What were the key highlights for TCS from 2025?
If I had to summarize 2025 in the same way people talk about real estate as “location, location, location,” then for 2025 it was “AI, AI, AI.” That applied to TCS and to the broader industry, and not just in software but across virtually every sector. I personally went through extensive training and hands-on work in AI. More broadly, 2025 marked a shift from experimentation to institutionalized AI. After COVID, many companies were focused on workforce optimization, and in 2025 we clearly moved beyond that phase.
Organizations began redirecting savings toward transformation, using AI to optimize operations and reinvesting that value into change initiatives. Over the year, we moved from some initial pressure to seeing strong green shoots, and after a relatively flat period we returned to growth. Much of the new work we’re seeing is centered on AI and data, which really defined the year from both a learning and growth perspective.
AI is nothing without a strong data backbone, and what we saw in 2025 was that for every dollar spent on AI, companies were spending roughly two dollars on data. AI and data together were the defining story of the year. Alongside that, TCS continued work across a broad portfolio, including life sciences and healthcare, with significant efforts in genomics research and large-scale system modernization powered by AI. At the beginning of the year, many companies were asking how to institutionalize AI and started building platforms. By the end of the year, many had completed those platforms and began moving toward agentic AI architectures, introducing more autonomy into their systems. The year effectively started with AI experimentation and ended with agentic AI, making it a highly productive year in terms of both capability building and growth.
How has TCS’ strategy evolved to match these developments?
From a TCS perspective, our AI strategy is now very clearly defined around a five-pillar framework. This begins with an AI-first mindset, where AI has the first right of refusal in everything we do. It extends to building an AI-ready culture, ensuring our people are prepared, making our customers AI-ready, developing platforms, and actively participating in the broader AI ecosystem.
We are investing across the entire AI stack, from infrastructure to intelligence. TCS has announced plans to build a one-gigawatt data center in India, with total investments of around six to seven billion dollars alongside partners. Our ambition is to operate across all layers of AI, from data centers and compute through models, data, agents, and ultimately conversational and physical AI, gradually moving through increasing levels of autonomy.
How does that AI strategy apply specifically to biopharma?
We apply the same framework across biopharma, starting with research. Rather than traditional drug discovery, we are increasingly focused on AI-driven drug design. In one case, we helped a customer narrow down 1,300 molecules to 12 candidates for pancreatic cancer using a fully AI-based approach. Beyond discovery, we are transforming the entire drug development value chain, including data management, drug safety, and regulatory processes.
In manufacturing, we are using digital twins to enable more autonomous and sustainable biopharma production. This includes process deviation management, output optimization, and carbon reduction. We also support rapid reconfiguration of biologics manufacturing, something many companies realized was critical during COVID. Using digital twins, manufacturers can simulate changes virtually before implementing them physically, supported by our TCS InTwin models and TCS PREMAP(R) platforms.
We are bringing these capabilities into biologics, manufacturing, automation and sustainability initiatives, as well as into commercial areas such as medical-legal content, document generation, and healthcare professional and patient engagement. In addition, AI is increasingly embedded in back-office functions including finance, HR, and supply chain, with a strong focus on agentic automation and end-to-end visibility.
By 2025, we reached an institutional scale of AI adoption, applying it across both core business value chains and internal operations. This represents a fundamental shift from isolated use cases to enterprise-wide transformation.
What is the growing importance of connected medical devices from your perspective?
Connected medical devices are fundamentally a platform that can be applied across monitoring, diagnostics, treatment, prevention, and wellness. The impact depends on how they are used. For example, digital twins of organs such as the heart can support wellness and prevention, while connected devices can also enable advanced treatments using pacemakers and other sophisticated instruments.
One of the biggest impacts will be in aging societies, where home healthcare is becoming increasingly important. Connected devices enable monitoring everything from vital signs and mobility to medication adherence. The real breakthrough will come when these devices are integrated around an individual, with different approaches tailored to different demographics, from wellness-focused solutions for younger populations to intensive monitoring for older patients.
How do you see connected devices evolving alongside AI?
Connected devices can address episodic issues such as cardiac events, as well as long-term monitoring needs. Looking ahead, connectivity may even extend directly to the brain, both for signal collection and stimulation in certain diseases. The scope ranges from everyday digital wearables to highly sophisticated medical devices.
The real power emerges when device data is connected with longitudinal patient data and combined with trained AI models. Together, these elements can autonomously interpret signals and provide precise guidance to healthcare professionals, creating a more effective and cost-efficient healthcare ecosystem.
How is TCS contributing to improvements in the U.S. public health system?
We do not work directly with public health agencies, but we work extensively with pharma companies, payers, and providers. Drug development costs can reach nearly a billion dollars, so even small reductions in time or cost have a significant impact on drug pricing. By transforming the entire drug development value chain, from discovery to regulatory processes, we can meaningfully reduce costs.
We are also working with insurance companies to reduce friction in areas like prior authorization. Using AI and data, we aim to increase approval rates while minimizing delays, which significantly improves patient experience. Additionally, by analyzing patient cohort data, we help insurers intervene earlier in chronic conditions, reducing costly acute care events and ultimately lowering the cost of care.
What are your priorities for TCS in healthcare over the next two years?
Our primary focus is scaling agentic AI across healthcare value chains. What has worked in controlled environments now needs to be industrialized and deployed across multiple trials and organizations. Scaling these capabilities will be a major focus over the next one to two years.
We are also focused on bringing AI into the physical world, including manufacturing, surgical robotics, and digital surgery. In parallel, we are investing in areas such as gene therapy, with the goal of dramatically reducing the cost of next-generation treatments. We are also collaborating with institutions like Carnegie Mellon and Cornell Tech to train AI models for applications such as tumor detection and personalized cancer treatment, including successful work that has significantly improved childhood leukemia survival rates in India. Our goal is to deliver advanced, effective care at a much lower cost, particularly in regions with limited resources.
