India’s pharmaceutical sector has long been positioned as the world’s pharmacy. However, as technological integration and regulatory scrutiny intensify in advanced markets, success will depend less on scale or cost efficiency alone, and increasingly on the ability to demonstrate quality and credible, data-driven decision making.
In India, Artificial intelligence (AI) is increasingly being integrated across the pharmaceutical operations. AI delivers its fastest returns when used as a speed and productivity lever in well-bounded processes. However, its long-term promise lies in scientific problem-solving beyond human cognitive limits. When AI’s speed is confused with its scientific capability, adoption stalls.
Why the AI for Speed vs the AI for Science Distinction Matters
When organisations conflate AI for Speed with AI for Science, it results in disappointment, misallocation of investment, and erosion of trust. Thus, this distinction is crucial. When used for speed, AI accelerates routine scientific and analytical tasks such as literature review, route scouting, data hygiene, and documentation, delivering immediate gains in productivity.
AI for science, on the other hand, can solve problems beyond human cognition, for instance, exploring astronomically large chemical and biological search spaces, and detecting subtle, probabilistic patterns in complex disease areas. While useful in several other spaces, including molecular search, enzyme-substrate compatibility, novel synthesis pathways, requires patience, deep scientific oversight, and long investment horizons. AI can raise productivity considerably but substituting scientific judgement with AI for speed tools can lead to low quality outputs.
A recent discussion on AI in the Pharmaceutical Value Chain, jointly organised by the Munjal Institute for Global Manufacturing and the Max Institute of Healthcare Management at the Indian School of Business and attended by senior industry leaders, pharma experts, and researchers, also emphasised that the constraints lie not in the algorithms themselves but in their correct use, along with data quality, governance, organisational design, and managerial conviction.
What Already Works and What Lies Ahead
In practice, AI’s most reliable gains are downstream, in predictive maintenance, deviation analysis, yield optimisation, and regulatory intelligence, where it delivers clear, measurable results. This value is driven not by better algorithms but by better information flow. Across organisations, data, and not model sophistication, is the primary constraint. The true gap lies between generic data sets that do not reflect the realities of proprietary process, and experimental datasets that are largely unstructured, inconsistently captured and siloed away in handwritten notes, scanned pdfs or fragmented systems. Under stringent regulatory scrutiny, these gaps become binding constraints.
The strategic advantage, therefore, lies in systematically capturing the ‘low hanging’ operational data. Firms that invest in cleaning, structuring and managing their internal data stand to build a compounding advantage and credibility over time. These firms position themselves on the frontlines of compliance and, therefore, become stronger contenders in international markets. This shifts the conversation from using advanced tools to the bigger questions of data governance and accountability.
Currently, in the absence of clear decision-making frameworks, business leaders remain accountable but hesitate to trust AI outputs that are difficult to explain, hard to rely on and not traceable to high quality data. This prevents data insights from turning into real actions. Effective AI adoption requires a critical translation layer, bringing together domain experts, data scientists, and translators who can convert operational problems into the right analytical questions.
The strategic implications for Indian pharma are clear. AI must be framed explicitly as a speed lever or a science lever, never ambiguously both. Data strategy must precede AI strategy as compliance expectations centre on traceability, explainability, and audit readiness. For most firms, that means a key focus on productivity, documentation, and quality, areas where value is measurable and risk is contained. Adoption accelerates when AI simplifies work before it attempts to transform outcomes, providing a steadier, more reliable solution rather than a quick high-tech fix.
Going forward, the real shift must be from pilots to decision-grade systems. This requires sustained capability-building for middle management, embedding AI into workflows where decisions are made, and treating data stewardship as a managerial responsibility, not a technical afterthought. This moment is as much about data governance and organisational readiness as it is about technology. As India’s trade and regulatory engagement broadens its horizons, these capabilities will increasingly determine which firms can translate market access into lasting credibility.
Authors’ Bios’:
Dr. Yugal Nauhria
Associate Director, MIGM
Dr. Yugal Nauhria is the Associate Director at the Munjal Institute for Global Manufacturing and the Punj Lloyd Institute of Infrastructure Management at the Indian School of Business (ISB), Mohali. He holds a PhD from IIT Delhi and a postgraduate degree in Operations and Human Resource Management from IMT Ghaziabad.
With over two decades of professional experience, Dr. Nauhria brings deep expertise in management consulting, operational leadership, and strategy execution across a wide range of manufacturing and service industries. His consulting career spans leading firms such as McKinsey & Company, KPMG, and his own independent advisory practice. Beyond consulting, he has held senior leadership roles at AB InBev, Ola Cabs, and Flipkart, where he contributed to large-scale operational transformations and strategic initiatives.
Amanjeet Singh
Senior Manager (Outreach), MIHM
Amanjeet Singh is Senior Manager (Outreach) at the Max Institute of Healthcare Management, Indian School of Business (ISB). He works on disseminating research, policy insights, and institutional work to wider public and policy audiences. Prior to this, he worked at O.P Jindal Global University, contributing to institutional storytelling and public engagement. He has also been associated with Hindustan Times, The Wire, and The Quint.
Navsangeet Saini
Writer, MIHM
Navsangeet Saini is a communication professional with over 13 years of experience across academia, media and communication research, and writing. She holds a Ph.D. in Mass Communication and is interested in how storytelling shapes communities and societies. At the Max Institute of Healthcare Management, Indian School of Business (MIHM‑ISB), she applies this perspective to healthcare communication, helping make research accessible so it can better inform and engage the audiences it reaches.