Why Investigator Onboarding Remains a Major Challenge in Clinical Trial Contracting

In a recent interview with Applied Clinical Trials, Tom Cowen, head, healthcare, life sciences, Conga, discussed the persistent contracting and budgeting challenges that slow down clinical trials and how technology, particularly AI-driven Contract Lifecycle Management (CLM), is helping the industry overcome them. Cowen highlighted that nearly half of study delays are tied to contracting bottlenecks, but with automation, centralized data, and smarter negotiation tools, life sciences organizations can significantly reduce cycle times, cut costs, and accelerate time to market—ultimately improving patient access to critical therapies.

ACT: What are the most common contracting bottlenecks that slow down clinical trials today?

Cowen: Clinical trials are complex by nature—often spanning dozens of sites and countries, across multiple phases, and lasting anywhere from one to four years. Within that complexity, contracting and budgeting, especially investigator onboarding, stand out as major bottlenecks. The Association of Clinical Research Professionals estimates that nearly 49% of study delays result from this process alone.

When we look at Contract Lifecycle Management (CLM), there are clear opportunities to streamline and automate. Whether it’s a single clinical trial agreement (CTA) or dozens of site agreements for a larger study, technology can help accelerate drafting, negotiation, approvals, and signatures—all steps that typically involve multiple stakeholders. At Conga, we’ve seen that organizations can reduce cycle times by about 33% and improve accuracy at a similar rate. Errors that require contract amendments are costly and slow things down, so improving consistency is critical. Overall, these efficiencies have the potential to shorten trials by as much as six months.

What’s interesting is that contracting is largely a mechanical, logical process—step by step—whereas the science of clinical trials is inherently unpredictable. That makes it a prime area for improvement. Beyond the quantitative benefits, there’s also a qualitative factor: ease of doing business. Large medical centers such as MSK, MD Anderson, and Mayo Clinic receive thousands of trial opportunities each year, and they can choose which ones to prioritize. Over time, sponsors that make contracting and budgeting easier stand out, reinforcing the importance of eliminating these bottlenecks.

Full Interview Summary: Clinical trials remain highly complex, often spanning multiple countries, dozens of sites, and lasting one to four years. A major bottleneck slowing these trials is the contracting and budgeting process, particularly investigator onboarding. The Association of Clinical Research Professionals estimates that nearly half of study delays stem from this process. Steps such as drafting clinical trial agreements (CTAs), negotiating terms, securing approvals, and obtaining signatures involve multiple stakeholders, creating opportunities for errors that trigger amendments and further delays. Efficient Contract Lifecycle Management (CLM) can reduce cycle times by roughly 33% and improve accuracy by a similar margin, potentially cutting six months off typical trials.

Artificial intelligence (AI) is increasingly helping pharmaceutical companies streamline operations. By centralizing historical contracts and agreements, AI can extract key clauses, metadata, and budget information, enabling faster, more informed negotiations. It can also support rationalizing and standardizing templates across multiple countries and therapeutic areas, creating stronger clause libraries and reducing legal risk. For instance, major pharma companies using AI-driven CLM tools like Conga have seen investigator onboarding times cut by 50%, with cycle times reduced from 120 days to 60 in some oncology trials.

Smaller biotech firms can also leverage these technologies. Even with fewer resources, CLM platforms allow them to manage agreements efficiently, scale operations quickly, and integrate contract management with commercialization processes. Partnering with CROs is simplified, and centralized platforms make collaboration more seamless.

Looking ahead, innovations in budgeting and patient access are poised to further transform clinical trials. AI can centralize budget data, reduce manual errors, and improve integration with clinical trial management systems. Additionally, patient access platforms, using automated document engines, help ensure therapy adherence and streamline insurance and affordability processes. Collectively, these technologies promise faster, more efficient trials, improved patient engagement, and more predictable operational outcomes.