Generative AI and LLMs: Where Do They Genuinely Help in Pharma and Supply Chain – and What Is Hallucination?
Large Language Models are the most visible face of AI today, and the pressure to “do something with ChatGPT” reaches every boardroom. In our industry they have real, valuable uses – provided you understand one fundamental characteristic of how they work.
An LLM is a deep learning model trained on enormous amounts of text to do one thing: predict plausible continuations of language. From this single capability emerge remarkable skills, summarizing, translating, drafting, answering questions, writing code. But the mechanism matters: the model generates what is statistically plausible, not what is verified true. When plausibility and truth diverge, you get hallucination: fluent, confident, well-formatted statements that are simply wrong – an invented regulation article, a plausible-but-false GS1 identifier structure, a fabricated reference.
Hallucination is not a bug to be patched; it is intrinsic to the generative mechanism. It can be mitigated – primarily by grounding the model in your own documents (retrieval-augmented generation, where the model must answer based on retrieved verified sources) – but never assumed away.
This defines where LLMs belong in pharma and traceability:
- Excellent, low-risk uses: summarizing deviation reports and batch documentation; drafting SOPs, emails, and audit responses for human review; natural-language querying of technical documentation and regulatory texts; assisting operators with troubleshooting knowledge bases; accelerating code and report generation for data teams. The common pattern: language-heavy tasks, human in the loop, verified sources.
- Uses to approach with strong safeguards: anything customer-facing without review; automated interpretation of regulatory requirements; extraction of critical data where an error propagates silently.
- Uses to avoid: letting an LLM make or record deterministic decisions on serialized product, batch disposition, or compliance status. These require systems of record, not systems of plausibility.
A simple mental model I offer executives: treat an LLM like a brilliant, tireless junior colleague with an occasional tendency to bluff. Extraordinarily useful – as long as someone qualified signs off on the work.
