multiple layers of manual intervention.
AI has the potential to change that gradually.
More personalized healthcare experiences, proactive patient engagement, and better caregiver support are emerging as long-term opportunities for the industry. For example, AI systems could eventually help caregivers monitor medication adherence or alert families when elderly patients miss prescription refills.
But achieving that level of personalization will require healthcare organizations to carefully balance innovation with privacy, regulation, and ethical safeguards.
Governance Is Becoming the Core of Healthcare AI
One of the clearest takeaways from the interaction was that governance is no longer being treated as a barrier to innovation. In healthcare, it is becoming the foundation of AI adoption itself.
Organizations are increasingly relying on controlled testing environments, secure data systems, and compliance-first frameworks before scaling AI initiatives. The industry’s slower pace may appear conservative compared to other sectors, but it also reflects the complexity and responsibility attached to healthcare decision-making.
For India’s GCC ecosystem, this creates a different kind of opportunity — one that is less about scale alone and more about building domain-led, trustworthy, and resilient AI systems for global enterprises.
