19 hours ago
AI and biomanufacturing: can India's policies match its ambitions?
India stands at a pivotal juncture in its quest to harness artificial intelligence (AI) for biotechnology innovation. On one hand, initiatives like the BioE3 Policy and the IndiaAI Mission reflect a bold vision to position the country as a global leader in AI-driven biomanufacturing and ethical AI development. On the other, fragmented regulations and lagging safeguards threaten to undermine this progress. As India races to capitalise on AI's transformative potential, a critical question emerges: can it balance ambition with accountability?
India's biomanufacturing sector is abuzz with possibilities. For decades, the country has been the world's go-to supplier for generic medicines and vaccines, a reputation it has built on scale, cost, and reliability. But now, as AI sweeps through the global life sciences industry, there's a sense that something much bigger is in the works. Many modern biomanufacturing facilities already have robots running precision tasks, biosensors streaming real-time data, and AI models quietly optimising everything from fermentation to packaging.
DNA of biomanufacturing
Biocon, one of India's largest biotechnology firms, is integrating AI to improve drug screening and its biologics manufacturing processes. By leveraging AI-based predictive analytics, Biocon will enhance the efficiency of fermentation and quality control, reducing production costs while maintaining global standards. Similarly, Bengaluru-based Strand Life Sciences uses AI in genomics and personalised medicine, helping accelerate drug discovery and clinical diagnostics. Their platforms use machine learning to analyse complex biological data, making it easier to identify drug targets and predict treatment responses. These efforts illustrate how AI is already reshaping biomanufacturing and healthcare delivery in India.
It's not just about swapping out people for machines. AI is transforming the very DNA of biomanufacturing. Imagine a production line where sensors feed thousands of data points every second into an AI system that can spot the faintest hint of trouble, like a temperature drift, a pH blip or a subtle change in cell growth. Before a human operator even notices, the AI predicts a deviation, tweaks the process, and keeps the batch on track. Digital twins, which are virtual replicas of entire manufacturing plants allow engineers to run simulations, test changes, and foresee problems without ever touching a real fermenter.
The result? Fewer failed batches, less waste, and products that consistently meet the gold standard for quality. For a country like India, where every rupee and every dose counts, these gains can be transformative.
Interesting and complicated
The Government of India has clearly recognised this potential. The BioE3 Policy, rolled out in 2024, is a playbook for the future. The policy lays out plans for state-of-the-art biomanufacturing hubs, biofoundries, and 'Bio-AI Hubs' that will bring together the best minds in science, engineering, and data. There's real money on the table too, with funding and grants designed to help startups and established players alike leap from the lab bench to the market shelf.
Equally important is the IndiaAI Mission, which is working alongside BioE3 to ensure India's AI revolution is both innovative and ethical. The Mission is as much about building technical capacity as about building trust. By supporting projects that focus on explainable and responsible AI — such as efforts to reduce algorithmic bias or frameworks for 'machine unlearning' — the Mission is helping set the standards for how AI should be developed and deployed in sensitive sectors like health and biotechnology.
But here's where things get interesting and complicated. While India's ambitions are sky-high, its regulatory framework is still catching its breath. The rules that govern how new drugs, biologics, and manufacturing processes come to market were written for a different era. Today's AI-driven systems don't always fit neatly into those boxes. For example, when an AI model is used to control a bioreactor or predict the yield of a vaccine batch, how do we know it's reliable? Who checks that the data it was trained on is representative of India's diverse conditions, or that it won't make a catastrophic error if something unexpected happens? These aren't just technical questions. They are matters of public trust and safety.
Risk-based, context-aware
Globally, the rules are changing. The European Union's AI Act, effective since August 2024, classifies AI tools into four risk tiers. High-risk applications like genetic editing face strict audits while the U.S. FDA's 2025 guidance mandates a seven-step framework for AI credibility. These models emphasise two things India lacks: context-specific risk evaluation and adaptive regulation. For instance, the FDA's 'Predetermined Change Control Plans' allow iterative AI updates that are critical for evolving cancer therapies without compromising safety. India needs this kind of risk-based, context-aware oversight as it moves from pilot projects to full-scale, AI-powered manufacturing.
