The pharmaceutical industry is facing growing challenges, including pricing pressures in the United States and geopolitical issues, which are raising fundamental questions about the sustainability of its innovation model. Development is at the core of this challenge, accounting for nearly 70 percent of total R&D expenses, while the risk of failure remains high. The need to accelerate timelines and reduce costs is urgent. Agentic artificial intelligence could transform how drug development is conducted, providing some relief. It offers intelligent, semi-autonomous partners that collaborate throughout the entire drug-development process, improving speed, efficiency, and quality, allowing up to twice as many trials with the same resources and cutting trial durations by as much as 12 months.
Scientific AI can optimize scientific decision-making at the study, program, and portfolio levels by evaluating tradeoffs in molecule and trial designs. This article focuses on the areas where agentic AI can transform development operations.
Unlike generative artificial intelligence, which focuses on creating content based on user prompts, agentic AI can reason, determine next steps, use other tools, and recall past tasks to guide future actions. Achieving the full potential of agentic AI requires not only an orchestrated ecosystem of AI-enabled tools, standardized data flows, and proactive cross-functional collaboration, but also a fundamental reconfiguration of development processes and a clear strategy for managing role transitions. This shift can help deliver medicines to patients more rapidly, improve decision-making, and boost the chances of success.
Transforming end-to-end development workflows with agentic AI
Agentic AI can transform operational workflows in four key areas: operational decision-making, clinical data flow, document generation, and regulatory decision-making.
1. Agentic operational decision-making
Agentic AI enhances clinical operations by enabling real-time monitoring and intervention throughout development, helping agents identify bottlenecks and suggest targeted actions. This is especially valuable in study start-up, which has traditionally been resource-heavy due to manual tasks such as site selection and contracting. Agents can streamline this process by analyzing site performance and demographics to prioritize high-performing sites. Contracting agents can produce first-time-right agreements based on fair market value, cutting down negotiation cycles. Agents can also coordinate site outreach, ensuring quicker alignment on key milestones, which can double site activation rates and reduce staffing needs by 30 to 50 percent. This allows study start-up leaders to focus on more complex issues.
Site engagement can also be transformed. Principal investigators (PIs) are often overwhelmed by untargeted communications. Agentic AI addresses this by using next-best-action algorithms and creating personalized content. Agents can analyze customer relationship management data to predict the best timing, channel, and message for engaging PIs, allowing clinical research associates (CRAs) to focus on higher-value tasks.
One leading pharmaceutical company has developed a multi-agent trial management co-pilot to oversee and support operational decision-making. These agents monitor site activation, patient enrollment, and data management in real time, analyzing data from their clinical control tower. By identifying underperforming sites early and suggesting remedial actions, these agents help their human colleagues keep trials on schedule while minimizing operational workload. The company plans to upgrade these agents to enable direct interaction with PIs and CRAs for routine tasks.
2. Agentic end-to-end data flow
Agentic AI transforms data management and statistical programming from linear, manual tasks to dynamic, iterative workflows, delivering significant efficiency gains and faster trial timelines. This has the potential to redefine roles in data management and statistical programming.
Data cleaning is a critical bottleneck, often involving duplicate and inefficient queries that require significant manual effort, frustrate investigative teams, and result in poor resource utilization. Agentic platforms improve this process by combining large language models (LLMs) with domain-specific heuristics to automatically identify, prioritize, and resolve data discrepancies in large datasets. These platforms can significantly lessen the workload for data managers and CRAs, boost site satisfaction, and potentially result in two to three times fewer queries. By enabling teams to focus their review on the most relevant issues related to endpoints, patient safety, and regulatory quality, it reduces the time and resources required to reach database lock.
Statistical programming, which transforms raw clinical data into the required study data tabulation model (SDTM) and analysis data model (ADaM) formats, is vital but hampered by rigidity. The current process is linear and sequential—from electronic data capture to SDTM, then ADaM, and finally static tables, listings, and figures. Any new data request requires repetitive, complex manual reprogramming of these sequential steps, which fragments workflows and causes delays. Agentic AI transforms this linear process into an agile, iterative one. Agents automate the generation of derivation code—the specific rules and logic used to calculate study variables from raw inputs, track specification changes, and selectively rerun affected programs. This enables dynamic reprogramming and parallel processing, shortening database build timelines from 2 to 3 months to less than 2 weeks and boosting programmer productivity by up to 60 percent.
3. Agentic technical-document generation
Agentic AI is transforming the document authoring process by speeding up key timelines and enhancing overall document quality, allowing humans to focus on informed decision-making. These agents handle the repetitive task of creating essential documents such as protocols, submission dossiers, and safety reports. They add value through data mapping and parsing, managing complex tables, listings, and figures, and linking them to report sections. They also produce high-quality drafts and refinements and generate regulatory-compliant initial versions, enabling humans to quickly update sections. Additionally, agents ensure consistency across documents by flagging updates needed in related files and maintaining alignment throughout the submission package. A multi-agent architecture, such as one McKinsey developed in partnership with a large pharmaceutical client, demonstrates this potential in streamlining clinical study reports. The architecture allows the platform to autonomously plan data extraction, perform analyses, and generate drafts with minimal user input. This deployment cut drafting errors by 50 percent and reduced the time from database lock to report finalization from around 12 weeks to six, facilitating faster trial closeout and regulatory approval. This capability is now being expanded to automatically generate other critical documents, including protocols and informed consent forms, to create a unified ecosystem for intelligent authoring.
4. Agentic regulatory decision-making
Agentic AI is poised to transform regulatory processes, marking a key area of change to better align with health authorities' expectations and enhance the likelihood of first-time-right submissions. Although still in development, initial efforts aim to create a comprehensive suite of regulatory agents that use a proprietary knowledge base of previous submissions and feedback.
The core of this transformation is the idea of a virtual regulator—a digital twin of global health authorities that helps teams anticipate regulatory questions and address potential problems before submission. This agent will perform real-time content reviews based on past feedback, regulatory examples, and guideline sets, including ICH E3 and E6.1 By simulating the perspectives of reviewers from the US Food and Drug Administration, the European Medicines Agency, and other relevant bodies, the agent can flag language likely to raise questions, identify unsupported claims, and check for structural issues. This proactive critique should lead to fewer rework cycles, faster interactions with agencies, and a higher first-cycle approval rate.
Complementing this, a digital twin of the regulatory project manager can actively manage submission readiness, forecast resource needs, and reallocate subject matter experts as priorities change. When integrated with a document-review agent, this system can organize reviewer feedback by theme, highlighting high-priority and cross-functional issues for human consensus. This collaborative, agent-based ecosystem replaces static tracking with predictive oversight, aiming to reduce overall review time by up to 40 days and deliver faster, higher-quality submissions at a lower cost.
Agentic AI represents more than a technological upgrade; it enables a fundamental shift toward peak operational performance. Integrating these capabilities demands a holistic redesign of clinical operations, compelling teams to continuously assess where processes can be simplified or automated. However, realizing this value across the full span of development involves orchestrating upwards of 30 specialized agents. This requires an enterprise-level agentic “foundry”—a centralized hub for continuously designing, training, and operating agents at scale. By transitioning from isolated pilots to this cohesive, integrated architecture, organizations can significantly reduce costs and accelerate pipelines, securing a competitive edge in delivering life-saving medicines.
The authors wish to thank Defne Yorgancioglu and Leo Potters for their contributions to this blog post.
1 Guidelines from the International Council for Harmonisation: E3 defines the structure and content of clinical study reports and E6 sets the standards for good clinical practice.