---
id: "lib-2026-05-00-agentic-ai-in-life-sciences"
type: "library"
title: "Agentic AI in life sciences"
lang: "EN"
org: "Capgemini"
source_type: "Report"
date: "2026-05-01"
week: "2026-W18"
url: "https://nosov.ai/library/lib-2026-05-00-agentic-ai-in-life-sciences/"
original_url: "https://www.capgemini.com/wp-content/uploads/2026/05/PoV_Agentic-AI-in-LS_May-2026.pdf"
tier: "C"
tier_name: "Weak signal"
tier_reason: "A Capgemini point-of-view paper that markets its own services; its one original figure (a scoring of 270 processes) comes without a published method, and most other numbers are recycled from MIT, McKinsey and Capgemini surveys."
summary_basis: "full-text"
original_minutes: "15"
tags: ["agentic ai", "life sciences", "operations"]
---

# Agentic AI in life sciences

Tier C (Weak signal): A Capgemini point-of-view paper that markets its own services; its one original figure (a scoring of 270 processes) comes without a published method, and most other numbers are recycled from MIT, McKinsey and Capgemini surveys.

Summary based on the full text.

## In short

The first paper (May 2026) in a Capgemini series on "rearchitecting life sciences for agentic AI", by Thorsten Rall, Parvin Moyassari and Neeraj Goel Mittal with a long list of Capgemini contributors. It argues that pharma and MedTech AI spending rises while value stalls, and that the fix is redesigning end-to-end workflows on a shared data and governance layer instead of funding isolated use cases. Its own analysis rates about 84% of 270 life sciences processes as automatable or augmentable by agents. The paper is a positioning document rather than a study.

## Key numbers

- ~84%: share of 270 pharma and MedTech processes Capgemini rates as 'agentifiable'
- 8%: life sciences firms actively implementing an enterprise AI strategy, against 71% defining one
- 63%: biopharma R&D leaders who lack the data maturity to realise AI's impact on R&D

## What it reports

- From a Capgemini Research Institute survey of biopharma R&D leaders: 86% expect AI to reshape R&D but 63% lack the data maturity; 76% anticipate new ways of working but fewer than half feel ready; 71% are defining enterprise AI strategies, 8% are implementing them.
- Cites the 2025 MIT "GenAI Divide" report: $35 to $40 billion invested in internal AI initiatives, 95% of organisations seeing near-zero returns.
- Industry context: $200 to $350 billion in annual pharma revenue exposed to expiring exclusivities; cost per molecule above $2 billion; MedTech development cycles of three to seven years.
- Capgemini's process matrix: about 28% of workflows "start here" (low complexity, high impact), about 26% strategic, about 17% opportunistic, about 12% deprioritised, about 16% not agentifiable.
- Not agentifiable examples: first-in-human trial design, Qualified Person batch sign-off, on-site GxP audits, FDA advisory presentations.
- Quotes McKinsey's estimate that 75-85% of life sciences workflows contain agent-ready tasks, freeing 25-40% of workloads.
- Names five capabilities: workflow redesign, AI-ready data and semantic layer, agent orchestration, a governance control panel, and "human-AI chemistry".
- Expects headcount to fall gradually in specific functions as agents absorb junior-to-mid-level cognitive tasks, with new roles such as agent orchestrators and clinical AI validators.

## How they got it

Capgemini scored 270 pharma and MedTech processes on business value, cross-functional collaboration, technical complexity and effort, and plotted them on an impact-complexity grid. No scoring rubric, rater details or process list are published. Survey figures come from separate Capgemini Research Institute reports.

## Limits

- The firm sells the transformation services it recommends; no client results or measured outcomes are reported.
- The MIT 95% figure is widely disputed and is cited without caveat.
- Use cases (pre-call briefing agents, CAPA agents) are described as possibilities, not deployments with numbers.

## Why it matters for companies

Read it as a checklist for regulated industries rather than evidence. Its useful parts are the list of decisions that stay human (sign-offs, audits, first-in-human design) and the advice to build only the parts of the data layer that a chosen high-value workflow needs, instead of a "fix all data first" programme. The plan to tell staff early about gradual headcount effects in junior-to-mid roles is a point HR and works councils will need to address in any regulated firm.

Original: https://www.capgemini.com/wp-content/uploads/2026/05/PoV_Agentic-AI-in-LS_May-2026.pdf