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 evaluating acquisitions or licensing opportunities can face data rooms containing thousands of documents. AI-assisted intelligence can categorise material, compare contracts, identify regulatory histories and highlight risks.
Quality assurance offers another opportunity. Deviation management and Corrective and Preventive Action (CAPA) processes are essential to pharma manufacturing but, again, involve manual work. AI can classify deviations, identify similar historical incidents, assemble relevant records and flag missing information — although a specialist must remain responsible for investigation and sign-off.
Supply chains and manufacturing can also benefit, with intelligent processing extracting info from certificates, declarations and shipping docs. Predictive models support demand forecasting, while predictive maintenance and computer vision enhance plant operations.
In all these cases, AI addresses high-volume, repetitive tasks with existing data that require significant human effort to extract value.
Organisations scaling AI should focus on high-frequency, lower-risk problems with understood data, measurable outcomes and human oversight.
Scaling safely
Governance must be integrated from the start, rather than added later. Data privacy, IP protection, access controls, validation and accountability are essential parts of the architecture. Enterprise models may require secure environments, sandboxed LLMs and role-based access controls, all while meeting GxP validation standards.
Regulators are exploring similar challenges. The US FDA has piloted AI-assisted scientific review, while the UK's MHRA is using its AI Airlock to examine regulatory challenges associated with AI as a medical device.2,3 The specific questions differ for operational AI, but the principle remains: innovation and governance must develop together.
Success must also be measurable. Has deviation investigation time fallen? Can employees find the correct SOP faster? Has equipment downtime decreased? Can due diligence be completed more efficiently?
A pilot's technical prowess isn't enough without demonstrating business value.
Pharma does not need tech companies to show it how to discover drugs using AI; it is already pioneering its use. What it needs is pragmatic, safe, scalable technology that minimises operational hurdles without risking compliance. The opportunity is to move AI beyond isolated pilots and into the everyday operations of the pharmaceutical enterprise.
References
- https://fptsoftware.com/newsroom/news-and-press-releases/press-release/fpt-releases-global-study-on-scaling-enterprise-ai.
- www.fda.gov/news-events/press-announcements/fda-announces-completion-first-ai-assisted-scientific-review-pilot-and-aggressive-agency-wide-ai.
- www.gov.uk/government/collections/ai-airlock-the-regulatory-sandbox-for-aiamd.
