Generative AI Uses and Risks for Knowledge Workers in a Science Organization

TL;DR

This study examines how knowledge workers in a science organization are adopting generative AI tools, their use cases, and concerns, providing insights for responsible AI integration in scientific settings.

Contribution

It offers empirical data on real-world generative AI use, user perceptions, and concerns within a scientific organization, highlighting practical adoption patterns and challenges.

Findings

Increasing use of Argo AI tool among employees

Use cases fall into copilot and workflow agent categories

Concerns include data security, publishing, and job impacts

Abstract

Generative AI could enhance scientific discovery by supporting knowledge workers in science organizations. However, the real-world applications and perceived concerns of generative AI use in these organizations are uncertain. In this paper, we report on a collaborative study with a US national laboratory with employees spanning Science and Operations about their use of generative AI tools. We surveyed 66 employees, interviewed a subset (N=22), and measured early adoption of an internal generative AI interface called Argo lab-wide. We have four findings: (1) Argo usage data shows small but increasing use by Science and Operations employees; Common current and envisioned use cases for generative AI in this context conceptually fall into either a (2) copilot or (3) workflow agent modality; and (4) Concerns include sensitive data security, academic publishing, and job impacts. Based on our findings, we make recommendations for generative AI use in science and other organizations.