How is AI used in pharma and life sciences?

The most documented AI use cases in pharma and life sciences are AI drafting of clinical study reports and regulatory documents, AI native platform for drug target discovery and molecule design and AI for clinical trial site selection and feasibility, across 46 public deployments by 37 organizations in the Blits.ai AI Use Case Library. 0% of public deployments in pharma and life sciences put the AI in front of customers directly, against 31% across the library. The rest support employees or run inside a back office process.

Data report

State of AI in pharma and life sciences

What 46 documented AI deployments by 37 organizations in pharma and life sciences say about who uses the AI, how far it acts on its own and how often the result is proven, set against the whole library.

Computed live from the library on 12 November 2026. By Len Debets.

Deployments

37 organizations in 13 countries

Face the customer

The AI works with customers directly; 31% across the library

24%

Agentic

The AI completes work itself; 63% across the library

46%

Disclose an outcome

A measurable result is public; 61% across the library

Who works with the AI

0% of public deployments in pharma and life sciences put the AI in front of customers directly, against 31% across the library. The rest support employees or run inside a back office process.

Public deployments by who uses the AI

Employee facing
36
Back office
10

How far the AI acts on its own

24% of public deployments in pharma and life sciences belong to use cases where the AI completes work itself (a supervised or autonomous agent, or an agentic workflow), against 63% across the library.

Public deployments by autonomy level

Copilot
22
Assist
15
Supervised agent
10

Where the deployments come from

48% of the public deployments in pharma and life sciences are by organizations in North America. 59% date from 2025 or 2026, against 57% across the library.

Public deployments by region

North America
22
Europe
18
Asia Pacific
4
Global
2

How often the result is proven

46% of public deployments in pharma and life sciences disclose a measurable outcome, against 61% across the library. Of the 21 reported metrics, 10 came from a vendor rather than from the organization itself.

Who made the claim (reported metrics)

Organization
11
Vendor
10

The most documented use cases in pharma and life sciences

Ranked by the number of pharma and life sciences organizations with a public deployment.

AI drafting of clinical study reports and regulatory documents9 organizations
AI native platform for drug target discovery and molecule design9 organizations
AI for clinical trial site selection and feasibility7 organizations
AI for pharmacovigilance adverse event case intake6 organizations
AI review of batch records and process data for quality deviations in pharma manufacturing4 organizations
AI agent for travel and expense report audit2 organizations
Governed text to SQL analytics assistant2 organizations
AI agent for complaints recognition, investigation and response1 organization
AI agent for contract obligation tracking and compliance1 organization
AI avatar and translated video for corporate communications and marketing1 organization

The benchmarks with the most evidence

KPIs with at least three pooled public values from pharma and life sciences organizations.

Benchmarks in pharma and life sciences
KPI	Median	Reported range	Data points	Claimed by
Cycle time reduction	53%	10% to 60%	5	4 organization, 1 vendor
Handling time reduction	85%	23.3% to 90%	3	3 organization

Frequently asked questions

How is AI used in pharma and life sciences?: The most documented AI use cases in pharma and life sciences are AI drafting of clinical study reports and regulatory documents, AI native platform for drug target discovery and molecule design and AI for clinical trial site selection and feasibility, across 46 public deployments by 37 organizations in the Blits.ai AI Use Case Library. 0% of public deployments in pharma and life sciences put the AI in front of customers directly, against 31% across the library. The rest support employees or run inside a back office process.
How many AI deployments in pharma and life sciences use AI agents?: 24% of public deployments in pharma and life sciences belong to use cases where the AI completes work itself (a supervised or autonomous agent, or an agentic workflow), against 63% across the library.
How often do pharma and life sciences organizations prove the results of AI?: 46% of public deployments in pharma and life sciences disclose a measurable outcome, against 61% across the library. Of the 21 reported metrics, 10 came from a vendor rather than from the organization itself.

About this report

This report counts every public deployment by an organization in pharma and life sciences, whatever the use case, and sets it against all 1921 public deployments in the library. The industry hub counts only the use cases tagged with this industry, so its totals can be lower. Every figure is computed with the rules on the methodology page, from the open data (CC BY 4.0). The sample reflects what organizations choose to publish, which skews toward large organizations and successful projects.