Scientists collaborating with AI in a futuristic laboratory

Decoding AI Adoption in Research: What's Holding Science Back?

Kai Mendoza in Tech & Innovation August 2025 • 4 min read.

"A deep dive into the drivers and barriers shaping the integration of artificial intelligence in scientific research, revealing key insights for researchers and policymakers."

Artificial intelligence (AI) and machine learning are revolutionizing scientific discovery, offering unprecedented tools for tackling complex problems. From predicting protein structures to mapping insect brains, AI's potential seems limitless. Yet, the integration of AI in science is not a straightforward process. Despite the documented impact of AI, a significant gap persists in understanding how and why scientists adopt—or resist—these technologies.

A recent survey highlighted obstacles preventing scientists from using AI more extensively: lack of skills, training resources, funding, computing resources, and data. These challenges underscore a critical need to understand the dynamics of AI adoption within the scientific community. Is it merely about access to technology, or are there deeper, more nuanced factors at play?

This article delves into a comprehensive study exploring the drivers and barriers to AI adoption in scientific research. By examining the 'scientific and technical human capital' (STHC) of scientists—their knowledge, skills, networks, and institutional environments—we aim to uncover the key elements that facilitate or hinder the integration of AI into scientific practices.

The Key Ingredients for AI Integration: Unpacking Scientific and Technical Human Capital

Scientists collaborating with AI in a futuristic laboratory

The theory of scientific and technical human capital (STHC) suggests that a scientist's behavior is influenced not only by their own knowledge and skills but also by the resources available through their networks and institutions. This framework helps us understand the multifaceted nature of AI adoption, moving beyond a simple 'access to tools' narrative.

Our analysis focuses on three critical dimensions of STHC:

Personal Knowledge and Skills: A scientist's pre-existing knowledge, skills, and appetite for experimentation.
Network Expertise: The knowledge and expertise of their research collaborators.
Institutional Setting: The resources and culture of the institution where the researcher is based.

By examining these dimensions, we can identify the specific factors that enable scientists to effectively integrate AI into their research workflows.

Navigating the AI Frontier: Key Takeaways and Implications

Our research reveals several key insights into AI adoption in science:<ul><li>Social Connections Matter: Collaboration with computer scientists and AI experts significantly increases the likelihood of AI adoption.</li><li>Institutional Support is Crucial: Scientists at institutions specializing in AI research are more likely to integrate AI into their work.</li><li>Young Researchers Lead the Way: Early-career researchers play a pivotal role in driving AI adoption, often challenging established norms.</li><li>Computing Resources Aren't Always Key: While important, access to advanced computing infrastructure is not always the primary driver of AI adoption, except in specific fields like medicine and chemistry.</li></ul>

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2312.09843,

Title: Drivers And Barriers Of Ai Adoption And Use In Scientific Research

Subject: cs.cy econ.gn q-fin.ec

Authors: Stefano Bianchini, Moritz Müller, Pierre Pelletier

Published: 15-12-2023

Everything You Need To Know

What is 'scientific and technical human capital' (STHC), and why is it important for understanding AI adoption in scientific research?

'Scientific and technical human capital' (STHC) refers to a scientist's collective knowledge, skills, and access to resources through their networks and institutional environments. STHC moves beyond simple access to tools narrative and acknowledges that a scientist's behavior is influenced not only by their own abilities but also by the expertise available through collaborators and the support of their institution. Examining STHC helps to understand the multifaceted nature of AI adoption.

Besides computing resources, what are some of the main obstacles preventing scientists from adopting AI more extensively in their research?

Several obstacles prevent scientists from adopting AI more extensively. These include a lack of skills and adequate training resources, insufficient funding, and a scarcity of relevant data. Interestingly, while computing resources are important, especially in fields like medicine and chemistry, they are not always the primary driver of AI adoption across all scientific disciplines. Collaboration and institutional support are key.

How do social connections and collaborations influence a scientist's likelihood of adopting AI in their research?

Social connections, particularly collaborations with computer scientists and AI experts, significantly increase the likelihood of AI adoption. This highlights the importance of interdisciplinary collaboration in fostering the integration of AI into scientific research. By working with experts in AI, scientists can gain access to knowledge, skills, and resources that they might not otherwise possess, enabling them to overcome some of the barriers to AI adoption.

What role do institutional resources and the research environment play in determining whether scientists integrate AI into their work?

Institutional resources and the research environment are crucial determinants of AI adoption. Scientists at institutions specializing in AI research are more likely to integrate AI into their work, because these institutions often provide access to advanced computing infrastructure, training programs, and a supportive culture that encourages experimentation with new technologies. The presence of these resources and a culture of innovation can significantly lower the barriers to AI adoption for scientists.

How are young researchers shaping the landscape of AI adoption in science, and why are they considered pivotal in this technological shift?

Young researchers are driving AI adoption and play a pivotal role in this technological shift. Early-career researchers are often more open to experimenting with new technologies and challenging established norms. They are more likely to embrace AI tools and techniques in their research, and in turn, they help to disseminate knowledge and best practices to their peers and senior colleagues. This creates a ripple effect that accelerates the integration of AI across the scientific community.