Ways AI Simplifies Contract Management in Global Clinical Studies

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: In what ways can AI help pharmaceutical companies optimize clinical trial operations?

Cowen: AI is certainly top of mind for everyone today, and it can add real value in several areas. One of the biggest is onboarding and clinical trial agreements. Traditionally, agreements are scattered across hard drives, SharePoint, or Word files, making them difficult to access. AI allows us to ingest these documents into a central repository, extract clauses, metadata, and budget tables, and make that information usable—not just for current studies, but for past ones as well. For example, if I need to see how we negotiated a clause with Mayo Clinic three studies ago, I can easily pull that up.

AI also improves the negotiation process by surfacing fallback clauses and highlighting site-specific preferences. If an institution like Mass General or MSK insists on certain language, the system can immediately flag the associated risks and help determine the best course of action.

Another key area is template rationalization. Every therapeutic area, and often every country, requires different contract structures, languages, and currencies, leading to an overwhelming number of templates. AI can analyze and consolidate these, helping to create a stronger, standardized clause library. That way, organizations can leverage proven language from past negotiations and move forward with greater speed and confidence.

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.