Beyond Commodity: How AI Platforms Can Accelerate Biopharma Innovation

Last week, while I was visiting Boston, I had an enlightening conversation with a respected venture capitalist about the role of generative AI in clinical trials and medical writing. The VC made a compelling point - that standalone AI-powered medical writing apps for clinical trials are quickly becoming a commodity business. 

After reflecting on this, I find myself agreeing with the assessment. Over time, will these apps become the Powerpoint templates of tomorrow? Surely a more comprehensive approach is required. Then I read Morgan Cheatham's incredibly insightful paper supporting Bessemer's thesis on AI in healthcare and it confirmed a number of truths we have held close to our hearts at Ryght. Namely, a connected ecosystem is required to really solve the problems in clinical research and a platform is needed to abstract the solutions above the fray of ever-changing AI models and services.

The Commoditization of AI Writing Tools

It's true that we're seeing a proliferation of AI writing assistants tailored for clinical trial documentation. Many of these tools leverage large language models to help medical writers draft protocols, clinical study reports, and regulatory submissions more efficiently. As the technology becomes more widespread and accessible, individual writing apps risk becoming interchangeable commodities.

However, I believe this view overlooks the broader potential for AI to transform biopharma R&D processes beyond just document creation. The real value lies not in isolated writing tools, but in comprehensive platforms that can accelerate AI adoption and innovation across an organization's entire clinical development pipeline.

Moving Beyond Point Solutions to Platforms

At Ryght AI, we've recognized that biopharma companies need more than just standalone AI writing assistants. They need flexible platforms that allow them to:

1. Leverage pre-built AI applications for common use cases. We’ve already built over a dozen of these and they automate super manually-intensive tasks via integrations to common external and internal data sources and templates.

2. Rapidly prototype and deploy custom AI tools without extensive coding

3. Integrate AI capabilities into existing workflows and systems

4. Maintain control over their proprietary data and models

This is why we've evolved our offering beyond individual apps to a full AI acceleration platform for biopharma. Our approach combines ready-to-use applications with powerful no-code AI development tools and SDKs.

Empowering Citizen Data Scientists in Biopharma

One of the key advantages of our platform approach is that it empowers "citizen data scientists" within biopharma organizations. Using our no-code AI copilot builder, clinical teams can create custom AI assistants tailored to their specific therapeutic areas, study designs, or corporate processes - all without requiring deep technical expertise. These no code builders automate manual workflows and speed research using external data sources which we connect to via API such as Pubmed, CT.Gov and many others.

This democratization of AI development is crucial for driving innovation. It allows the subject matter experts who intimately understand clinical development challenges to directly shape AI solutions, rather than relying solely on centralized IT or data science teams.

Accelerating AI Initiatives Across the R&D Lifecycle

By providing a unified platform for AI development and deployment, we're helping biopharma companies accelerate their AI initiatives across the entire R&D lifecycle. Some examples of how our customers are leveraging the platform include:

- Automating the extraction of insights from historical trial data to research competitive landscapes and inform protocol design

- Processing thousands of trial protocols and finding relevant trials to get patients onto the appropriate therapy

- Creating AI assistants to support site feasibility assessments and patient recruitment

- Developing custom models to predict and mitigate potential safety signals

- Building AI-powered pipelines for clinical data management and analysis by non developers

The key is that these aren't isolated point solutions, but part of an integrated ecosystem that allows for rapid experimentation, iteration, and scaling of successful AI use cases.

Balancing Innovation and Compliance

Of course, operating in the highly regulated biopharma industry means that any AI platform must have robust governance and compliance features built-in. Our platform includes capabilities for model versioning, audit trails, and explainability to ensure that AI-assisted processes remain transparent and validated.

We've also designed the platform with data privacy and security as top priorities, allowing companies to keep sensitive clinical data within their own environments while still benefiting from advanced AI capabilities.

The Path Forward

As AI continues to mature, I believe we'll see a shift away from companies simply adopting individual AI writing tools towards embracing comprehensive AI acceleration platforms. These platforms will become a crucial competitive differentiator, allowing biopharma organizations to innovate faster, operate more efficiently, and ultimately bring life-changing therapies to patients sooner.

While standalone AI writing apps may indeed become commoditized, there's immense value to be unlocked by empowering biopharma companies to become AI innovators in their own right. By providing the tools, infrastructure, and expertise to accelerate AI adoption, we can help the industry realize the transformative potential of this technology across the entire drug development process.

The future of AI in biopharma isn't just about better writing - it's about reimagining how we approach clinical development in the age of artificial intelligence. And that's a future we're excited to help build.