When every lab runs the same AI, know-how stops being an edge
General-purpose assistants give every company the same answers. The know-how that sets your organisation apart has to live somewhere they cannot reach.
Every major pharma and biotech company can license the same frontier models. That is good for productivity, and it raises a strategic question: if every lab asks the same model the same question, what makes your answer better?
Where the edge actually lives
In R&D, competitive advantage rarely comes from access to public knowledge. It comes from things that are not public:
- Assay data and the structure-activity patterns your chemists have learned to read.
- SOPs, templates and review habits refined over many submissions.
- The way your regulatory team argued a similar point in an earlier procedure, and what the agency accepted.
- The judgement of experienced safety physicians on what matters in a case.
A general-purpose assistant knows none of this. Its answers are shaped by public data and the vendor's defaults, which are the same for every customer. Used alone, it pulls every organisation towards the same average.
Two ways to lose the edge
The first is quiet: experts accept generic drafts because they are good enough, and the house standard slowly erodes. The second is more direct: confidential material pasted into public tools leaves your control, which is a trade-secret, patentability and data-protection risk you cannot audit.
Your competitor can license the same model tomorrow. They cannot license your compounds, your data or your scientists' judgement.
Put your know-how where the AI can use it, and others cannot
The alternative is to bring the models to your knowledge rather than the other way round. Private models running in your own cloud can retrieve your SOPs, prior dossiers and study data, cite them in every answer, and be tuned on your own approved documents so drafts read like your team wrote them.
Your experts then do what only they can do: rate answers on real cases. Those ratings decide which model or configuration goes live, so the system improves by your standard rather than the vendor's benchmark. Nothing you build trains anyone else's model.
Managed models still have a place
None of this means ignoring frontier models. For published literature, public guidance and competitor news, they are often the best tool available. A hybrid setup uses them for that public share of the work, and keeps everything that carries your know-how on the private side.
The question to ask
For each AI workflow, ask where the knowledge that makes the answer good actually comes from. If the answer is your own data and your own experts, that workflow belongs on AI that is yours.