Tom Cowen, head, healthcare, life sciences, Conga, explains how biotech startups use scalable contract management platforms to efficiently manage clinical trials, support CRO partnerships, and accelerate drug development pipelines.
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: How can smaller biotech firms leverage these technologies compared to large pharmaceutical companies?
Cowen: We’ve talked a lot about the big pharmaceutical companies, and it’s easy to see the scale and complexity they manage—dozens of clinical trial agreements (CTAs) going out for negotiation at any given time. Smaller biotech firms face a different challenge: they have fewer resources and lower volumes, so efficiency is critical.
One biotech we’re working with is preparing its first drug for market after completing Phase III. They use Conga to manage all of their Contract Lifecycle Management processes, and the platform will continue to support them into the commercialization phase. They currently have seven drugs in the pipeline, which they’re moving through quickly, so the ability to ramp up operations efficiently is key.
What’s helpful for smaller firms is that Conga is already used by large pharmaceutical companies, so they can leverage a proven, scalable solution without building processes from scratch. This allows them to operate efficiently while maintaining the ability to grow and adapt as their pipeline expands.
Partnerships are also critical. Many small biotechs deploy CROs to assist with trials, and Conga’s platform allows these partners to participate seamlessly where needed, supporting collaboration and accelerating study timelines.
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.
