Possible Futures: Scenario Planning for Clinical Research

Rehearsing the Future

Strategic planners use a tool called scenario planning to think about the future. Initially developed by Royal Dutch Shell for the energy industry in the 1960s, this method stood out in an era when many assumed the future would resemble the past.

Operating in the oil sector, which had experienced consistent, steady growth and stable prices since World War 2, Shell’s scenarios imagined bold but plausible alternative futures. One of these was an abrupt spike in oil prices triggered by geopolitical changes, something considered unthinkable at the time by their peers. When the OPEC embargo of 1973 arrived, Shell was better prepared than its rivals, responding rapidly and decisively to emerge as an industry leader

“Using scenarios is rehearsing the future,” Shell alumnus Peter Schwartz wrote in his 1991 book The Art of the Long View. The purpose is to “make strategic decisions that will sound plausible for all plausible future scenarios.  Scenarios must be simple, dramatic, and bold – to cut through the complexity and aim directly at the heart of an individual decision.”

An Overview of Scenario Planning

Schwartz describes the basic engine of scenario planning in his book The Art of the Long View.

Identify the driving forces. The first step is to identify the major factors shaping the future, typically falling into the categories covered by the acronym STEEP (social, technology, economic, environmental, and political).

Rank by importance and uncertainty. Some influencing forces may have a high impact but be relatively stable. Others may have great variability but have a limited impact. The critical thing is to use a combined ranking to identify the driving forces with attributes that carry the greatest impact and highest risk of uncertainty.

Select scenario matrix. Once you have the driving forces with the highest combined score, form them into axes for a scenario X-Y mapping.  Flesh out each scenario as a detailed, internally consistent narrative.

Identify the implications. Once you’ve created the narrative for each scenario, assess what this means for your decision-making and strategic planning.

Select leading indicators. Determine what early signals would tell you about the scenario that is manifesting itself in the real world?

Driving Forces in Clinical Trials Scenarios

In the prior article for Clinical Trials Day, we reviewed the cycle of “challenge and response” that marked key milestones in clinical trial history. In this article we focus on what comes next. There are many inexorable forces shaping the future of the clinical trials ecosystem, such as accelerating AI usage, increasing clinical trial complexity, shifting to digital technologies, risk-based trial design, decreasing genomics costs, and increased demand driven by global aging. Let’s set those aside for this exercise and instead focus on the key drivers where there is a high degree of uncertainty.

1. Data Sovereignty vs. Harmonization: Globalized trials are an opportunity through decentralized technology. But it’s an open question whether health data will flow across jurisdictions, or fragment behind national walls, commercial agreements, and geopolitical fault lines. This will have the most direct structural impact on trial design to enable a future of global adaptive trials, which would yield huge opportunities in personalized therapies for mass populations.

2. Regulatory Evolution. Computational and synthetic biology are rapidly advancing in their ability to generate evidence. The FDA has even recently adopted AI tools and provided increased guidance. Yet regulatory agencies base approval on the Randomized Clinical Trial (RCT) paradigm of human trials as the gold standard established after World War II. Will the FDA, EMA, PMDA, and NMPA adapt their evidence frameworks for these innovations or defend a continuation of the RCT paradigm?

3. AI/Computational Validation: Beyond regulation, AI is becoming a social and political flashpoint. This will inevitably extend to medical use cases. Do computational representations like digital twins and foundation biology models reach the level of scientific credibility and public trust for clinical trial acceptance? A single high-profile failure, leading to a wave of reversals on AI-driven approvals, could set this back for a generation, regardless of the underlying science.

4. Equity & Access The FDA has announced efforts like Health Care at Home and decentralized clinical trials to improve access, diversity and inclusion for underserved groups. Still it remains to be seen whether innovation will outpace this commitment. Will the continuous health record infrastructure that makes precision trials possible reach the populations historically excluded from trials? Or will it amplify existing barriers, creating a world of precision medicine for the wealthy and generalized, less effective medicines for everyone else?

