India is at a critical point in its journey to use artificial intelligence (AI) for transforming biotechnology and healthcare. With major policies like the BioE3 Policy and the IndiaAI Mission, the country has shown a strong vision to become a global leader in AI-based biomanufacturing. This means using AI not just to speed up production, but to completely change how biological products like vaccines, drugs, and diagnostics are designed, developed, and delivered.

• However, alongside this ambition, India faces a significant challenge. Its regulatory and safety frameworks have not kept pace with the rapid growth of AI in the biotechnology sector. If not addressed quickly, these gaps may slow down progress and reduce public trust in AI-driven innovations.

Biomanufacturing in India: A New Chapter:

India has long been known as the “pharmacy of the world.” It supplies over 60% of global vaccine demand and is a major exporter of affordable generic medicines. This success is built on large-scale production, low costs, and consistent quality. But the global biotechnology landscape is now changing. Countries are moving from bulk manufacturing to more sophisticated, AI-driven systems that improve efficiency, accuracy, and adaptability.

In modern biomanufacturing facilities, AI is already being used in many ways:

• Robots carry out precise, repetitive tasks without human error.
• Sensors collect thousands of data points every second.
• AI algorithms analyse this data in real-time to optimise processes.

AI helps ensure that the production environment stays stable. If something starts to go wrong — for example, a shift in pH or a rise in temperature — the AI system can predict the issue and adjust the system automatically. This results in fewer errors, less waste, and better-quality products.

Indian Companies Leading the Change:

Several Indian companies are already using AI to improve their biotechnology operations:

• Biocon, a major biopharmaceutical company, uses AI in its biologics manufacturing. AI improves drug screening and fermentation processes, reducing costs while maintaining quality.
• Strand Life Sciences, based in Bengaluru, uses AI in genomics and personalised medicine. Their AI models can analyse complex genetic data to find better drug targets and predict patient responses.
• Wipro and Tata Consultancy Services (TCS) are helping pharmaceutical companies use AI for drug discovery, clinical trials, and treatment predictions.

These examples show that India is not just following global trends but also developing its own capabilities in AI for healthcare and biomanufacturing.

Government Initiatives: BioE3 and IndiaAI Mission:

Recognising the potential of AI in biotechnology, the Government of India has launched two key initiatives:

1.       BioE3 Policy (2024)

This policy outlines plans to create:

o    Advanced biomanufacturing hubs

o    Biofoundries for research and product development

o    Bio-AI Hubs that bring together experts in science, technology, and data analytics

These hubs will support startups, researchers, and large companies by providing infrastructure, funding, and policy support to bring ideas from the lab to the market.

2.      IndiaAI Mission

This mission supports the growth of AI in a responsible and ethical way. It promotes:

o    Explainable AI, which makes decision-making transparent

o    Efforts to reduce algorithmic bias

o    Development of frameworks for safe and trustworthy AI systems

Together, these programmes aim to make India a global leader in AI-powered biotechnology — not just in terms of scale, but also in safety, ethics, and innovation.

The Regulatory Challenge:

Despite these efforts, India’s regulatory framework is still designed for older technologies. The rules for approving new drugs, biologics, and manufacturing systems were made long before AI became part of the process. This creates several problems:

• There are no clear guidelines on how to test and approve AI models used in manufacturing.
• It is unclear who is responsible if an AI-driven system makes a mistake.
• There are no standards for ensuring that the training data used by AI is representative of India’s diverse environments.

For example, an AI system trained on data from a high-tech facility in Bengaluru might not perform well in a small-town factory in Baddi or Indore. Factors like local climate, water quality, and electricity supply can affect production, and if the AI is not trained on diverse data, it might fail to adjust properly.

Global Models India Can Learn From:

Other countries are already creating smarter, risk-based regulatory systems for AI:

• The European Union’s AI Act (2024) classifies AI tools by risk level. High-risk applications like AI in gene editing require strict audits and testing.
• The U.S. Food and Drug Administration (FDA) introduced a seven-step framework for evaluating AI in healthcare. It includes tools like Predetermined Change Control Plans, which allow AI systems to evolve safely over time while maintaining strict oversight.

India does not yet have such systems. Without context-aware and flexible regulation, AI innovations could stall or lead to failures, especially in high-stakes sectors like health and biotechnology.

Broader Impact of AI in Biotech and Healthcare:

AI’s role goes far beyond manufacturing. It is also transforming:

• Drug discovery: AI can screen millions of compounds virtually, saving time and money.
• Molecular design: AI helps develop drugs with fewer side effects and better efficacy.
• Clinical trials: AI tools make trials faster and more accurate by selecting better participants.
• Healthcare delivery: AI improves patient diagnosis, predicts disease risk, and enables precision medicine.

In India, AI-based systems could help:

• Predict disease outbreaks in remote areas
• Create personalised treatment plans based on genetic data
• Reduce supply chain problems by forecasting medicine demand

These applications show how AI can strengthen India’s entire healthcare ecosystem.

Key Challenges That Must Be Solved:

Despite the promise, several challenges need urgent attention:

1.       Data Governance

AI models are only as good as the data they are trained on. India needs rules to ensure datasets are:

o    Clean and accurate

o    Free from bias

o    Inclusive of India’s geographic and population diversity

2.      Intellectual Property (IP)

When AI tools help invent new drugs or processes, questions about who owns the invention must be clearly defined. Without clear IP laws for AI, legal disputes could increase.

3.      Skilled Workforce and Infrastructure

India needs to build talent in AI, data science, and biotechnology — not just in metros, but also in smaller towns and Tier-2 cities. Infrastructure investments must also support this distributed growth.

The Way Forward

To move ahead, India must take three major steps:

• Build an adaptive, risk-based regulatory system that includes context-of-use, model validation, and continuous oversight.
• Invest in infrastructure and human capital across the country, ensuring regional inclusion.
• Encourage collaboration among government bodies, industry leaders, academic institutions, and international organisations to share knowledge and set global benchmarks

Conclusion

India’s strength in affordable drug manufacturing is already well established. Now, with the rise of AI, the country has the chance to lead in innovation, not just in production. If it can build smart policies, strong institutions, and skilled people, India can become a global powerhouse in AI-driven biomanufacturing. The future of healthcare and biotechnology may well be shaped in India — but only if science and policy move forward together.