Picture an Indian biotech startup that develops an AI platform to optimise enzyme production for the specialty chemicals industry. This sector is already worth $32 billion (Rs 2.74 lakh crore) and growing fast. If this AI is trained only on data from large, urban manufacturing sites, it might fail to account for the quirks of smaller plants in semi-urban or rural areas, like differences in water quality, ambient temperature or even local power fluctuations. Without clear standards for dataset diversity and model validation, the tool could recommend process tweaks that work beautifully in Bengaluru but flop in Baddi. The result: lost revenue, wasted resources, and a blow to India's reputation for quality. This is why the context of use and credibility assessment that are core pillars in the FDA's approach are so important. We need to be clear exactly what question the AI is answering, how it's being used, and how strict our oversight should be, depending on the risks involved.
Of course, biomanufacturing is only one piece of the puzzle. Imagine a future where India not only supplies 60% of the world's vaccines but also designs them using algorithms that predict viral mutations. A future where farmers in Bihar receive AI-generated advisories to combat pest outbreaks and patients in rural Tamil Nadu are diagnosed by tools trained on India's genetic diversity. This isn't science fiction — it's the promise of AI-driven biomanufacturing, a field where India is making bold strides. Yet beneath this optimism lies a critical question: can our policies keep up with science?
With great power comes…
The intersections are multiplying. In drug discovery, AI platforms can screen millions of compounds in silico, slashing the time and cost needed to find new treatments. Molecular design tools are helping researchers fine-tune drug candidates for maximum efficacy and minimal side effects. Clinical trials that were once notorious for delays and inefficiencies are being streamlined by AI systems that optimise patient recruitment and trial design, making studies faster and more representative. Even the supply chain is getting an upgrade: AI-powered predictive maintenance keeps manufacturing lines humming, while demand forecasting ensures that medicines reach the right place at the right time, reducing shortages and waste.
Another unique application of AI is Wipro's work in developing AI-powered solutions for pharmaceutical companies to streamline drug discovery. By combining machine learning algorithms with computational biology, Wipro has helped reduce the time required to identify viable drug candidates. Similarly, Tata Consultancy Services is leveraging AI in its 'Advanced Drug Development' platform, which uses machine learning to fine-tune clinical trials and predict treatment outcomes. These applications demonstrate how AI is not just confined to manufacturing but is transforming the entire healthcare value chain, from research to patient care. These innovations also indicate India's potential to lead the way in AI-powered healthcare solutions.
But with great power comes great responsibility and a host of new challenges. Data governance is a big one. AI models are only as good as the data they're trained on, and in a country as diverse as India, that's no small feat. The Digital Personal Data Protection Act 2023 is a start, but it doesn't address the specific needs of AI in biomanufacturing, like ensuring that datasets are clean, diverse, and free from hidden biases. Intellectual property is another thorny issue. As AI begins to play a bigger role in inventing new molecules and processes, questions about inventorship, data ownership, and licensing are becoming more urgent. Without clear, harmonised policies, the risk of stifling innovation or ending up in costly legal battles persists.
Create, not just copy
So, what's the way forward? First, India needs to move quickly towards a risk-based, adaptive regulatory framework. This means defining the context of use for every AI tool, setting clear standards for data quality and model validation, and ensuring ongoing oversight as systems evolve.
Second, India needs to invest in infrastructure and talent — and not just in the metropolitan cities but across the country.
Third, it needs to foster a culture of collaboration, bringing together regulators, industry, academia, and international partners to share best practices and solve problems together.
If the country gets this right, the rewards are enormous. India's legacy in generic drug manufacturing is secure but the future belongs to those who can harness the power of AI to create, not just copy. With the right policies, the right people, and the right priorities, there's no reason why the next great leap in biomanufacturing shouldn't come from India. The world is watching and the time to act is now.
Deepakshi Kasat is a scientist with GlaxoSmithKline in California.
