Agentic AI adoption in life sciences is in its early stages, with potential in drug discovery, clinical trials, and regulatory compliance.
Finding 1 of 5
PeMa Quadrant · 1st edition · Published October 3, 2025
Agentic AI Use Cases in Life Sciences
27 vendors evaluated on market penetration and technology maturity. 11 qualify as Leaders.
27
Vendors evaluated
11
Leaders
First edition of this PeMa Quadrant. Next edition: RFI closes 16 Nov 2026 →
11 Leaders · 8 Seasoned · 1 Growth · 7 Challengers
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Where all 27 vendors landed
Hover a dot for the vendor, click to open its passport.
Technology maturity →
SeasonedLeadersChallengersGrowth
Market penetration →
2025
Positions from 2025 are read from the chart AIM published that year. Source →
What the report finds
5 shifts buyers should know
Agentic AI is anticipated to drive hyper-personalized drug development and enable real-time optimization of drug discovery and clinical trials.
Life science companies face challenges in adopting Agentic AI, including data privacy concerns and ensuring regulatory compliance.
Agentic AI is transforming life sciences by accelerating drug discovery, clinical trials, and regulatory workflows.
Multi-agent systems automate reporting, ensure data integrity, and streamline compliance documentation in life sciences.
Vendors evaluated
All 27 vendors, and how they moved
| Vendor | Placement | Headquarters | Vendor passport |
|---|---|---|---|
| Blend360 | Leaders | — | Passport → |
| Centific | Leaders | — | Passport → |
| EY Global Delivery Services LLP | Leaders | — | Passport → |
| Fractal Analytics | Leaders | — | Passport → |
| Fujitsu | Leaders | — | Passport → |
| LatentView Analytics | Leaders | — | Passport → |
| MathCo | Leaders | — | Passport → |
| Straive | Leaders | — | Passport → |
| Tiger Analytics | Leaders | — | Passport → |
| USEReady | Leaders | — | Passport → |
| WNS Global Services | Leaders | — | Passport → |
| Aays | Seasoned | — | Passport → |
| C5i | Seasoned | — | Passport → |
| Dentsu | Seasoned | — | Passport → |
| Evalueserve | Seasoned | — | Passport → |
| InfoCepts | Seasoned | — | Passport → |
| JK Tech | Seasoned | — | Passport → |
| SG Analytics | Seasoned | — | — |
| StratLytics | Seasoned | — | Passport → |
| Indus Net Technologies | Growth | — | Passport → |
| Akaike Technologies | Challengers | — | Passport → |
| Artivatic | Challengers | — | — |
| Chetu | Challengers | — | Passport → |
| Intelekt AI | Challengers | — | — |
| Konverge AI | Challengers | — | Passport → |
| Senzcraft | Challengers | — | — |
| Think360.ai | Challengers | — | Passport → |
Blend360Leaders
CentificLeaders
EY Global Delivery Services LLP Leaders
Fractal AnalyticsLeaders
FujitsuLeaders
LatentView AnalyticsLeaders
MathCoLeaders
StraiveLeaders
Tiger AnalyticsLeaders
USEReadyLeaders
WNS Global ServicesLeaders
AaysSeasoned
C5iSeasoned
DentsuSeasoned
EvalueserveSeasoned
InfoCeptsSeasoned
JK TechSeasoned
SG AnalyticsSeasoned
StratLyticsSeasoned
Indus Net TechnologiesGrowth
Akaike Technologies Challengers
ArtivaticChallengers
ChetuChallengers
Intelekt AIChallengers
Konverge AIChallengers
SenzcraftChallengers
Think360.aiChallengers
Showing 10 of 27 ·
Abstract
The report on Agentic AI use cases in life sciences explores the transformative potential of autonomous AI systems in the sector.
Agentic AI, characterized by its ability to operate independently and adapt to environments without constant human intervention, is being increasingly adopted across the pharmaceutical value chain. Key applications include drug discovery, clinical trials, pharmacovigilance, and regulatory compliance. The report highlights that while the adoption of Agentic AI is still in its early stages, it shows promise in enhancing efficiency, decision-making, and operational agility. Leading pharmaceutical companies are exploring scalable AI solutions to improve drug discovery processes, optimize clinical trials, and ensure regulatory compliance. The report also discusses the challenges faced by life sciences companies in implementing Agentic AI, such as data privacy concerns, integration of heterogeneous datasets, and maintaining transparency in AI-driven decisions. Overall, the adoption of Agentic AI is poised to transform how pharmaceutical companies innovate, operate, and deliver therapies, positioning AI as a crucial enabler of next-generation life sciences innovation.
Methodology
How vendors were scored
The research was conducted by evaluating Agentic AI use cases across the life sciences value chain.
X axis
Market penetration
- Delivery scale
- Financial health
- Customer mindshare
Y axis
Technology maturity
- Technical innovation
- Value realisation
- DataOps depth
The research was conducted by evaluating Agentic AI use cases across the life sciences value chain. The methodology involved analyzing data from leading pharmaceutical companies and service providers to assess the maturity and potential of Agentic AI applications. The evaluation framework considered parameters such as complexity, scalability, need for autonomy, and regulatory compliance. Case studies and industry trends were examined to provide insights into the current and future impact of Agentic AI in life sciences.
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Request subscriber accessFrequently asked questions
The PeMa (Penetration and Maturity) Quadrant is AIM Research's 2x2 vendor evaluation framework. It measures each provider's market penetration (clients, deal sizes, geographic spread) and technology maturity (capabilities, platforms, partnerships), and places them as Leaders, Seasoned Vendors, Growth Vendors or Challengers.
Any enterprise technology vendor active in the relevant market category can request to participate. AIM Research issues an open RFI for each study and invites qualifying vendors via the Research Calendar.
Vendors are evaluated through a structured RFI process backed by primary research, including vendor briefings, customer references and product evaluations. They are scored across 40+ weighted criteria covering product capabilities, strategy and market impact, and the weighted scores are mapped to one of the four quadrant positions.
The date of the next edition has not been announced yet. Upcoming studies and their RFI windows are listed on the AIM Research Calendar.
Agentic AI refers to a class of artificial intelligence systems designed to operate autonomously, make decisions, and adapt to environments without constant human intervention. Unlike traditional AI, Agentic systems execute complex workflows, analyze large datasets, and optimize processes in real time.
In drug discovery, Agentic AI is used for target identification, compound screening, and mechanism modeling. It enables autonomous research agents to conduct and optimize scientific research, and AI-enabled solutions tailor treatments to individual patient profiles, accelerating the drug discovery process.
Life science companies face challenges such as data privacy and security concerns, integrating heterogeneous datasets, ensuring regulatory compliance, maintaining transparency in AI-driven decisions, and balancing human oversight with automation. High computational costs and model interpretability also pose challenges.