Pharmaceutical companies are abandoning the idea that a single AI platform can solve all their clinical trial problems. Instead, they're adopting modular, task-specific AI agents that work alongside existing systems, according to Saama, a life sciences AI company that has spent over a decade building regulated AI solutions for drug development.

What's Driving the Shift Away From Monolithic Platforms?

For years, pharmaceutical companies invested in massive enterprise software systems designed to handle every aspect of clinical trials at once. But that approach created a problem: these systems didn't always talk to each other, and companies had to replace their entire technology stack to adopt new solutions. Saama identified this inefficiency as a major inflection point in the industry.

The company's response was to move beyond offering only monolithic enterprise software. Instead, Saama now provides modular stacks and smaller agents and agentic components designed for specific tasks, personas, or both. This means a pharmaceutical company doesn't have to rip out its existing infrastructure to benefit from AI.

"Our continued investment and maturity have reached a point where the industry was previously spending money on many different pieces of the puzzle, often from different vendors. These systems did not necessarily communicate with one another. Saama's difference is a unified approach. We can provide individual or modular solutions, while also working with other investments that a customer already has," explained Ari Srinivasan, Chief Customer Success and Growth Officer at Saama.

Ari Srinivasan, Chief Customer Success and Growth Officer at Saama

How Is the Pharmaceutical Industry Changing Its Approach to AI?

The pharmaceutical industry's relationship with technology has transformed dramatically. Historically, pharma was not an early adopter of new tools; it tended to be a fast follower or even a laggard. But the COVID-19 pandemic accelerated a shift in mindset. Over the last few years, the industry has started actively seeking solutions that remain within the regulated ecosystem but are much more agile.

This shift has also changed who's buying AI solutions. Saama's customer base has evolved significantly. Originally, the company focused heavily on top-tier pharmaceutical companies. Over the last two to three years, that reliance has reduced to around half of revenue, with the remainder increasingly coming from the small and mid-market biotech ecosystem. The company is also developing emerging relationships with contract research organizations (CROs), which conduct clinical trials on behalf of pharmaceutical sponsors.

Steps to Implementing Modular AI in Clinical Trials

• Assess Existing Systems: Evaluate your current technology stack to identify which systems are working well and which create bottlenecks or communication gaps between departments.
• Identify High-Impact Tasks: Prioritize specific clinical trial functions where AI agents can deliver immediate value, such as data management, medical monitoring, or quality management, rather than attempting a complete overhaul.
• Plan for Change Management: Invest in talent development, subject matter expertise, and cross-training so teams can transition from traditional workflows to AI-assisted processes with minimal disruption.
• Integrate Gradually: Deploy modular AI components that work alongside existing investments, allowing your organization to adopt new capabilities without replacing entire systems at once.

Saama's platform supports the clinical trial lifecycle from study start-up through to regulatory submissions. The platform includes several dimensions across that lifecycle, including study start-up, data management, clinical operations, risk-based quality management, medical monitoring, and submission support.

Why Does Regulated AI Experience Matter More Than Money?

A critical insight from Saama's approach is that simply having funding and access to cutting-edge AI models isn't enough in a regulated industry like pharmaceuticals. The company started working with GPT models before ChatGPT became mainstream, but quickly realized that large language models (LLMs), which are AI systems trained on vast amounts of text data, couldn't simply be used to answer medical questions without safeguards against hallucination, a phenomenon where AI systems generate plausible-sounding but false information.

To address this, Saama took a Meta Llama model, a publicly available foundation model, and developed a custom large language model trained using medical question-and-answer datasets. When benchmarked against GPT, it demonstrated significantly better accuracy for medical applications. This kind of specialized expertise cannot be replicated simply by spending more money on licensing or computing resources.

"You cannot beat 10 years of regulated experience, augmented by subject matter expertise and iterated with humans in the loop, and then deliver that as a regulatory-grade, enterprise-grade offering in six months simply because you have invested more money," stated Srinivasan.

Ari Srinivasan, Chief Customer Success and Growth Officer at Saama

The pharmaceutical industry's move toward modular, task-specific AI agents reflects a broader maturation in how companies think about technology adoption. Rather than seeking a silver-bullet solution, organizations are recognizing that the most effective approach combines specialized AI tools, deep regulatory knowledge, and careful change management. This pragmatic shift suggests that the future of AI in drug development isn't about replacing human expertise, but augmenting it with intelligent, focused tools designed for specific challenges in the clinical trial process.