Trust, data, governance: How life sciences can balance the opportunities and risks of AI – responsibly
Conor Healy, CEO of Cork Chamber; Cian Kelliher, consulting partner with KPMG; Rob Horgan president of Cork Chamber; and Naoimh Frawley, director of people, operations and governance with Cork Chamber, promoting the Cork Company of the Year Awards 2026, which are sponsored by KPMG. Photo: Gerard McCarthy
KPMG’s , consulting partner, explores the importance of trust, data and governance in responsible AI adoption in life sciences – highlighting both the opportunities and the critical guardrails needed to implement AI safely and effectively.
Artificial intelligence is rapidly reshaping the life sciences industry, ushering in a new era of discovery, precision and efficiency.
While the potential is enormous, so too is the responsibility that comes with deploying AI in a sector built on patient safety, ethical integrity and regulatory compliance.
AI has already begun revolutionising many aspects of life sciences. In drug discovery, algorithms can detect novel therapeutic targets within massive datasets, accelerating the earliest, and historically slowest, phase of pharmaceutical development. Personalised medicine is becoming more achievable as AI models tailor treatments to individual genetic, biological and lifestyle factors. Clinical trials, long hampered by inefficiencies and dropout rates, are now benefiting from AIbased patient matching, adaptive trial design and realtime monitoring.
In short, AI offers the life sciences sector a unique chance to:
- Boost innovation;
- Cut costs;
- Reduce timelines;
- And bring therapies to market faster.
But this technological leap forward does not come without risks.
Unlike other industries, life sciences operates in a deeply regulated environment centred on patient wellbeing. Trust is nonnegotiable. The recent KPMG report: Intelligent Life Sciences: A Blueprint for Creating Value Through AI-Driven Transformation emphasises that every AIpowered decision, clinical, operational or commercial, must be explainable, fair and safe. Transparency is key: stakeholders, from clinicians to regulators, must understand how AI systems reach their conclusions.
This is especially pressing given welldocumented concerns around algorithmic bias, opaque “black box” models and inconsistent data quality. Without careful oversight, such issues could compromise patient outcomes and erode public confidence.
The pathway to responsible AI begins with data—its quality, its governance and its security. We stress the need for robust data governance frameworks to ensure integrity, privacy and compliance. This includes establishing clear rules for data use, implementing strict access controls and ensuring models are trained on datasets that are accurate, representative and ethically sourced.
Given that life sciences organisations often struggle with fragmented data landscapes, stretched across R&D, clinical operations, manufacturing and commercial functions, harmonising and standardising data is essential for safe AI deployment.
In our 2025 Global Life Sciences CEO Outlook we call for organisations to establish comprehensive AI governance frameworks incorporating:
- Clear policies and oversight structures;
- Rigorous testing and validation procedures;
- Bias detection and mitigation processes;
- Documentation to support regulatory compliance;
- Ongoing audit and performance monitoring.
Such governance ensures that AI adoption is not merely reactive or experimental but embedded within organisational culture, ethical standards and longterm strategy.
AI in life sciences presents a dual challenge: organisations must innovate at pace while also ensuring absolute compliance with evolving regulations. Life sciences leaders recognise this tension. Many CEOs report that ethical considerations, including bias, transparency, fairness and accountability, pose significant barriers to AI implementation, even as they invest aggressively in emerging technologies.
Yet, these same leaders see AI not as optional but essential. Our survey revealed, 75% of life sciences CEOs said AI remains a top investment priority, and 80% reported significant investment in agentic AI, highlighting a sector committed to intelligent automation.
According to global findings from KPMG, 97% of life sciences organisations have already seen operational improvements from AI adoption, while 86% believe embracing AI will deliver a competitive advantage. These gains range from efficiency improvements to streamlined manufacturing and logistics. However, challenges remain around data quality, ROI predictability and overcoming internal risk aversion.
Crucially, organisations that successfully progress through the three phases of AI maturity, Enable, Embed, Evolve are best positioned to unlock sustainable impact. AI maturity requires scaling beyond isolated pilots, aligning with business strategy and embedding trust at every stage.
The message from KPMG is clear: AI is not merely enhancing the life sciences sector it is redefining it. But innovation must be matched with responsibility. Leaders must balance speed with caution, using AI to transform the future of health while safeguarding the rights, safety and trust of the patients they serve.
To do this effectively, life sciences organisations will need to invest not just in technology but in governance, culture, oversight and transparency. Those that strike the right balance will shape the next era of medical breakthroughs, ethically, safely and confidently.
