Last week at Summit for Clinical Operations Executives (SCOPE) (1), the energy around artificial intelligence (AI) was unmistakable. From plenary stages to side conversations, AI was framed as the great accelerant of drug discovery and development. Much of that excitement is well-earned. Tools like AlphaFold and increasingly sophisticated molecular simulation models are fundamentally reshaping early discovery, compressing timelines that once took years into months, sometimes weeks (2). Chemistry, target identification, protein structure prediction, and lead optimization are advancing at a pace that would have seemed implausible a decade ago.
Yet a critical question emerges when considering these developments through a global and African lens: What happens after discovery?
Because while AI is rapidly transforming the front end of the pipeline, clinical development remains the stubborn bottleneck, less because of science, and more because of systems.
Clinical development is not just about protocols and endpoints. It is about execution in the real world: regulatory pathways, site readiness, recruitment, data quality, community trust, and financial sustainability. These are not glamorous problems, but they are the ones that determine whether scientific breakthroughs actually reach patients.
Regulatory design and submission are often cited as barriers, but in truth, they may be relatively low-hanging fruit for innovative solutions. With the right tools, templates, and regulatory intelligence, much of this process can be standardized, digitized, and accelerated. AI can and should play a role here, supporting protocol drafting, scenario modeling, submission readiness checks, and even regulator–sponsor interactions.
The deeper challenges lie downstream.
In the United States, most clinical trial recruitment still occurs in academic medical centers, even though most patients receive care outside those settings. This structural mismatch creates predictable bottlenecks: slow enrollment, unrepresentative populations, and trial designs that do not map onto real-world care pathways. At the same time, many sites that are capable of conducting trials find them economically non-viable. Margins are thin, administrative burdens are heavy, and payments are often delayed, sometimes by months, placing unsustainable strain on operations (3–5). These are not hypothetical failures; they are structural features of the current model.
The response emerging in the U.S. is not incremental optimization of individual sites, but consolidation into site networks. Standalone sites struggle to survive without sufficient volume, digitized workflows, and shared operational infrastructure. Networked models aggregate demand, standardize processes, and spread fixed costs, making trials feasible where they otherwise would not be.
From an African perspective, this evolution is instructive. Many of the same constraints, limited workforce, infrastructure gaps, fragmented care delivery, and low or variable trial volume, are present in low- and middle-income settings, often more acutely. Expecting isolated sites to succeed under these conditions is increasingly unrealistic. In contrast, coordinated site networks, coupled with intentional deployment of digital and operational technologies, offer a pathway to leapfrog legacy models and build sustainable, trial-ready capacity at scale. When sites operate as part of a network rather than isolated units, economies of scale are no longer theoretical, they become decisive.
For sponsors, site networks can dramatically improve study start-up timelines through standardized contracting, unified budget frameworks, and consistent regulatory approaches. They reduce friction, uncertainty, and duplication. For sites, networks provide something even more critical: a predictable pipeline. Clinical trials are rarely high-margin activities. Sustainability comes from volume, continuity, and operational maturity, not from one-off studies.
This is where AI’s role in clinical development could be most transformative yet remains under-leveraged. AI is not just a discovery tool; it can be an operations tool. It can support smarter feasibility assessments, real-time recruitment forecasting, adaptive site selection, protocol simplification, and continuous quality monitoring. Combined with site networks, AI has the potential to shift clinical development from artisanal execution to disciplined, scalable delivery (6,7).
Community engagement and recruitment deserve special attention. Increasingly, “diversity” has become a loaded or politicized term in global clinical research. That is unfortunate, because diversity is not a compliance requirement; it is a scientific necessity. Human biology, behavior, and disease expression are shaped by genetics, environment, culture, and health systems. Ignoring that variability weakens science and limits the generalizability of results.
African clinical research has long understood this, often out of necessity. Recruitment is not transactional; it is relational. Trust, relevance, and community partnership are not optional add-ons, they are core infrastructure. AI can support these efforts by identifying gaps, predicting drop-off, and tailoring engagement strategies, but it cannot replace the human systems that sustain participation.
If SCOPE underscored anything, it is that the future of clinical development will not be unlocked by algorithms alone. It will be unlocked by integrating AI with operational redesign, economic realism, and inclusive trial ecosystems. Africa has much to learn from global innovation, but it also has much to teach. The continent could lead early adoption by being unencumbered by legacy processes.
The next wave of progress will belong to those who stop treating clinical development as an afterthought, and start building systems, digital, operational, and human, that are fit for purpose.
Reference List
- Scope Summit [Internet]. [cited 2026 Feb 10]. SCOPE Summit 2026 | February 2-5,2026| Orlando, FL. Available from: https://www.scopesummit.com
- Jumper J, Evans R, Pritzel A, Green T,FigurnovM, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature [Internet]. 2021 Aug [cited 2026 Feb 11];596(7873):583–9. Available from: https://www.nature.com/articles/s41586-021-03819-2
- Kitterman DR, Cheng SK, Dilts DM, Orwoll ES. The Prevalence and Economic Impact of Low-Enrolling Clinical Studies at an Academic Medical Center.AcadMed [Internet]. 2011 Nov 1 [cited 2026 Feb 10];86(11):1360–6. Available from: https://doi.org/10.1097/ACM.0b013e3182306440
- Norris E. Unveiling 2025’s Biggest Site Challenges: Data-Driven Insights to Optimize Site Success [Internet]. ACRP. 2025 [cited 2026 Feb 11]. Available from: https://acrpnet.org/2025/10/14/unveiling-2025s-biggest-site-challenges-data-driven-insights-to-optimize-site-success
- SCRS. The Economic Impact of Payment Terms on Clinical Research Sites [Internet]. Society for Clinical Research Sites. 2025 [cited 2026 Feb 11]. Available from: https://myscrs.org/resources/economic-impact-site-payment-terms/
- OlawadeDB, Fidelis SC,Marinze S, Egbon E, Osunmakinde A, Osborne A. Artificial intelligence in clinical trials: A comprehensive review of opportunities, challenges, and future directions. Int J Med Inf [Internet]. 2026 Feb 1 [cited 2026 Feb 10];206:106141. Available from: https://www.sciencedirect.com/science/article/pii/S1386505625003582
- Accelerating AI in clinical trials and research | McKinsey [Internet]. [cited 2026 Feb 11]. Available from: https://www.mckinsey.com/industries/life-sciences/our-insights/how-artificial-intelligence-can-power-clinical-development