From Hype to Impact: How Pharmaceuticals Use Agent AI to Improve Efficiency and Trust

AI in Pharma is becoming more and more real. The abstract hype once about machine intelligence quickly became practical, measurable impact, especially with the advent of proxy AI.

Unlike suggested or supported AI assistants or chatbots, the proxy system is able to complete tasks from main or semi-autonomously with minimal human input. This level of autonomy opens the door to higher productivity, but also requires clarity, trust, and strategic consistency.

In Pharma, precision, compliance and risk reduction are the most important, and proxy AI has nothing to do with futuristic disruption. It’s about helping teams work smarter in existing restrictions. While total autonomy may never be suitable for many healthcare applications, the real-world use cases that have emerged in the real world are practical, measurable, and increasingly valuable.

What makes AI “agent”?

It is important to distinguish proxy AI from other types. Although most of the conversations focus on predicted and generated AI, proxy systems are well suited for operational execution. They will not only inform or inspire—they act within defined boundaries. This distinction is crucial in pharmaceuticals, where workflows often involve repetitive, heavily regulated tasks that benefit from consistency and efficiency without compromising compliance.

AI systems can be classified by techniques (e.g., rules-based, machine learning, deep learning) and functions (e.g., prediction, generation, proxy). What makes proxy AI different is that it not only provides insight. It acts.

This action-oriented capability introduces both opportunities and responsibility. To be effective, a clear understanding of the task, its context and its limitations must be made, especially in high-risk environments such as clinical operation or regulatory submission, and a clear understanding of the agency system must be made. When carefully designed, they become a powerful tool for expanding expertise and reducing bottlenecks.

These systems can follow workflows based on structured parameters, trigger decisions and adjust outputs. Greater autonomy makes it ideal for automating daily tasks but crucial tasks – as long as there are correct safeguards and supervision.

Where it’s already working: Three practical pharmaceutical use cases

1. Simplify research and discovery – Agent AI increasingly supports early research by generating hypotheses, scanning literature, and even identifying potential intellectual property conflicts. By automating basic work, researchers can focus on evaluating and refining ideas rather than manually collecting information.

2. Automation of cross-functional content creation – In areas such as medical affairs, marketing and regulatory documentation, agent systems are being deployed to manage workflows across literature or internal document reviews, copywriting and compliance checks. Multiple agents can be used simultaneously – drafting language, verifying outputs according to standard operating procedures, formatting documents – while maintaining traceability and regulatory standards.
3. Drive regulatory compliance with higher speed and accuracy – From the process of converting commit data to a desired format (such as CDISC) to real-time monitoring of bias, the proxy system can help ensure consistency and integrity in the regulatory workflow. Results: Fewer errors, faster review cycles and stronger audit ready.

Next Frontier: AI as a decision-making partner

One of the most exciting emerging use cases for pharmaceuticals is the ability to use proxy systems to ask internal and external data sources to support strategic decision-making.

For example, consider the key questions about which drug candidates promote clinical development. The decision depends on a complex combination of preclinical and clinical data, market intelligence, competitive landscape and regulatory precedent. AI agents can be trained to synthesize this information, highlight gaps or red flags, and generate comparative summary, allowing leadership teams to make faster and more informative choices.

This is not to replace human judgment. It’s about reducing the time spent in the data and increasing the time to interpret it.

What is holding a company?

Proxy AI has a real commitment, but there are several ongoing barriers to prevent wider adoption:

• There is insufficient understanding of the value that different types of AI (such as predictive versus generators) cannot be delivered for different use cases.
• The relevance of traditional AI as a tool or input for proxy AI is underestimated.
• The doubts surrounding the output generated by AI combine the underutilization of a strong proxy architecture.
• The lack of established governance processes can not handle risks such as data breakdown or model drift.

Solution? Started from childhood.

Organizations should adopt a risk-based approach, starting with administrative and low-risk tasks, and then gradually expand to include more critical applications such as clinical operations or patient-oriented tools. This reflects how the industry has managed innovation: prudent, measure and accountable.

From insight to action: Building a smarter, more agile future of pharmaceuticals

The pharmaceutical industry is no stranger to complexity, regulation or hitting new bets. What changes is how organizations choose to deal with these stresses. AI, especially proxy AI, is quickly becoming part of the answer.

This value is not only automated for automation. This is unlocking talent to focus on strategy, innovation and patient outcomes while delegating significant improvements to repetitive, rule-based and data-intensive tasks to systems that can handle them efficiently and reliably.

But success for Agentic AI doesn’t come from racing to adopt the most gorgeous tools. It will come from strategic consistency, understanding where AI can create real value, mitigate risks through thoughtful implementation, and ensure transparency and supervision at every step.

For biopharmaceutical companies, this means starting with basic use cases, such as simplifying literature or document reviews, enhancing regulatory submissions, and accelerating compliant content creation, and then developing into more complex, high-risk applications such as decision support.

Agent AI is not about hype. This is to bring better results for the team, patients and the entire business.

Image: Yuichiro Chino, Getty Images

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