What specific types of decisions or workflows within PPD’s clinical research business are expected to benefit most from AI integration, and how will success be measured beyond speed?
At Thermo Fisher Scientific, we share our customers’ passion for bringing life-changing therapies to patients as quickly as possible. We view AI as a transformational catalyst that enables smarter decisions and helps reimagine the business workflows that drive clinical research.
Within our PPD™ clinical research business, we’re applying AI across the full development spectrum — trial design, patient recruitment and engagement, site activation, data management and quality oversight, and regulatory submission assembly and review. By embedding AI into these core workflows, we’re generating deeper insights and reshaping how studies are designed, executed and analyzed to improve efficiency, quality and speed.
With decades of experience in clinical development, we are leveraging our deep expertise, and data/insights as we expand our application of AI to optimize every phase of our customers’ clinical development journeys. Our goal is to accelerate clinical trial timelines, enhance data quality and improve accessibility, helping our customers bring new medicines to market faster. AI will also help identify therapies that are less likely to succeed earlier in the development process, enabling our customers to reallocate investments toward more promising drug candidates.
Today, our PPD™ clinical research business is using AI augmented workflows and AI Agents and AI assistants with Expert SME oversight to help customers achieve faster study startups, smarter site selection, cleaner data, increased transparency and streamlined regulatory compliance. Recent advances, including AI Agent-powered medical writing that is reducing regulatory document timelines, AI-assisted data quality oversight, a newly launched Clinical Decision Suite with Generative AI capabilities that unifies insights and decision intelligence across clinical studies, and expanding AI capabilities in pharmacovigilance and regulatory intelligence — reflect how broadly and concretely this transformation is already underway. Our collaboration with OpenAI will accelerate and extend this foundation further, co-developing solutions tailored specifically for the future of clinical research.
How does Thermo Fisher plan to validate AI-assisted predictions (e.g., identifying likely trial failures) to ensure reliability and reproducibility before they inform customer decisions?
Krishna Cheriath, VP digital and AI, Biopharma Services, Thermo Fisher Scientific
In practical terms, how will AI be incorporated into the Accelerator™ Drug Development solution — will it influence study design, patient recruitment, data analysis, or another stage?
We are working with OpenAI to accelerate life science innovation by embedding AI across our business. This includes our Accelerator™ Drug Development integrated CDMO and CRO solutions, an end-to-end suite of services covering early pharmaceutical development through commercialization. This integration will amplify the benefits of Accelerator Drug Development to deliver greater speed, scalability, and simplicity to the drug-development journey.
Our AI-driven capabilities will be made available to Accelerator Drug Development customers to enhance manufacturing, packaging and labeling, enabling an increasingly intelligent supply chain for investigational products, ancillaries and lab samples. Through advanced modeling, simulation and predictive analytics, AI also will optimize the scientific and operational aspects of study protocols, strengthen proactive risk mitigation, and advance integrated data visibility and process optimization across manufacturing and clinical systems.
Accelerator™ Drug Development is the operational backbone through which we are bringing our growing AI, digital, and endpoint data capabilities into a unified, end-to-end customer experience — making advanced intelligence accessible and actionable from early development all the way through commercialization.
What governance or oversight structures are being implemented to manage bias, data provenance, and regulatory compliance as AI becomes part of the clinical pipeline?
Governance is essential, and it is built into our innovation foundationally. Our AI governance model aligns with current and emerging frameworks from the FDA, EMA and ICH, and any use of AI in regulated processes is validated and approved under our Quality Management System and Responsible AI Framework prior to use. We maintain clear accountability for how data is used, how AI outputs are reviewed, and how results are continuously monitored for reliability, bias and accuracy. Thermo Fisher has formalized a company-wide Responsible AI policy that applies to all colleagues and all AI use, ensuring that responsible innovation is not an aspiration but a requirement.
How does Thermo Fisher view the balance between automation and human scientific judgment in this transition — particularly when working with early-stage biotech clients?
At Thermo Fisher, we view AI as a way to transform the ways in which we work, amplify human experience and extend scientific expertise. Our approach ensures that automation strengthens decision-making and processes while preserving the critical judgment of our researchers and clinical professionals. This philosophy is central to our goal of being the world's leading patient-centric CRO, where technology amplifies human expertise rather than replacing it.
For early-stage biotech customers, this balanced partnership between humans and AI is essential. They rely on Thermo Fisher for both advanced technology and trusted partnership. While AI helps drive efficiency and insight, our scientists, medical professionals and drug development experts remain key to guiding programs with the rigor, oversight and scientific judgment that ensure the highest quality outcomes.
Looking ahead, what are the most significant technical or cultural barriers to embedding generative AI across such a large, regulated scientific organization?
Embedding generative AI across a global, scientific enterprise requires both technical rigor and cultural adaptability. AI must operate in a governance framework that meets regulatory standards, ensures data integrity and transparency, and can be rigorously validated. This means establishing clear accountability, defining how data is used, who reviews AI outputs and how results are continuously monitored for reliability and bias especially as we advance responsible, transformational innovation.
Culturally, we must create an environment that ensures employees build AI fluency, confidence and trust. Enterprise-wide adoption depends on an environment that encourages curiosity, provides accessible training and fosters a deeper understanding of AI as a continuation of our long-held mindset of continuous improvement.
Across Thermo Fisher, we are taking a company-wide approach to building digital fluency, equipping every colleague, not just specialists, with the mindset, skills and confidence to work in a digital-first world, through training programs, ambassador networks and hands-on access to tools like ChatGPT Enterprise. Our approach will build a team that works smarter, reduce complexities and accelerate scientific progress driving higher value for our customers and advance our mission to make the world healthier, cleaner and safer.