From AI deployment to enterprise value.
THE SIGNAL
AI is no longer a distant possibility in life sciences. It's already becoming part of how scientific and operational work gets done.
NVIDIA's 2026 State of AI in Healthcare and Life Sciences survey reports widespread AI adoption across the industry, with drug discovery and development emerging as significant areas of investment and reported value. Benchling's 2026 Biotech AI Report offers another perspective: AI is increasingly embedded in R&D activities, from scientific literature review to protein analysis.
These aren't hypothetical applications.
They're examples of AI moving into everyday work.
And that's progress worth recognizing.
But it raises a more consequential question:
What happens after adoption?
WHY IT MATTERS
For several years, much of the enterprise AI conversation has focused on experimentation.
How many pilots have we launched?
How many employees are using AI?
How many use cases have we identified?
How much have we invested?
Those questions were useful when organizations were discovering what AI could do.
But they're insufficient for understanding whether AI is creating sustainable enterprise value. A successful scientific application can accelerate a workflow without transforming the broader organization.
An AI assistant can improve individual productivity without changing how decisions are made.
A promising pilot can demonstrate technical capability without becoming an operational capability.
Adoption is evidence of participation. Impact is evidence of transformation. The distinction matters because the next stage of AI investment will require leaders to explain not simply what they've deployed, but what has measurably improved.
THE CURBSIDE VIEW
At RISE, we believe the next meaningful divide in enterprise AI will be between organizations that have adopted AI and organizations that have learned how to operationalize it.
The difference isn't necessarily the sophistication of their technology.
It's their ability to connect AI capabilities to business priorities, decisions, workflows, and accountability.
Consider three levels of progress.
LEVEL 1 - DEPLOYMENT The organization has AI tools, licenses, platforms, and pilots. People are experimenting, and use cases are expanding.
LEVEL 2 - ADOPTION
Employees are using AI consistently. Workflows begin to change. Teams develop confidence, and practical applications become more common.
LEVEL 3 - ENTERPRISE IMPACT
AI produces measurable improvements in decision quality, cycle time, scientific productivity, commercial effectiveness, customer engagement, or operating performance.
The first two levels matter. But the third is where transformation becomes meaningful.
And moving between those levels requires more than another technology investment.
It requires leadership, governance, workforce readiness, operational integration, and a clear definition of success.
THE MOVE
As organizations prepare their 2027 AI priorities, we'd encourage executive teams to change the conversation.
Instead of asking only how much AI has been adopted, ask:
1. What decisions are better because of AI?
Are we making more informed, timely, or consistent decisions?
2. What workflows have fundamentally improved?
Have we reduced friction, accelerated execution, or eliminated unnecessary work?
3. Where is value measurable?
Can we identify improvements in productivity, quality, cost, speed, or commercial outcomes?
4. Who owns the results?
Is accountability assigned to business leaders, or does AI remain primarily a technology initiative?
5. What can scale?
Are we building repeatable enterprise capabilities, or accumulating disconnected successes?
These questions shift the conversation from AI activity to organizational performance.
And they are central to the research we're conducting through The RISE Index™ 2027. The Index explores how life sciences organizations are moving from AI ambition and experimentation toward adoption, operational integration, and measurable business impact.
We're particularly interested in perspectives across commercial, medical, digital, technology, and enterprise leadership.
Because the future of AI in life sciences won't be determined solely by what the technology can do.
It will be determined by what organizations can accomplish with it.
ONE QUESTION TO TAKE WITH YOU
If your organization stopped counting AI deployments tomorrow, what evidence would remain that AI is making the business better?
That's the conversation worth having.
HELP SHAPE THE RISE INDEX™ 2027
We're inviting life sciences leaders to contribute their perspectives to our enterprise AI adoption research.
The survey takes approximately 8–10 minutes and explores the capabilities, barriers, and organizational conditions shaping AI transformation.
Participate in the RISE Index™ 2027:
https://therisegroup.ai/research/enterprise-ai-adoption-2027 Your perspective can help inform a more practical understanding of how AI is changing life sciences organizations.
SOURCES & FURTHER READING
NVIDIA — State of AI in Healthcare and Life Sciences (2026) https://blogs.nvidia.com/blog/ai-in-healthcare-survey-2026/
Benchling — 2026 Biotech AI Report https://www.benchling.com/biotech-ai-report-2026
RISE | GTM Strategy. Business Development. Commercial Excellence. AI Transformation. AI Powered. Human Led.
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