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AI in R&D: From Bench to Bedside—Improving Time to Market

AI Is Changing the Cost Equation for Biomedical Innovation and Universities are Ripe for Adoption


Universities are quietly becoming the world’s largest incubators of life sciences innovation. Nearly half of all FDA-approved drugs trace their origins to academic research, with U.S. institutions leading in patents, partnerships, and startup formation (Gardner, K., & Kinch, M, 2025).


Yet, while the research breakthroughs themselves are extraordinary, the path to market remains slow and expensive. Every new therapy or medical device emerging from a university lab must still cross a steep commercialization valley which spans years of clinical testing, documentation, and regulatory approval.


As costs climb and timelines stretch, the next frontier in translational research may not be a new molecule or biomaterial—it may be how we use AI to move ideas to market faster.


The Hidden Cost of Commercialization

Bringing a biomedical innovation to market is is a long relay through several high-cost stages.

  • Clinical Trials: Phase 3 trials can cost anywhere from $20 million to $100 million (Wouters et al, 2022). After that, teams spend another 6–12 months compiling and cleaning data for submission, involving 50–150 specialists and generating 300,000–500,000 pages of documentation (Synerg Biopharma, 2024).

  • Regulatory Submission: Every dataset must be reformatted into the FDA’s Common Technical Document (CTD) structure, following strict ICH standards for traceability and validation (LungEvity Foundation, 2024).

  • Medical Devices: Even simpler devices are not immune to complexity. A Class II 510(k) submission typically costs $30,000–$150,000 and takes 6–12 months (FDA, 2026; BlueGoat Cyber, 2025).

  • Manufacturing and Scale-Up: Transitioning to Good Manufacturing Practice (GMP) adds another 6–12 months and millions in validation and compliance costs. Each stage builds on the last—and each month of delay compounds investor risk and extends the path to patient impact.


AI at Work: Insights from How I AI

In a recent episode of How I AI, host Claire Vo interviewed Prerna Kaul, a product leader formerly at Moderna. Kaul showcased how she used Anthropic’s Claude to automate one of the most painful steps in pharmaceutical development: compiling an FDA submission.Her workflow auto-redacted patient data, summarized clinical trial results, structured documents into CTD format, and even exported them in FDA-ready XML—all within days (Vo, 2025). Tasks that once took dozens of people and six months of effort could now be completed by a single individual using an AI-powered pipeline. Beyond speed, the system tracked per-operation costs and compliance performance, showing how transparency and automation can coexist.


Calculating the ROI Impact


The FDA receives ~1,500 new INDs per year (Lapteva, L., & Pariser, A. R. , 2016). This means the total impact on submission drafting in medicines could be worth tens, if not hundreds of millions of dollars annually. Across the national ecosystem of biomedical ventures this could have massive impact in reducing the risk of investing into early stage technologies and reducing the cost of healthcare at large. But even at the individual level, organizations can reap benefits. Moderna, where Prerna Kaul lead AI Strategy, submits 3-6 FDA filings per year. This is modest and likely matched by submissions from spinouts and startups at research universities, such as the University of Florida.


For a university tech transfer office managing a mixed pipeline of 6-8 medical device and drug or biologics projects, the economics add up quickly. Each project will require multiple submissions and the total could consume $100,000 to over a $1 million in documentation, labor and consulting costs alone. If AI-assisted automation saves even 30%, that is tens to hundreds of thousands per project—and millions across the small portfolio. This is a conservative estimate.


The benefits extend beyond large pharmaceutical filings. For Class II device companies, trimming two to three months off the submission process could mean an earlier market launch, faster investor returns, and greater runway to scale. The ROI compounds when workflows are reused across multiple programs. The first project may take experimentation—but by the third or fourth, the cost curve starts to bend sharply downward.


Integrating AI Into R&D Strategy

Image from National Cancer Institute depicting scientists researching cancer biologics using lab techniques.

AI in biomedical research isn't just about productivity. For those who develop specialized internal solutions AI will be a strategic capability that accelerates time to market and reduces costly risks. Universities and R&D organizations that plan for it now will hold a competitive edge in commercialization over the next decade.


There are many possible applications: grant submissions, patent reviews, data analysis, experiment and trial pre-planning, quality control and design checks, and FDA submissions, just to name a few. Prerna's LinkedIn states that she developed and launched "750+ agents across regulated domains" at Moderna in one year. This suggests one individual placed in the right environment and culture open to the innovation can create tremendous impact within their organization.


Licensing and Ventures groups, biotech and medtech incubators and investment groups, innovation offices and institutes have an opportunity to give all their associated startups and licensed projects an advantage from the start. Government agencies like ARPA-H have already started funding AI based technologies to accelerate and improve clinical trials


Like any strategic project, they will need to start with their vision for what they want to achieve and an assessment of needs and workflows and great project management. However, they are in a great position to both adopt new technologies (as they are innovation centered) and to achieve massive returns on investment given their existing scale and quantity of projects.


Our mission at StratCraft is to help companies scale and thrive by combining technology, market strategy, and financing. We turn innovations into sustainable businesses. Learn more about us.


Click here to read more case studies on how AI is transforming healthcare & medicine.


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