Jayaprakash Nair is head of AI and Analytics at Altimetrik.

Jayaprakash Nair

Futuristic concept of artificial intelligence and large language models (LLM) with glowing AI icons and human hand interface. Innovation in machine learning and digital transformation. See Less | Image Credit: © Looker_Studio -stock.adobe.com

Pharma is captivated by agentic artificial intelligence’s (AI’s) promise: AI systems that can independently coordinate complex workflows, reason across siloed functions, and adapt their approach based on results, all within defined guardrails.

But there’s a fundamental mismatch between this vision and where most pharmaceutical organizations stand. The foundation models powering these agents were trained on public data. They understand language exceptionally well. What they don’t understand is a specific company’s batch records, validated procedures, or deviation protocols—the proprietary documentation that lives behind its firewall.

This creates a critical gap in AI maturity. Enterprises typically operate across four levels of sophistication:

 Users send questions directly to a platform such as ChatGPT or similar tools and receive answers based solely on the model’s training data.

 Companies connect foundation models to local databases, retrieve relevant documents, and send them with prompts to provide context. This is where most pharmaceutical companies operate today.

 Organizations take a foundation model, install it in their environment, and retrain it on their proprietary data, fundamentally changing the model’s composition.

 An expensive endeavor that remains out of scope for most enterprises.

Most pharmaceutical companies are stuck at level two because level one is too naive for regulated operations, while levels three and four are prohibitively expensive (large language model [LLM] fine-tuning runs can cost $10,000–$15,000 each). There’s a middle path that works exceptionally well for industries where the stakes are high: domain-specific language models.

Foundational LLMs achieve proficiency with billions or trillions of parameters. GPT-5, for example, 

 (1). These behemoths encode knowledge across every conceivable domain. For pharmaceutical operations, most of that breadth is wasted capacity.

Domain-specific language models take a smaller base model and train its capacity intensively on the company’s enterprise documentation. Instead of knowing a little about everything, they know everything about the company’s operations. 

 (2) rates domain-specific GenAI models as “High Benefit,” yet adoption remains under 5%, indicating both the opportunity and the early stage of this approach.

It matters profoundly in pharma, where the margin for error is zero. When AI informs decisions about drug manufacturing, clinical protocols, or quality investigations, “close enough” based on generic industry knowledge isn’t acceptable. The system needs to understand the specific processes—the ones regulators audited, the ones that determine whether a batch of diagnostics gets released to patients.

The implementation is more accessible than traditional fine-tuning. Zero-shot training allows business users to feed documentation directly without preparing elaborate training datasets. The system processes a company’s standard operating procedures (SOPs) and deviation reports, building understanding from the company’s corpus. When responses miss the mark, subject matter experts correct them through the interface, and those corrections feed back through reinforcement learning. The model improves continuously.

This method combines the best elements of RAG and fine-tuning. The ability to query specific documents is maintained while fundamentally changing how the model understands the domain.

 (3), a localized framework for drug labeling documents that operates within secure IT environments. The system achieved 95% accuracy in drug name recognition.

Why does the current approach fall short?

To understand why domain-specific models matter, consider where RAG breaks down. Error of omission occurs when retrieval systems leave behind critical information. The AI may need three related SOPs to answer a question about equipment changeover, but the system only surfaces two. The missing context leads to hallucinations.

Error of commission happens when foundational models’ pre-existing training conflicts with the validated processes. Ask about cleaning validation, and the model provides textbook answers based on public guidance, not the facility’s validated process.

No large language model is currently FDA-authorized

 (4) as a clinical decision support device. Yet pharmaceutical operations demand systems that can inform decisions about product release and quality, creating a gap that domain-specific models are better positioned to address.

A company’s most valuable documentation will never appear in foundation model training data because it lives within the enterprise environment. Foundation model API calls are also priced per token. When processing batch records that span more than 150 pages of technical data, operational costs accumulate quickly.

Domain-specific models are ready for any pharmaceutical application that requires a deep understanding of the proprietary processes, like batch record analysis, deviation investigation, protocol compliance checking, or supply chain optimization for time-sensitive products.

This brings us back to agentic AI and why domain-specific models are a prerequisite. Autonomous agents make decisions. They reason about the best path to achieve a goal, coordinate with other agents, and adapt based on results. The value proposition depends entirely on trusting those decisions. Trust requires that the underlying intelligence understands what it’s operating on.

The frameworks that coordinate multiple AI agents, like AutoGPT and LangChain, depend entirely on the underlying models’ knowledge. If those models lack domain knowledge, the agents won’t have it either.

Consider bounded autonomy, the guardrails preventing agents from exceeding authorized scope. What constitutes acceptable autonomous action in your validated manufacturing environment? The answer depends on the procedures, risk assessments, and regulatory commitments. Boundaries need to be defined by domain-specific requirements.

Human oversight compounds this challenge. With autonomous agents, the paradigm shifts to human-in-the-loop supervision, where humans intervene only when certain conditions trigger escalation. What should those triggers be? They should reflect risk tolerance, deviation patterns, and quality standards. A domain-specific model can recognize when situations deviate from normal operations. A foundation model lacks that institutional knowledge.

The vision of agentic AI in pharmaceutical operations is compelling, but you can’t skip from basic prompting to autonomous multi-agent systems. The intermediate layer—models that understand a company’s operations because they were trained on that company’s documentation—is not optional.

The pharmaceutical industry’s document-intensive, process-rigorous environment makes it a natural fit for domain-specific language models. The documentation already exists. The need for AI that understands proprietary processes is clear. The technology has matured. What’s required is recognition that before pharma can trust AI agents to operate autonomously, those agents need training on your SOPs, batch records, and validated operations.

Domain-specific models represent the final mile of AI for pharma, closing the gap between general capabilities and operational reality. Organizations that build this foundation first will be positioned to deploy agents that actually deliver on their promise in pharmaceutical operations.

Battsengel Sergelen. Introducing GPT-5: Redefining the Future of Artificial Intelligence. 

www.linkedin.com/posts/battsengel-sergelen_introducing-gpt-5-redefining-the-future-activity-7359414711014080512-ZO54/

Chandrasekaran, A. The 2025 Hype Cycle for GenAI Highlights Critical Innovations.” 

www.gartner.com/en/articles/hype-cycle-for-genai. Accessed 1 Dec. 2025

Wu, Leihong, et al. A Framework Enabling LLMs into Regulatory Environment for Transparency and Trustworthiness and Its Application to Drug Labeling Document. 

 149, (105613) pp. 105613–105613, DOI: 

Weissman, G.; Mankowitz, T.; Kanter, G.; et al. Large Language Model Non-compliance with FDA Guidance for Clinical Decision Support Sevices, Sept. 9, 2024, PREPRINT (Version 1) available at Research Square. 

Articles

Pharmaceutical companies are intrigued by agentic AI's potential to autonomously manage complex workflows, but face challenges due to AI's reliance on public data, which lacks proprietary company-specific information. Most companies are at the retrieval augmented generation (RAG) stage, as fine-tuning and custom model creation are costly. Domain-specific language models offer a solution by training on proprietary data, enhancing AI's understanding of company operations. This approach is crucial for high-stakes environments like pharma, where precise adherence to validated processes is essential for decision-making and regulatory compliance.

Large-language models are excellent for general-use AI systems, but they don’t understand pharmaceutical companies’ proprietary documentation—the validated procedures and quality protocols that ensure drug safety. Smaller, domain-specific language models give companies more control and efficiency in their AI use.