Latest
|
Macro Economy
Latest
|
Consumer Finance
AI
|
Latest LLMs
CX/CS
|
Fintech
Latest
|
AI Infrastructure
Enterprise
|
ROI of AI
AI
|
Ethics & Safety
Latest
|
Politics & Policy
AI
|
Enterprise AI
AI
|
Big AI
Latest
|
Consumer Banking
Latest
|
Fintech Funding
AI
|
AI in Fintech
CX/CS
|
Fintech
AI
|
Health Tech
AI
|
AI Governance
Latest
|
LLMs
Latest
|
Fintech
AI
|
Open Source
AI
|
AI Security
Enterprise
|
Cloud Security
Latest
|
Macro Economy
Enterprise
|
Enterprise Solutions
AI
|
GRC
AI
|
AII Adoption
AI
|
AI Ethics
AI
|
Healthtech
CX/CS
|
AI in CX
AI
|
Quantum Computing
AI
|
Cybersecurity
Latest
|
Healthtech
CX/CS
|
AI Adoption
AI
|
AI
AI
|
Safety and Compliance
Latest
|
Big Tech
AI
|
Consumer Tech
AI
|
AI Ethics and Risks
CX/CS
|
AI
Enterprise
|
Data and Privacy
Latest
|
LLMs
Latest
|
Banking and Blockchain
AI
|
Healthtech
Enterprise
|
AI in the Enterprise
AI
|
AI Risk and Compliance
AI
|
AI Arms Race
Enterprise
|
AI
Latest
|
LLMs
CX/CS
|
Compliance
CX/CS
|
Great CX
CX/CS
|
CS in Blockchain
AI
|
AI News
Enterprise
|
AI
|
CX/CS
|
CX/CS
|
AI
|
CX/CS
|
AI
|
AI
|
Enterprise
|
AI
|
CX/CS
|
CX/CS
|
Enterprise
|
Enterprise
|
Pharma’s biggest AI bets showing huge potential, but slow progress
.jpg)
⋅
April 10, 2025

Key Points
Pharmaceutical companies are investing heavily in AI to accelerate clinical trials, research, and process optimization.
Despite AI's promise, the technology is still in a trial phase, with no AI-based drugs approved yet.
Harini Gopalakrishnan, ex-Global CTO of Life Sciences at Snowflake, highlights AI's potential to halve research timelines from 12 to 6 years, and remains optimistic about where the tech is headed.
It’s nice to see that AI has actually made a real impact—not just hype or improving writing and content, but addressing a much-needed problem in science.
Harini Gopalakrishnan
ex-Global CTO of Life Sciences | Snowflake
Pharmaceutical companies are betting big on AI, hoping it will transform consumer health through more efficient clinical trials, research, and process optimization. But AI's progress is far from linear. While the relatively new tech is making strides in some areas, it remains stuck in others, often leaving the industry ‘two steps forward, one step back’.
To understand where AI is gaining traction, we spoke with Harini Gopalakrishnan, ex-Global CTO of Life Sciences at Snowflake. With a wealth of experience across AWS, Sanofi, and Cognizant, and a history of groundbreaking work in AI platforms for life sciences, Gopalakrishnan is a leading voice in the intersection of big data and AI in pharma.
Halving timelines: "AI is definitely making an impact," Gopalakrishnan begins. "The transformation in research is much more pronounced because you can go from 12 years to 6 years. So, the value chain and time to value are much bigger if you solve that problem." AI, she explains, is accelerating the pace of discovery by reducing timelines and making research more efficient, an area where the industry is already seeing some disruption.
Needle in a haystack: However, she cautions, AI’s journey in pharma is far from simple. "It’s also like finding a needle in a haystack," she says. "There are around 10-12 AI-based drugs in clinical trials, but none of them have been approved yet. It’s still in a try-and-test phase." Gopalakrishnan emphasizes that, while AI holds promise, it is not yet poised to overhaul clinical trials entirely. "It's not like we are going to reduce or replace clinical trials completely. It's more about optimizing for a better success story. The ROI is probably bigger in terms of cash, but it doesn't necessarily imply a faster time to market. It's about being more precise in how you run the trials," she explains.
The ROI is probably bigger in terms of cash, but it doesn't necessarily imply a faster time to market. It's about being more precise in how you run the trials.
Harini Gopalakrishnan
ex-Global CTO of Life Sciences | Snowflake
Two areas for improvement: According to Gopalakrishnan, the opportunities for AI in pharma are clear in two distinct areas: "The first is in early-stage research, where the focus is on accelerating the time to market by quickly identifying new drugs, molecules, or modalities. AI can help reduce these timelines, potentially cutting them in half, from research all the way to the first patient. The second area is when a drug is already in clinical trials or has patients involved. Here, AI can help accelerate the process, ensuring faster regulatory approvals and a quicker path to market."
Where we’re headed: Gopalakrishnan remains optimistic about the future. She points to the 2024 Nobel Prize in Chemistry, awarded for generative AI breakthroughs in life sciences, as a sign that AI is not just a buzzword. "It’s nice to see that AI has actually made a real impact—not just hype or improving writing and content, but addressing a much-needed problem in science," she says. "For me, the next step is seeing how it translates to real-world value. I’d love to see how it helps in finding the next drug or molecule, like finding that needle in a haystack."