Indian pharma is rapidly moving from discussing generative AI to deploying it across Medical Affairs, commercial operations, content development, field-force enablement and internal knowledge retrieval.
An EY India study found that 50% of surveyed pharmaceutical companies had initiated GenAI proofs of concept, while 25% already had applications in production. EY estimated that GenAI could create a 32–34% productivity impact across the pharmaceutical value chain by 2030.
The potential is considerable. But before asking, “Where can we use AI?”, pharma leaders need to ask a more fundamental question:
What knowledge are we giving AI to represent?
Pharmaceutical knowledge is different
Pharma organizations possess enormous volumes of scientific, medical and commercial information:
- Clinical evidence and publications
- Approved product information
- Medical-response documents
- Regulatory material
- Brand and campaign content
- Field-force training
- CRM and market insights
- Congress updates
- Global and local content
- Agency-created assets
But these documents were developed for different purposes and audiences.
A statement may be scientifically accurate but not approved for promotion. Evidence suitable for a Medical Affairs discussion may not be appropriate for a medical representative. A global claim may require local approval. An HCP response may be unsuitable for patient communication.
AI does not automatically recognize these distinctions simply because the documents have been uploaded to a repository.
AI can make fragmented knowledge sound authoritative
Generative AI is exceptionally good at producing clear, confident language. That is useful, but it also creates risk.
An AI system could combine an older evidence summary, a current brand presentation, an unapproved publication and an agency-created interpretation. The resulting answer may sound complete even though its sources were created for different contexts.
The answer does not need to be fabricated to be problematic.
A factually plausible answer is not automatically medically appropriate, approved or contextually responsible.
The industry is beginning to recognize this. At the 2026 ETPharma Tech Innovate Conclave, Biocon CTO Mandar Ghatnekar emphasized the importance of maintaining human validation of AI-generated outputs. Other participants highlighted the role of data quality, integrity and contextual interpretation. ETPharma
Human oversight is essential. But reviewers cannot sustainably validate every AI response if the organization has not first established which knowledge can be trusted.
Pharma is accumulating knowledge debt
For years, pharmaceutical companies have created content around immediate needs:
- A product-launch presentation
- An advisory-board summary
- A campaign
- A training module
- An HCP email
- A congress update
- A patient-education initiative
Each output may have been reviewed and approved independently. But the underlying knowledge is not always captured in a structured, reusable and governable form.
Multiple versions accumulate. Teams repeatedly search, verify, recreate and approve similar information. This creates pharmaceutical knowledge debt.
AI does not automatically eliminate this debt. It may simply provide a faster way to search through it and potentially distribute its inconsistencies more widely.
The regulatory expectation remains
India’s Uniform Code for Pharmaceutical Marketing Practices 2024 requires information about drugs to be balanced, current, verifiable, non-misleading and capable of substantiation. It must accurately reflect current knowledge or responsible opinion. Department of Pharmaceuticals
These expectations remain applicable when information is retrieved, summarized or generated using AI.
The organization must still know:
- Which source supports the answer
- Whether the source remains current
- What limitations and qualifications apply
- Whether the information is approved
- Who is permitted to use it
- Which audience may receive it
- When escalation to Medical Affairs is required
- Who is responsible for correcting or withdrawing it
The real requirement is not simply a chatbot. It is a governed pathway from source to answer.
Five tests for AI-ready pharma knowledge
Before scaling a knowledge assistant, content generator or field-force copilot, pharma organizations should ask:
- Is it traceable?
Can every important statement be linked to an authoritative source?
- Is it current?
Are review dates, versions and superseded documents clearly identified?
- Is the context preserved?
Does the knowledge retain the indication, population, evidence limitations, safety information and appropriate qualifications?
- Is its use controlled?
Is it clear who may use the information, for which audience, through which channel and for what purpose?
- Is someone accountable?
Is there an identified owner responsible for approval, updating, correction and withdrawal?
If these conditions are absent, the organization may have a content repository, but it does not yet have AI-ready pharmaceutical knowledge.
Build the foundation before scaling AI
Before investing in more AI tools, pharma organisations should:
- Identify authoritative scientific sources
- Separate approved, unapproved and reference-only information
- Remove outdated and duplicate versions
- Structure claims, evidence, FAQs and escalation rules
- Preserve evidence context and limitations
- Establish role- and audience-based permissions
- Assign knowledge owners and review cycles
- Make AI answers traceable to their sources
- Test the system against difficult and high-risk questions
The AI model, retrieval architecture, security and user experience all matter. But none can compensate for an organisation that has not decided which knowledge it trusts.
The companies that gain the greatest value from AI may not be those using the largest number of tools. They may be those that make their pharmaceutical knowledge scientifically reliable, contextually complete, properly approved and difficult to misuse.
Before asking what AI can do for pharma, ask what pharmaceutical knowledge the organisation is prepared to let AI represent.
Good pharma AI does not begin with a prompt.
It begins with knowledge that deserves to be trusted.
Build the pharmaceutical knowledge foundation first. Then make it intelligent.
