borgscience

Call for Science

AI-Assisted Frontiers in Organizational Science

CALL FOR SCIENCE

AI-Assisted Frontiers in Organizational Science

Introduction: Better, not more

Our editorial last month has prompted many in our field to ask a fundamental question about artificial intelligence and social science: do we want it to produce faster, cheaper versions of what we already do, or do we want fundamentally new science? This special issue is our attempt to reimagine organizational research – and the social sciences more generally – of tomorrow.

Organization Science was a bet on escaping the constraints of local search and incrementalism. In the first article written in this journal in 1990, Daft and Lewin urged us to break out of the “normal science straitjacket.” Over the last 35 years, Organization Science has published new and often weird ideas that have radically shaped our thinking about organizational phenomena and how to study them.

This special issue is thus a return to the founding mission of Organization Science. We want to shift our focus to how AI is changing the production of science and how it can expand our knowledge, rather than merely increasing the number of papers.

Current evidence, both from our editorial (Gartenberg, Hasan, Murray and Pierce, 2026) last month and from studies across different fields, suggests that AI is driving us to produce more—and, in many cases, worse—papers. It is obvious that the real question for us is not “how do we write 14 papers in a year?” but, rather, what kinds of knowledge can we create when the tools of inquiry themselves change?

What we’re looking for

In this call for science, we seek contributions that reimagine what a social science research contribution is in an AI-enabled world. We are encouraging researchers to experiment with wild ideas rather than obvious improvements. As Girotra, Terwiesch, and Ulrich (2010) argue, good ideas come not only from a high volume of high-quality ideas, but also from a high variance and our ability to spot the truly exceptional ones. We are okay with false positives as we seek the right-tail ideas that emerge from the most ambitious and creative uses of artificial intelligence in the social sciences.

We are open to a wide range of contribution types, including but not limited to the following.

AI-enabled research loops under human direction: Submissions may develop systems that support end-to-end or partial scientific workflows. It is unclear when or even whether these loops can perform valid social science or are just Potemkin villages of slop masquerading as research, but it is important to test their capabilities and limits.
Reusable research infrastructure: Submissions can also create other research infrastructure for social science, including custom models, multi-agent workflows, simulation environments, etc., that can be used by other scholars.
New forms of measurement: Submissions may use AI to measure constructs that were previously difficult or impossible to observe at scale.
AI-enabled qualitative and theory building work: Submissions may use AI systems to generate qualitative work or theory. The key criterion is not whether AI was used, but whether the work advances theory in a way that is important and insightful and not slop.
Synthetic social systems: Submissions may build or evaluate synthetic communities that allow social scientists to explore social dynamics in new ways.
New approaches to established research designs: Submissions may use AI to improve established research designs (approaches deployable without AI), such as improving causal identification and mechanism exploration. Such work improve what can be credibly inferred, and not merely speed up existing analyses.
Critical, diagnostic, or boundary-setting work: We also invite submissions that identify the limits of AI-enabled social science. Contributions may explore where AI systems drift, obscure judgment, or otherwise undermine validity of knowledge. Cautionary contributions are welcome when they improve the field’s ability to use AI responsibly and rigorously.

This list is not exhaustive. While what we are looking for is less clear, what we do not want is much more obvious: conventional papers, but with AI (and no slop either).

Process

In an effort to encourage submissions that think differently, we are running a three-stage process.

Stage 1: Submit research proposal and package (Deadline: November 1, 2026):

A short proposal: approximately 3-5 pages / 1,500–2,000 words, which should describe the core intended scientific contribution. This should include a description of what you have done, what you hope to do, the key risks you face in fully executing your vision, and how organizational science would change if you are successful.
Technical appendix or supplement: The technical appendix should provide the details needed to evaluate and understand the work.
Research artifact: prototype and repository. Authors should provide a Git repository or equivalent artifact that includes a prototype or proof of concept for the work.

We would prefer submissions of boundary-pushing prototypes rather than polished papers.

After receiving submissions, we will convene the Special Issue editors to select a small number of submissions for further development. We see this as a hard filter and narrow gate: once proposals make it through this initial gate, the aim is for them to have a place in the issue (with rare removal at editorial discretion due to ethical concerns and the like).

Stage 2: Engage in development process (January-May, 2027):

Selected proposals will undergo a development process to build and refine the final project deliverables. The development process will be both offline and in-person, with a workshop over two days (we are currently aiming for February 27-28, 2027, although this date may change), where participants will convene to collaboratively develop their projects and brainstorm how to improve the peer review process for this type of research.

Stage 3: Finalization of research projects (Summer, 2027):

The final product is a special issue featuring a mix of two article types. The most developed contributions will appear as ~five-page Science/Nature-style articles presenting the main insights from the research, along with a detailed technical appendix and either a public GitHub repository or, equivalently, an accessible research object.

For attempts that did not develop as far, we will provide a “letters” format: short one- or two-page contributions that distill the key learnings so the field can benefit from the work. In both cases, the bar is similar: did we learn something that advances organizational research?

Logistics

We welcome submissions from scholars across the social sciences and adjacent fields, so long as they address organizational or managerial implications, broadly interpreted. Relevant areas include, but are not limited to:

management and organizations;
strategy;
entrepreneurship and innovation;
organizational behavior;
economics;
sociology;
psychology;
computational social science;
political science;
computer science, when the contribution is directed toward social science.

Timeline:

Stage 1: Submit research proposal and package	November 1, 2026
Stage 1: Selected projects notified	January 10, 2027
Stage 2: Engage in the development process	January-May, 2027
Stage 2: In-person workshop	February 27-28, 2027 (tentative)
Stage 3: Finalization of research projects	Summer, 2027
Target special issue date	Fall, 2027

FAQ:

Can I submit a traditional empirical paper that used AI at some point in the workflow?

You can…but only if AI is core to the scientific contribution. A paper that primarily uses AI for writing, coding, summarizing, scraping, or routine classification is unlikely to meet the call.

Can this be qualitative?

Yes, we welcome qualitative and mixed-method contributions.

Can this be theoretical?

Yes, we welcome work that uses AI to build theory, provided the process is transparent and the theoretical contribution is clear.

Can I submit something that later supports a longer paper elsewhere?

Yes, provided the submitted contribution is itself publishable as a research artifact and does not create copyright or duplicate-publication conflicts.

What if my data cannot be shared?

Since the aim of this issue is to disseminate insights on AI-enabled social sciences, the research must be reproducible by others. Projects that cannot share sufficient data to be reproduced are unlikely to be a good fit for this issue. Synthetic data may be an appropriate substitute if the insights can be reproduced on a case-by-case basis.

What if my workflow depends on proprietary AI models?

Authors should select infrastructure that other researchers have a reasonable chance of accessing.

Are negative results or cautionary studies welcome?

Yes. Work that identifies failures or limits of AI-enabled social science can make an important contribution.