AI for pharma
AI for pharma companies
Pharma does not have an AI problem, it has a boundary problem, and the boundary is the line where a system starts touching a regulated record.
The productive way to plan AI in a pharmaceutical company is to sort the work by which side of the regulated record line it sits on, then run two different programmes at two different speeds. Firms that run one programme end up applying full validation effort to a literature search tool, conclude AI is too slow to be worth it, and stop.
Below the line, ship this quarter
- Literature and internal document retrieval for scientists, citing the source passage every time.
- Competitive and regulatory intelligence monitoring, with the human reading the source before acting.
- Internal policy and SOP retrieval, where the answer is a pointer to the controlled document rather than a paraphrase of it.
- Commercial and medical affairs content drafting, entering the existing medical, legal and regulatory review unchanged.
None of these produce a regulated record. They need access control, sensible retention and a source citation, and they can go live in weeks rather than quarters.
Above the line, validate properly
Anything touching batch records, deviations, complaints, submission documents or pharmacovigilance case processing is a regulated system. Data integrity expectations apply in full: attributable, legible, contemporaneous, original and accurate records, with computer-generated audit trails that capture changes and are reviewed. In practice that means a documented intended use, risk-based test evidence concentrated where patient safety and product quality risk sits, change control, and periodic review. It also means the human decision stays human. A model can draft a deviation narrative or triage a case for prioritisation, but the assessment and the sign-off belong to a qualified person whose approval is recorded.
Where the largest measurable win sits
For most mid-sized companies it is regulatory document work: assembling and cross-checking submission modules against source documents, finding inconsistencies between a clinical study report and its summary, and answering health authority questions from a body of documents nobody can hold in their head. The model does not author the submission. It finds the disagreements a human would find on the fourth read, which compresses a review cycle without moving the accountable decision.
Digiton builds and operates production AI agents and closed retrieval systems, with deployments across 8 countries, and works in English, Portuguese and French. An AI audit sorts your candidate processes on either side of the regulated record line before anyone commits budget.
Frequently asked questions
Where can pharma companies use AI without full validation?
Anywhere the system does not create or modify a regulated record: literature and internal document retrieval, competitive and regulatory intelligence, SOP lookup that points at the controlled document, and content drafting that still enters the existing medical, legal and regulatory review. These need access control and citations, not a validation programme.
What do GxP audit trail requirements mean for an AI system?
Records must be attributable, legible, contemporaneous, original and accurate, with computer-generated audit trails capturing changes and a documented review of them. For an AI system that means every automated action carries an identity, every human override is stored with a reason, and the trail cannot be edited by the people it records.
Can AI write regulatory submission documents?
It should not author them. The high-value and defensible use is cross-checking: finding inconsistencies between a clinical study report and its summary, tracing claims back to source documents, and drafting responses to health authority questions for a qualified person to assess and sign. The accountable decision stays with the human.
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