Despite ongoing uncertainty, pharmaceutical leaders are operating renewed confidence in the clinical research ecosystem. Industry forecasts point to steady revenue growth through 2027, improving internal rates of return among leading biopharma companies, and a global clinical trials market expected to approach $100 billion by 2034.
After years of pressure, clinical research budgets are expanding—but expectations are rising just as fast.
We surveyed 500 industry executives, and what we uncovered shows an industry that’s eager for growth, optimistic about the future and the potential new technologies may offer but also confronted by some familiar challenges.
Survey data suggests that sponsors are prepared to spend more than in previous years to capitalize on emerging opportunities. New therapeutic areas, particularly obesity, diabetes, and oncology, are attracting significant investment. At the same time, organizations are looking beyond molecules alone, focusing on how innovation in trial design and execution can unlock meaningful efficiencies.
Artificial intelligence sits at the center of this conversation. Many leaders see AI as a critical enabler of smarter trials, with the potential to improve protocol feasibility, refine inclusion and exclusion criteria, and support more effective patient identification, engagement, and retention. Real-time data from wearables and digital endpoints further promise to enhance outcome assessment and monitoring.
“Everyone in pharma is talking about AI and with good reason,” Bené said. “Clearly, there are benefits that could significantly enhance outcome assessments, particularly with the widespread use of wearables that provide real time data.”
This optimism is reflected in broader market trends. A majority of pharma leaders report plans to increase AI investment across the value chain, and the global AI in healthcare market is expected to grow dramatically over the coming decade.
AI is widely viewed as a catalyst for smarter trials—but its real value depends on execution.
Yet enthusiasm is paired with caution. While the potential value of AI is widely acknowledged, its practical implementation remains unclear for many organizations. Concerns around data privacy, algorithmic bias, regulatory acceptance, and global scalability persist. Leaders also recognize that AI is not a silver bullet; without clean, integrated data and strong governance, its impact will be limited.
“However, the risks associated with data privacy, errors generated through bias, and global accessibility cannot be overlooked — one size will not fit all,” clarified Bené.
As budgets expand, the key challenge is not whether to invest, but where and how. Organizations that succeed will be those that align technology investments with operational readiness, regulatory strategy, and clear business objectives—ensuring that innovation translates into measurable trial outcomes rather than unrealized potential.