The clinical trial ecosystem is entering a phase of consolidation and reinvention driven by the collapse of boundaries between functions, data, and even companies themselves.
In 2025, we saw clinical operations leaders in biotech, pharma, CROs, as well as technology vendors start to work to capture more of their value chain. New partnerships and collaborative ecosystems are slowly dissolving historical silos.
In 2026, non-traditional players will continue to disrupt clinical research. Compounding pharmacies and telehealth providers have growing market power, medical-grade consumer health wearable devices are delivering real-time diagnostic insights, and new payment models (like most-favored nation status or direct-to-consumer payments) are changing the way patients get and pay for care. We expect this will also change the way new therapies, devices and diagnostics are brought to market, especially as organizational strategies change to meet the market moment. The entry of more non-traditional players into the industry will encourage all stakeholders to consider the distinctions between regulated and non-regulated spaces, with implications that can influence payment, privacy, access to care, and experimental medicines across the industry.
We also expect that platformization—the consolidation of tools into a single unified solution—will move from theory to reality. As technology, data standards, and operational models are harmonized into intelligent end-to-end systems, we will finally start to see the end of piecemeal innovations.
Platformization will be a key catalyst for the industry to deliver more value, as traditional EDC and protocol management give way to “living protocols” and automated data capture, powered by regulatory harmonization (notably
The maturation of hyper-personalized protocol tailoring supported by seamless integration with healthcare data sets will begin in earnest, driving targeted trial recruitment and inflight tuning of trial design without the historical barriers that come with protocol amendments. This opens the door for more experimentation for study teams. For example, testing multiple variations of study and compound delivery approaches, which can be rapidly tweaked in collaboration with discovery teams with minimal disruption to portfolio timelines.
This year, we observed a significant reduction in capacity within the FDA and an exodus of experienced staff. The agency pivoted to primarily focus on the day-to-day process of review to continue to drive the engine of market authorizations. This came at the expense of the agency’s historical energy toward policy development, new guidance, and scientific advice.
In 2026, we expect to see a slow recovery of policy and guidance work, although we anticipate a long ‘refractory period’ as momentum lost in policy formulation is regained. This will likely lead to a slip in the FDA’s leadership position amongst regulatory agencies. Clinical development program leaders can expect a slow resumption in engagement (e.g. for scientific advisory meetings) but there is cause for concern for clinical development innovators, as the industry will still to the US to guide risk-tolerance and acceptability.
The impact of artificial intelligence (AI) in 2026 will not be about shiny new pilot projects. Rather, its impact will be realized by the stakeholders and organizations that will have rebuilt core clinical trial processes and workforces around AI as the default approach. Regulatory authorities are continuing to be dissatisfied with “black box” results, and rather want traceable, explainable logic and robust data provenance for every clinical decision made or supported by AI. An example of this is the
We are seeing the beginning of a sharp divide. On one side, organizations building AI fluency into every layer of the clinical process; on the other, legacy operators still piloting stand-alone “AI use cases.”
By 2026, that gap will dictate survival. We foresee that an organization’s AI fluency, measured in talent, governance, and operational agility, will become its number one differentiator.
AI must be an operating system for drug development. The shift we are seeing is toward teams rebuilt around data product ownership, agile pods, and prompt engineers. In these models, AI literacy is foundational, with the boundaries of traditional roles automated out and enabled by new standards for explainability and interoperability.
As such, we predict five major trends to be on the lookout for in the new year:
Charlie Paterson, Health and Life Sciences Expert; Jenna Phillips, Health and Life Sciences Expert; Kieran Reals, Operational Improvement Expert; and Naveed Panjwani, Clinical Development Expert; all with PA Consulting
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