From discovery to delivery: AI reshapes life sciences and pharma

For an industry built around scientific discovery, precision and rigorous controls, artificial intelligence is opening up new opportunities across almost every stage of the life sciences and pharmaceutical value chain.

From identifying potential drug candidates to improving clinical trials, pharmacovigilance, manufacturing and quality operations, AI is increasingly being embedded into everyday processes rather than treated as a standalone experiment.

For Elaine Daly, partner and head of business consulting at Forvis Mazars, the shift is particularly significant for Ireland, given its concentration of global pharma, medtech and healthcare organisations.

“AI is moving from the fringes of life sciences and healthcare to the centre of how work gets done. What has changed is less the ambition and more the practicality. Mature cloud platforms, improved data infrastructure and advances in generative AI mean organisations can now embed AI into everyday operations rather than treating it as a standalone experiment,” she said.

In drug discovery, AI is already being used to analyse biological and molecular information and help researchers identify the most promising opportunities.

“AI is helping researchers identify biological targets, analyse genomic and molecular data, and prioritise the most promising compounds for development. Machine learning models can rapidly screen millions of potential candidates digitally, allowing scientists to focus their laboratory work on the highest-value opportunities,” said Daly.

“AI is also improving molecular design and helping organisations identify approved medicines that may have potential in new therapeutic areas.”

Clinical development is another area where AI is gaining traction, particularly around some of the resource-intensive processes involved in running trials.

“AI is improving patient recruitment, protocol design, trial-data quality and signal detection. One of the biggest challenges in clinical research has always been identifying suitable patient populations and managing large volumes of documentation,” said Daly.

Generative AI is also beginning to support research and documentation, although Daly stresses the importance of appropriate oversight in regulated environments.

“AI is increasingly being used to support these activities, while generative AI is helping with literature reviews and drafting regulated documentation under appropriate human oversight,” she said.

The applications extend beyond research and development into manufacturing and quality operations.

“Manufacturing and quality operations are also seeing growing adoption. Computer vision, advanced analytics and predictive monitoring can improve defect detection, quality control and operational efficiency,” said Daly.

Pharmacovigilance is another area where AI has particular potential, especially given Ireland’s role as a global hub for pharmaceutical safety operations.

“AI is already being used to support case processing, triage and signal detection, the real value comes not from replacing scientific expertise, but from helping experts reach better, faster and more informed decisions” she said.

Across all these applications, Daly emphasises that the objective is not to replace scientific expertise but to enhance it.

“The organisations that will benefit most will not necessarily be those with the most advanced models. They will be the ones that build strong data foundations, establish appropriate governance and focus on solving real operational and clinical challenges rather than pursuing technology for its own sake” she said.

That makes data readiness one of the less visible but most important elements of any AI strategy. Life sciences organisations often have decades of information distributed across legacy systems, spreadsheets, departmental databases and disconnected applications.

“If the underlying data is fragmented, inconsistent or poorly governed, the outputs will be unreliable regardless of how sophisticated the technology is,” said Daly.

Bringing that information together requires organisations to catalogue their data assets, integrate disparate sources, address quality issues and establish consistent definitions, ownership and governance.

For life sciences companies, those foundations also need to reflect regulatory requirements from the beginning, including GDPR, the EU AI Act and sector-specific GxP controls.

“The regulatory implications will depend heavily on how the technology is used. An AI tool that performs medical analysis or forms part of a medical device may be treated as high-risk under the EU AI Act. That changes the approach from day one. Human oversight must be built into the solution, with compliance shaping its design rather than being checked at the end,” said Daly.

The challenge, therefore, is not simply adopting AI but creating the infrastructure, governance and operating models that allow it to work reliably at scale. As organisations move beyond pilots, the winners are likely to be those that combine technological capability with scientific expertise, rigorous controls and a clear focus on measurable value.

The full text of the article can be found on the Business Post.