AI and the Future of the Life Sciences Industry

AI and the Future of the Life Sciences Industry

The AI in the life sciences market is estimated at US$3.61billionin2025,projectedtogrowtoaroundUS$11.11 billion by 2030 (≈ 25.2 per cent compound annual growth rate).76 In the coming years, there would be widespread adoption of AI and GenAI in drug discovery. The next decade will mark a shift from AI-assisted to AI-driven life sciences innovation, fundamentally redefining how science is conducted and how therapies reach patients. There would be convergence of AI with other emerging technologies such as IoT, blockchain robotics and edge/IoT in diagnostics. There would be close collaboration among AI companies and pharma biotech companies. AI-designed drugs are likely to become more common, and AI-supported drug discovery processes would support expeditious development of drugs with lowering of costs. Instead of the dependence on the traditional lab-based experimentation, drug discovery would increasingly move towards AI-based target identification and molecular design. This would also help in developing drugs for addressing rare or neglected diseases.

Robotics and AI and ‘lab-in-the-loop’ systems would be implemented to automate lab workflows,77 and virtual screening of a vast range of compounds would also improve the time and cost of drug development. Scientists would not only use AI as a tool but also develop a close collaboration with AI. AI would help scientists by taking on tasks such as sifting through research literature and resolving some of the research bottlenecks and free up the scientists to do more ‘thinking’ and ‘original’ work. Embedded AI governance frameworks would ensure traceability, auditability and fairness across product and clinical outcomes. AI models driven by systems biology will help in mapping disease pathways and simulate interventions that were previously invisible. Therefore, personalised and precision medicine would become mainstream, facilitating customised treatment plans. Risk prediction models for chronic diseases would become more accurate. Population-specific therapeutics would become more feasible. Clinical trials would become smarter, decentralised and more cost-effective with AI playing a significant role in optimising trial design, identifying patient cohorts across diverse populations and predictive analytics ensuring minimal drop-outs. Digital twins would simulate patient samples, and AI-enabled trials could facilitate continuous trials with real-time data insights. The combination of digital twins, AI and IoT will make pharma plants fully autonomous, and with better demand prediction, batch production would be adjusted in real time and supported by real-time quality checks. Biomanufacturing units, like smart factories, are using AI sensors for continuous monitoring and self-correction. While the potential of AI adoption and the efforts initiated by several corporates are encouraging, ethics and data privacy issues continue to be daunting. These matters should be high on the agenda to be addressed by the combined efforts of the government, industry and key technology players.

India’s pharma R&D employment will shift 20–25 per cent towards data-driven and automation-linked roles.78 Bioinformatics, genomics and digital trial operations are expected to create 50,000–70,000 new high-skill jobs by 2030.79 A new workforce model will emerge at the intersection of biology, data science, AI, ethics and engineering. India has an exciting opportunity to build the new-age bio-AI talent that would drive biotech innovation. To make this a reality, new formats of partnerships are necessary between pharma, AI start-ups, cloud providers and research institutions. Let us examine the changes in the roles as a result of the transition in the life sciences industry in Tables 6.4 and 6.5.

Table 6.4 Emerging Roles in the Life Sciences Industry

Source: Curated with inputs from ChatGPT