Pharma’s New Co-Worker: Why Three-Quarters of Drug Companies Are Racing to Build Agentic AI
Imagine an AI that doesn’t just answer questions but acts—booking meetings, compiling regulatory evidence, and triaging patient queries—with the autonomy of a junior employee and the recall of a supercomputer. That future is already here for many drugmakers.
Pharmaceutical companies are moving fast to embed “agentic” AI—autonomous, goal-directed AI agents—into commercial and medical workflows. A new MIT Technology Review Insights study, commissioned with Globant and based on a survey of 250 senior pharma executives across nine countries, finds that nearly three in four (73%) of pharmaceutical organizations are planning, piloting, or deploying agentic AI initiatives, with adoption expected to accelerate over the next 12–18 months. (PR Newswire)
What the study found
- Adoption is widespread and growing: 73% of respondents are at some stage of planning, piloting, or deploying agentic AI—some already in production. (PR Newswire)
- Top use cases: Regulatory compliance (51%), data standardization (49%), patient support (46%), and market intelligence (46%) are the highest-priority commercial/medical use cases. (PR Newswire)
- Biggest barriers: Workflow design and orchestration (60%) and compliance/validation (55%) top the list of deployment challenges; tech infrastructure (42%) and data governance (38%) are also significant. (PR Newswire)
- Data first: 84% plan to prioritize data standardization and integration to make agents reliable at scale—reflecting a consensus that good agents require disciplined data plumbing. (PR Newswire)
- Economic promise: MIT Technology Review Insights frames generative/agentic AI as poised to add an estimated $60–$110 billion annually in economic value for pharma and medical products—if the tech is stewarded properly. (PR Newswire)
Why this matters
Agentic AI moves the needle from “tools” to “teammates.” For pharma, that shift is consequential because the industry juggles high regulatory risk, complex data silos, and intensely personalized stakeholder communication (KOLs, clinicians, patients). Agents can automate repetitive orchestration tasks—summarizing literature, pulling regulatory clauses into workflow checks, generating tailored HCP materials—freeing human experts to focus on judgement and strategy. Several industry leaders quoted in the report stress the need to pair autonomy with clear human oversight and built-in compliance. (PR Newswire)
But adoption isn’t just technical. The survey makes it clear that organizational change, data hygiene, and compliance workflows are the gating factors. In other words: the companies most likely to win with agentic AI are those that treat it as an organizational transformation (people + process + tech), not a plug-in product.
The tradeoffs: speed vs. safety
Agentic systems excel at speed and scale—but they amplify risk where regulatory or ethical mistakes matter most. The report highlights compliance/validation as a major concern. That tension will shape the next wave of real-world use: expect hybrid models where agents perform low-risk automation and intelligence tasks, plus human sign-off at predefined handoff points. As Salesforce’s life-sciences lead and a Johnson & Johnson commercial data science VP note, embedding legislation/legible audit trails and setting explicit handoff points are essential. (PR Newswire)
What leaders should do now
- Prioritize data standardization and integration. The survey shows 84% planning that—a smart first step because reliable agents need consistent, discoverable inputs. (PR Newswire)
- Design workflow orchestration deliberately. Map where agents act autonomously and where human oversight is mandatory; define audit trails. (PR Newswire)
- Treat compliance as a design constraint, not an afterthought. Build compliance validators into agent workflows from day one. (PR Newswire)
- Upskill teams. As one executive put it: AI won’t take your job—someone using AI better than you might. Embrace training and role redesign. (PR Newswire)
Deeper reflections
This moment feels like the difference between early automation (macros, RPA) and collaboration (AI as a working partner). If pharma firms succeed, agentic AI could reduce time to market, personalize patient and HCP engagement, and de-risk routine compliance tasks—unlocking the economic value the report projects. But the flip side is real: poorly governed agents could amplify regulatory missteps, bias, and data leaks. The winners will be the companies that combine technical rigor, regulatory savvy, and human-centered design.
Glossary
- Agentic AI: Autonomous, goal-directed AI systems that can perform multi-step tasks with limited human intervention (sometimes called “AI agents”). (PR Newswire)
- Workflow orchestration: The design and automation of multi-step processes, including task sequencing, handoffs, and exception handling. (PR Newswire)
- Data standardization: Converting data to consistent formats and taxonomies so systems (and agents) can interpret and act on it reliably. (PR Newswire)
- Compliance/validation: Processes that ensure a system’s output meets regulatory and safety requirements and is auditable. (PR Newswire)
Source: https://www.prnewswire.com/news-releases/mit-technology-review-insights-and-globant-report-three-quarters-of-global-pharma-organizations-are-piloting-or-deploying-agentic-ai-302583623.html. (PR Newswire)
