From Core Facility to Collaborative Partner: Reimagining Institutional Shared Resources as CRO-Model Research Partners Enhanced by Agentic AI

Academic biomedical research institutions are confronting a converging challenge: tightening federal research budgets and growing demands for faster and more reproducible science,1 and increasing pressure to maximize value derived from every research dollar. Traditional shared resources have enabled access to sophisticated technologies and expertise as transactional, fee-for-service facilities that prioritize throughput and cost recovery over scientific outcomes. We propose a shift toward a Contract Research Organization (CRO)-inspired operating model in which shared resources function as integrated research partners, supported by agentic artificial intelligence (AI) systems that enhance project management, experimental execution, data analysis, and scientific decision-making. This Perspective outlines the limitations of the current paradigm, identifies lessons from commercial CROs, explores the opportunities for agentic AI across major research domains, and presents a roadmap for institutional implementation. We argue that institutions that embrace this transformation will be better positioned to increase productivity, improve reproducibility, accelerate discovery, and strengthen research competitiveness in an increasingly resource-constrained environment.

Authors

Institutions

Publication Details

Journal
Journal of Biomolecular Techniques JBT
Published
2026-09-11
DOI
https://doi.org/10.7171/001c.167753
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From Core Facility to Collaborative Partner: Reimagining Institutional Shared Resources as CRO-Model Research Partners Enhanced by Agentic AI

Jay W. Fox, Nicholas P. Ambulos
Journal of Biomolecular Techniques JBT
Scientific Computing and Data Management
article

From Core Facility to Collaborative Partner: Reimagining Institutional Shared Resources as CRO-Model Research Partners Enhanced by Agentic AI

Jay W. Fox, Nicholas P. Ambulos
article en

Abstract

Academic biomedical research institutions are confronting a converging challenge: tightening federal research budgets and growing demands for faster and more reproducible science,1 and increasing pressure to maximize value derived from every research dollar. Traditional shared resources have enabled access to sophisticated technologies and expertise as transactional, fee-for-service facilities that prioritize throughput and cost recovery over scientific outcomes. We propose a shift toward a Contract Research Organization (CRO)-inspired operating model in which shared resources function as integrated research partners, supported by agentic artificial intelligence (AI) systems that enhance project management, experimental execution, data analysis, and scientific decision-making. This Perspective outlines the limitations of the current paradigm, identifies lessons from commercial CROs, explores the opportunities for agentic AI across major research domains, and presents a roadmap for institutional implementation. We argue that institutions that embrace this transformation will be better positioned to increase productivity, improve reproducibility, accelerate discovery, and strengthen research competitiveness in an increasingly resource-constrained environment.

Journal of Biomolecular Techniques JBTVol. 37(3)
University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Virginia (US)
Openalex Percentile: Top 4%
Scientific Computing and Data Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.