Artificial Intelligence (AI) is reshaping industries worldwide, and life sciences is no exception. From drug discovery to clinical decision support, AI has the potential to transform the development, testing, and delivery of therapies. Yet, while the promise is immense, adoption remains cautious. The road ahead requires striking a balance between innovation and responsible governance to ensure that AI delivers on its potential without compromising safety, quality, or trust.
AI in life sciences holds tremendous promise. With the ability to process vast datasets, identify patterns invisible to humans, and support predictive decision-making, AI is already finding applications across the value chain:
- Drug Discovery and Development – accelerating identification of new compounds and predicting therapeutic potential.
- Clinical Trials – optimising patient recruitment and monitoring trial performance in real-time.
- Manufacturing and Quality – improving process control, predictive maintenance, and anomaly detection.
- Clinical Decision Support – assisting healthcare professionals in diagnosing diseases and personalising treatment.
These examples represent only the beginning. As models and computational power evolve, AI applications will continue to expand, offering more precise, efficient, and cost-effective solutions.
Despite its promise, AI adoption in life sciences is still limited. Across the industry, many initiatives remain at the pilot stage, with only a fraction scaled into production environments. This “confidence gap” highlights the difference between enthusiasm for AI’s potential and the readiness to deploy it in mission-critical, regulated processes.
Even where AI is deployed, organisations face challenges in ensuring reliability, consistency, and compliance.
Trust remains the central barrier.
The hesitation stems from AI’s unique characteristics compared to traditional software systems.
- A Different Paradigm: Traditional software is designed top-down, with requirements defined upfront and changes tightly controlled. AI, by contrast, evolves from data and models—a bottom-up approach that introduces variability and uncertainty. Integrating these two paradigms is complex and difficult to manage under conventional frameworks.
- Assurance and Validation Gaps: Current assurance and validation practices, designed for deterministic systems, are ill-suited to AI’s adaptive nature. Regulators recognise this and are updating frameworks such as PIC/S Annex 11 and PIC/S now introducing Annex 22. Existing Computer Software Assurance (CSA) principles still apply however they must be adapted and expanded for AI.
- Unclear Risk Landscape:
- The full spectrum of risks AI introduces is not yet understood.
- Requirements and risk measures remain poorly defined.
- Current approaches to managing these risks are fragmented, manual, and lack standardisation.
To unlock AI’s potential, life sciences companies must build a foundation of trust through structured risk management and governance. Key elements include:
- Systematic Risk Identification – mapping the full spectrum of risks for each AI use case and defining proportional risk controls.
- Risk-Based Approach – applying safeguards that align with actual exposure, avoiding both under-protection and over-engineering.
- Integrated Governance – combining governance structures with technical measures and process integration. Governance alone is insufficient without supporting execution.
- Human Oversight – embedding oversight not merely as accountability but as an active control mechanism. This requires vigilance, as people tend to trust AI outputs once systems appear to perform well. Maintaining critical human judgment is essential.
To help clients adopt AI solutions with greater confidence, SeerPharma is proud to announce a partnership with AIQURIS—a proven AI risk and quality management solution tailored to the life sciences sector.
To launch this collaboration, we are hosting a webinar on Wednesday, 24 September 2025.
- Speakers:
- Ian Lucas – Director at SeerPharma, who will share his experience assessing AI applications and insights from contributing to the latest ISPE GAMP Guide on AI.
- Dr. Andreas Hauser – Founder & CEO of AIQURIS, who will demonstrate how AIQURIS helps organisations systematically map and manage AI risks, including a case study on risk management for a Diagnostics & Clinical Decision Support system.
To learn more about AIQURIS, do visit: https://www.seerpharma.com/tools/aiquris
At SeerPharma, we are committed to sourcing solutions that give our clients confidence in compliance and to advance Quality and GMP best practices across the Asia-Pacific region. Our partnership with AIQURIS is another step forward in helping the life sciences industry unlock AI’s potential safely, responsibly, and effectively.
