This is what respondents will receive in Q1 2027: market panorama, real-world AI usage across BioPharma, MedTech and Diagnostics sales, adoption barriers, and 5 concrete levers not to fall behind.
Numbers below are fictional — they show the shape and depth of the final report.
180
respondents
BioPharma · MedTech · IVD · Tools
65%
use AI weekly or more
+38 pts vs. 2024
4.5 h
saved / week
estimated per sales rep
26%
have deployed an official tool
74% still individual usage
The gap between perceived lag and concrete action is the signature finding of this edition. Field reps fill the void with consumer tools (ChatGPT, Gemini) — 63% use them daily, with no internal framework or GxP guardrails.
% of respondents citing each pressure as "strong" or "very strong" (multiple answers).
Price / margin pressure
Hospital GPOs & centralized procurement
Direct e-commerce from manufacturers
Longer approval cycles (FDA, MDR, IVDR)
Shortage of scientific sales talent
Payer / value-based purchasing pressure
Across the full panel.
Several times a day
Every day
1 to 2 times a week
A few times a month
Never / blocked
Tasks already delegated to AI.
Responding to tenders / RFPs
Call summaries & CRM notes
Preparing customer visits (HCPs, hospitals)
Competitive & regulatory intelligence
Scientific research on an account
Drafting technical & clinical documentation
Configuration & quoting of complex systems
81%
of leaders
believe their teams are trained on AI
27%
of field reps
say they received structured training
54 pts
perception gap
the biggest gap measured in the study
Main barrier cited by respondents (one answer each).
Confidentiality / sensitive patient data
Regulatory constraints (FDA, MDR, IVDR, GxP)
Lack of team training
Tools poorly integrated with CRM / ERP
No clear strategy from leadership
Cultural resistance
Life Science commercial business splits into two very different logics — and AI plays a very different role in each. This is the frame we use to read the 2026 results.
Engine 1 — Efficiency
Consumables, reagents, catalog products. The sale flows through tenders, RFQs, framework agreements and one-stop distribution. The stake is productivity.
Engine 2 — Value Selling
Instrumentation, capital, software, complex scientific services. AI detects signals, the rep qualifies, then AI helps craft the high-value offer (ROI, business case, clinical value story).
What this changes for the org: two AI roles, two sales processes, two rep profiles — and therefore two training, coaching and compensation trajectories to run in parallel.
Frame an official, compliant AI usage
Move out of wild ChatGPT usage: one internal tool, a data & GxP charter, business-validated prompts. Without it, 100% of gains stay invisible and risky.
Industrialize tender & RFP responses
It's the #1 use case (58%) and the most profitable: an AI response engine connected to the catalog, memory of won tenders, measured win rate.
Reactivate the installed base with AI-driven cross-sell
1 rep out of 3 says their installed base is unused. It's the fastest consumables & service margin lever, activable in 90 days on a properly-fed CRM.
Train reps for the augmented scientific-selling posture
More tools does not create more sales. The Life Science program blends method + AI + field coaching, calibrated on the long cycle of capital and instrumentation.
Define 3 AI impact KPIs and track them monthly
Time saved, tender win rate, cross-sell rate on installed base. Without measurement, AI stays an IT topic, not a Commercial Leadership topic.
Full report — Q1 2027
7 minutes of questions and you get the full Life Science Study 2026 in preview: benchmark of your profile vs. the panel, focus on AI in Life Sciences, and an invitation to the live results webinar.
Take part in the studyStudy conducted by Qwantiq with Skeells.ai — data anonymized.