Pharmaceutical companies are already pioneers in applying AI to drug discovery and clinical development. The greater challenge is turning experimentation into a system that works across an organisation.
For example, research conducted with Forrester Consulting showed that 41% of organisations cite integrating AI into existing systems and workflows as their biggest implementation hurdle.1 In pharmaceuticals, this challenge is compounded by fragmented data and rigorous compliance requirements.
Yet pharma companies contend with enormous volumes of documentation, fragmented systems and manual processes. SOPs, batch records, deviation reports, equipment manuals and regulatory documents can run to thousands of pages across multiple systems. These conditions can allow a small-scale pilot to succeed while making enterprise-wide deployment much harder.
AI where pharma needs it
This is where operational AI can make a difference. Generative AI, combined with Retrieval-Augmented Generation (RAG) and semantic searches, can create knowledge copilots that search SOPs and compliance documentation by meaning rather than exact keywords. As a result, an employee can ask a question and receive an answer grounded in relevant source material, which matters in pharma.
A chatbot-generated answer is not sufficient in a regulated environment: humans must remain responsible for decisions.
Likewise, business development teams
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