Success Stories of Pharma Companies Using Contract Lifecycle Management in Trials

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: Can you provide examples of companies that have successfully modernized their trial processes using technology?

Cowen: One top five pharmaceutical company we’ve worked with began this journey about seven or eight years ago in the US. They started by implementing Conga’s core Contract Lifecycle Management (CLM) capabilities and then built out a broader model. Information from their CTMS was integrated with key details from study protocols—such as inclusion and exclusion criteria, patient numbers, and participating sites—and layered into a country-level hierarchy. Because Conga runs on the Salesforce platform, they were able to configure it extensively to match their operating model. After proving success in the U.S., they rolled it out globally. At the start of the project, they were managing nearly 600 templates across therapeutic classes and countries. By streamlining processes, they cut investigator onboarding times for oncology studies from 120 days to 60—an impressive 50% reduction that meant trials reached the FDA two months faster.

Another example is a top 15 pharmaceutical company that took a global-first approach. Their focus was on standardizing the contracting process across countries, a major undertaking given how varied those processes were initially. The result was much greater consistency, improved access to data, and smoother operations worldwide.

Gilead Sciences is another strong case. Thousands of users across the company rely on Conga—not just for clinical trial agreements, but also for market access, supplier contracts, and legal workflows. Their teams have seen significant gains in template management, faster cycle times, and improved ability to adapt contracts to evolving needs.

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.