5. Commercial Incentives Do drug and device-maker business models evolve to reward faster, smaller, more adaptive trials? Or will liability structures, IP strategy, and reimbursement frameworks keep the incentive aligned with the status quo, even if regulatory agencies provide more flexibility?

Four Scenarios for Clinical Trial Futures

We have selected data integrity and regulatory compliance as the two most important factors driving future scenarios. Therefore, they form the y- and x- axes for our matrix. The Data axis ranges from Global Harmonization to Regional Fragmentation. The Regulatory axis ranges from Adaptive Stance to Defensive Stance. This leaves us with four scenarios, one in each quadrant.

"The Living Trial" (Globally Harmonized Data × Adaptive Regulation). This is the optimistic scenario. Continuous real-world data, digital twins, and adaptive regulators produce a world where trials are ongoing, living processes rather than discrete events.

"The Archipelago" (Regionally Fragmented  Data × Adaptive Regulation). This is arguably the most likely scenario. Regulators modernize, but data remains siloed behind national and commercial walls in the US, EU, China, and other major centers. Innovation happens in isolated islands — some countries move fast, others don't. Global trials become a patchwork of bilateral agreements.

The Open Biology Commons (Harmonized + Defensive). In this scenario, the CRO serves as a bridge between the commons layer and the traditional regulatory track. Indicators include patient data cooperatives reaching a critical mass of participants and the emergence of observational data in peer review as primary evidence.

The Fortress Trial (Fragmented + Defensive) The CRO provides efficiency within constraints.  The RCT paradigm remains, but AI can compress timelines and reduce deficiency cycles even inside that framework.  Indicators here would include FDA guidance reaffirming RCT primacy; an increase in AI submissions with deficiencies; data sovereignty rules, such as EU AI Act enforcement.

Implications and Indicators for CROs

For a CRO in the drug, device, and diagnostic sectors, it is critical to contemplate the value proposition needed to provide to clients for all of these possible future scenarios. This is a high-level view of the needs we see evolving for clinical research in each of these scenarios.

The Living Trial (Harmonized + Adaptive). In this scenario, the CRO must become an architect who can help clients build AI-native trial protocols from scratch efficiently and effectively. Key indicators that the world is moving in this direction would include the following: ICH harmonizations accelerate, Real World Evidence is broadly accepted, the FDA and EU provide clear , harmonized regulatory frameworks for AI-generated evidence and digital twins.

The Archipelago (Fragmented + Adaptive). Here, the CRO must serve clients as a navigator and translator, fluent in interpreting a patchwork of strategies required for the unique terrain of different systems in the EU, US, and APAC.  A key indicator for this scenario would be the clear divergence of AI evidence standards across the FDA, EMA, and Asian nations.

The Open Biology Commons (Harmonized + Defensive). In this scenario, the CRO serves as a bridge between the commons layer and the traditional regulatory track. Indicators include patient data cooperatives reaching a critical mass of participants and the emergence of observational data in peer review as primary evidence.

The Fortress Trial (Fragmented + Defensive) The CRO provides efficiency within constraints.  The RCT paradigm remains, but AI can compress timelines and reduce deficiency cycles even inside that framework.  Indicators here would include FDA guidance reaffirming RCT primacy; an increase in AI submissions with deficiencies; data sovereignty rules, such as EU AI Act enforcement.

The key to preparedness is developing flexible and adaptive capabilities that are essential to thrive in any of these scenarios. These areas include AI literacy, which helps clients with digital evidence. It also requires a regulatory navigation and translation strategy, which might include global partnerships, to ensure robust support in fragmented jurisdictions. Finally, it means instituting a process of active learning to continuously monitor signals about where the future is headed.

In The Art of the Long View, Schwartz describes scenarios as “building blocks for designing strategic conversations – conversations that, in themselves, lead to continuous organization learning about key decisions and priorities. At the Landrich Group, we welcome your views on how the future might unfold, and how we can collaborate to create a better tomorrow. Landrich has deep capabilities and experience in the sectors that will shape future clinical research scenarios, such as quality assurance, risk management, and regulatory affairs.