Turnover in clinical operations is often discussed as an unfortunate reality of a demanding industry. Long hours, aggressive timelines, and increasing complexity are cited as unavoidable contributors. But after more than two decades leading clinical teams across sponsor and contract research organization environments, the author of this column has come to see turnover differently.
Artificial intelligence (AI) has fundamentally reshaped the clinical data industry. We routinely harness AI to generate deeper insights from complex trial data, accelerate detection of risk, and enhance oversight. Yet across the broader enterprise, many organizations have not embedded AI deeply enough into their internal workflows to unlock its full value.
For many early-career clinical researchers, trial design can feel rigid. A primary endpoint is selected, the study is powered around it, and success is determined by that single measure. But patients do not experience treatment effects through one outcome. They experience a combination of efficacy, side effects, symptoms, and quality of life. Reducing this to a single outcome can risk missing the bigger picture.
The need for diversity in clinical trials and the challenges of identifying patients who are unknown to the healthcare system complicate trial enrollment and add financial stress that may hinder the success of a study. A targeted, engineered approach to patient enrollment and diversity, using a financially calculable strategy, can ameliorate these challenges.
As experienced pharmaceutical industry executive Mo Ali settled into his volunteer duties as Chair of the ACRP Board of Trustees for 2026, a year in which the Association celebrates its 50th anniversary of service to its members and stakeholders, he took time to answer some questions about his experience in the clinical research enterprise, some of the challenges and opportunities facing it, and how ACRP can further its mission in a time of rapidly evolving technologies and processes.