At the 26th Annual CIO & Leader Conference, I spoke about the unique challenge of integrating Artificial Intelligence into the pharmaceutical industry.
Artificial Intelligence is reshaping the pharmaceutical industry, but in a tightly regulated sector, innovation must always be balanced with compliance and patient safety. Every innovation must pass through the lens of the FDA and global regulatory compliance.
At Aurobindo Pharma, we are one of the world’s leading generic pharmaceutical companies, operating in over 150 countries, and a major provider of generic medicines. Unlike innovative drug makers, we focus on affordable generics. This makes efficiency, compliance, and consistency central to our AI journey. I also highlighted our IT backbone, which is built entirely on Oracle systems and has data centers in Hyderabad and New Jersey. While we use cloud tools for value-added solutions, our core systems remain on-premises to protect intellectual property.
Drug Discovery: The Slow Path Speeds Up
Drug discovery typically takes 10 to 15 years, and early AI efforts have not significantly shortened this timeframe, with some projects still taking more than a decade. With large language models, however, timelines are beginning to shrink.
That said, today’s AI is still narrow and pattern-based, dependent on training data and GPU-heavy models. Any deviation from established patterns can be risky in the pharmaceutical industry. Human scientists remain irreplaceable.
Clinical Trials: From Weeks to Days
Clinical trials remain the biggest bottleneck. Traditionally, modeling how a drug interacts with the body could take 29 days. With AI, this is expected to reduce to just a few days, saving significant scientific effort while maintaining compliance.
Looking ahead, I see AI-powered simulated clinical studies as a significant opportunity. These simulations could further reduce trial timelines while adhering to regulatory standards.
Manufacturing and Supply Chain: Lessons from COVID
The pandemic exposed severe supply chain vulnerabilities. To address these challenges, we utilized Meta’s Prophet model, achieving 90% forecast accuracy even in dynamic, tender-based markets.
On the manufacturing side, AI now helps define optimal parameters for a “golden batch,” such as blending speed and temperature, that ensure consistent quality and safety. In the future, AI could directly control manufacturing equipment to ensure these standards are maintained.
Guardrails: Ethics and Security
Hallucinations and bias in AI can have serious consequences for patient health. That is why I have called for bias audits and stress audits in addition to FDA inspections.
I also see blockchain as a tool for tamper-proof quality records, and quantum computing as both an opportunity and a risk. It could accelerate discovery but also break current encryption standards, creating cybersecurity challenges.
The Call to Responsibility
The future of the pharmaceutical industry lies at the intersection of AI, blockchain, and quantum computing. However, our industry will continue to prioritize on-premise systems to safeguard sensitive data.
For the pharmaceutical industry, AI is not just about faster processes; it is about ensuring trust, accountability, and patient safety.