On April 15, I had the privilege of attending the AI Healthcare Leadership Summit, an invite-only event hosted by Bessemer Venture Partners, Bain & Company, and Amazon Web Services. This summit convened many of the industry’s leading minds — CEOs, founders, clinicians, investors, and technologists — who are actively shaping the trajectory of artificial intelligence in life sciences, clinical medicine, and healthcare financing. One of the most valuable components of the summit was the unveiling of the Healthcare AI Adoption Index, a data-rich analysis of how more than 400 healthcare organizations are approaching AI adoption. The findings reflect what many of us are seeing firsthand: AI adoption is not only accelerating, it’s becoming central to enterprise strategy.
As the founder of Opus Strategy, a firm that works with investors and large companies in the pharma industry, this event offered essential insights into how AI is evolving, particularly in the life sciences, and what healthcare leaders and innovators must prioritize to stay ahead.
AI Applications in Life Sciences
Only 15% of current AI projects are categorized as vertical applications, a fact that leaves immense whitespace for startups and healthcare innovators to co-create tools tailored for a range of healthcare applications.
Within pharma specifically, AI is being utilized in preclinical applications such as molecule identification and indication selection, along with clinical applications like protocol design and even New Drug Application (NDA) submission. The technology also is being leveraged in pharma for marketing and sales.
Despite the current and potential applications for AI, trust in the technology is not quite robust. And trust will be paramount to capitalizing on AI’s potential. Additionally, outcomes continue to drive procurement decisions, creating fertile ground for strategic partnerships built on transparency, data ownership, and performance.
AI Co-Development within the Pharma Industry
While much of the early focus on healthcare AI revolved around startups, what’s emerging now is a more complex and collaborative model. Many of the most promising AI applications are being developed not just by health tech startups, but are being co-developed by internal provider teams working closely with large technology firms and cloud providers.
According to the report released by Bessemer, 57% of pharma executives believe AI will help drive new therapies over the next decade. With security concerns, costly integrations, and the need for AI-ready data (especially in pharma), executives do believe collaboration will be key. Indeed, many believe adoption risk is mitigated when collaboration exists between traditional industry players and innovators. This opinion underscores a key strategic shift: AI is no longer seen as a siloed. It’s now deeply embedded in core corporate strategy.
Despite this momentum, adoption remains somewhat limited. Only about 30% of AI pilots make it beyond the proof-of-concept stage, and within pharma specifically, fewer than 24% of innovators have reached that milestone. This lag, especially when compared to adoption rates in the payer and provider sectors, highlights the urgent need for a more agile, “test and learn” approach to drive real-world implementation.
AI Budgets and IT Spend
The Bessemer report demonstrates AI has moved beyond the experimental phase. It is now a core element of competitive healthcare strategy with 60% of healthcare executives allocating more resources to AI than to traditional IT at a time when budget authority is increasingly consolidated at the C-Suite level.
This shift marks a broader institutional commitment: 65% of AI initiatives are now funded through centralized corporate budgets, while the remaining 35% are supported at the departmental level. In the past, IT budgets were the primary obstacle to advancing AI. Now that’s not the case. Most respondents to the Bessemer survey indicated budget constraints are no longer a primary obstacle to scaling AI from pilot to production, signaling a shared understanding that AI adoption is a strategic imperative, not a discretionary expense.
That said, executives do have a preference with whom they work.
While big tech companies are often seen as leaders in generative AI, nearly half (48%) of executives say they prefer to partner with startups, especially those that bring flexibility and a collaborative mindset. Moreover, 64% of executives are open to co-developing AI solutions with early-stage companies, particularly when those partnerships offer clear, measurable value and demonstrate alignment with clinical or operational needs.
Once again, being open to collaboration is key.
Pioneers Shaping the Future
Throughout the summit, participants heard from pioneers who are already shaping this future, including former FDA deputy commissioner Janet Woodcock. The message was consistent: success in healthcare AI will be defined not only by technical sophistication, but by deep integration and collaboration, shared accountability, and measurable value.
What excites me most is the collective momentum. Healthcare organizations are no longer asking if AI should be adopted — they’re defining how it can be implemented responsibly, efficiently, and equitably. As someone who works closely with clients on strategy, market insights, and innovation in the life sciences industry, this summit reaffirmed the importance of staying informed and being engaged within the ecosystem. We need to work collaboratively to turn promise into progress.
I’m grateful to the organizers for creating such an insightful and inspiring environment, and I look forward to continuing this important work with many of the brilliant minds I met last week.
For those interested, the full Healthcare AI Adoption Index is well worth the read: https://www.bvp.com/atlas/the-healthcare-ai-adoption-index