In 2026, clinical trial design will be reshaped by artificial intelligence (AI)-powered simulation and richer clinical and real-world data. Improvements in and a focus on the patient experience will emerge as a competitive differentiator, driving higher retention and better quality data. Moreover, regulators across geographies, once broadly aligned, are now diverging, forcing sponsors and contract research organisations (CROs) to navigate fragmented expectations. Taken together, these dynamics mark 2026 as a turning point. This will be the year when the clinical trials industry begins to move from exploration to execution, with the groundwork laid over the past years putting clinical trials in a strong position to turn AI’s theoretical benefits into concrete operational outcomes.
AI will begin to deliver real value
The past years have marked a rapid evolution in how the industry talks about, and now expects, AI advancements. In 2023, every discussion started with the same question: “What are you doing in AI?”
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By early 2024, the focus had narrowed to generative AI. Traditional machine-learning (ML) models were no longer the headline; sponsors wanted to understand how generative tools could reshape workflows and decision-making. By 2025, the terminology shifted once again. Agentic AI became the dominant theme, reflecting a growing interest in systems that can take action.
These shifts demonstrate a meaningful change in our industry: a workforce that is becoming increasingly fluent in the language and possibilities of AI. After years of caution, organisations are now more willing to experiment and to see what happens in practice, rather than piloting ideas indefinitely.
After several years of intense AI investment and discussion, however, the industry has reached a point where return on investment can no longer be theoretical. Organisations have poured significant resources into AI infrastructure, talent, and experimentation. The level of spending, combined with the sheer volume of attention AI has commanded, has created an inflexion point. In 2026, firms will be expected to demonstrate tangible value from the models and capabilities they have built.
One way that AI will deliver return on investment in 2026 is by slashing costly clinical trial missteps through simulation, made possible by increases in data collection and centralisation. Next year, we will see an increase in clinical, operational, and real-world data being used together, which will begin to reshape the foundations of trial design and execution. For years, sponsors have relied on retrospective analyses, intuition, and fragmented feasibility insights to design protocols. But with the increased use of wearables and other digital data collection assets (which makes real-world data more common in trials) and the ability of AI to analyse and simulate, the volume and richness of today’s data now make a new approach possible. Trials now use, on average, seven times more data points than they did 20 years ago.
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By GlobalDataForward-looking companies will seize on the opportunity to turn data volume and complexity into a strategic asset. AI-enabled simulation tools are emerging as one of the most meaningful developments in this space, giving teams the ability to model a trial end-to-end before the first site is activated. Organisations will be able to test assumptions, evaluate multiple scenarios, and expose bottlenecks long before they turn into delays. Eligibility criteria can be stress-tested, enrolment curves can be predicted, and more molecules can be disqualified in Phase I and II before the more expensive Phase III trials. This foresight allows teams to refine protocols with greater precision, reducing the need for costly amendments and supporting earlier, more confident decision-making. This will have dramatic effects for sponsors and CROs, reducing development timelines for trials by at least six months. Consequently, clinical trials in 2026 will become more predictive and proactive rather than reactive.
Diverging regulation
In 2026, one of the biggest challenges facing the life sciences industry will be navigating a growing divergence in regulatory approaches across countries. Over the last decade, sponsors have operated under a broadly harmonised set of expectations for clinical trials and drug approvals, allowing companies to plan global development programmes with some predictability. That alignment is now fraying.
A particularly pressing issue is the rise of data sovereignty requirements. Increasingly, regulators are mandating that clinical data must remain in the country where it was collected. While this is intended to protect privacy and security, it fundamentally disrupts centralised data strategies that have long supported efficient trial design, creating operational friction. This slows down the development of life-saving drugs for patients.
For sponsors and CROs, 2026 will require agile strategies to manage compliance while preserving the efficiencies that new technologies enable. Harmonisation may be possible over time, but the immediate impact of these divergences is significant: companies will need to rethink how they collect, store, and analyse data, and how they design trials to satisfy multiple, sometimes conflicting, regulatory expectations. Successfully navigating this environment will be critical not only for speed to market but also for maintaining patient trust in an increasingly complex data landscape.
Time for impact
2026 presents a great opportunity for sponsors and CROs. AI is moving from theory to practice, enabling predictive trial design, more efficient protocols, and faster decision-making that will lead to more successful clinical trials. Decentralised trials are improving patient experience and data quality, while wearables and digital tools are helping create more inclusive and representative datasets.
However, there are some challenges. Diverging regulatory frameworks and data sovereignty requirements in 2026 will complicate global trial strategies, requiring sponsors to adopt agile approaches to compliance while maintaining operational efficiency. Navigating these differences will be critical to ensure treatments can reach markets worldwide.
Overall, 2026 is the year that AI will be expected to prove its value. The groundwork laid over the past several years positions the industry to translate AI into concrete impact. Organisations that can integrate AI and regulatory strategy effectively will define success in this next chapter of clinical development.
