Evidence map›Paper›PMID 40486918›Full record

ReviewFrontiers in artificial intelligence2025

Is AI a functional equivalent to expertise in organizations and decision-making? Opportunities and pitfalls for AI in the context of just transitions.

Marco Billi, Julio Labraña

Abstract readReview
In one paragraph

Review in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Marco BilliFaculty of Agricultural Sciences, University of Chile, Santiago, Chile.
Julio LabrañaCenter for Climate and Resilience (CR2), University of Chile, Santiago, Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The urgency of addressing climate change and achieving a just transition to sustainability has never been greater, as the world approaches critical environmental thresholds. While artificial intelligence (AI) presents both opportunities and challenges in this context, its role in organizational decision-making and expertise remains underexplored. This paper examines the interplay between AI and human expertise within organizations, focusing on how AI can complement or substitute traditional expertise across factual, temporal, and social dimensions. Drawing on Social Systems Theory, we argue that while AI excels in data processing and rapid decision-making, it falls short in contextual adaptation, long-term strategic thinking, and social legitimacy-areas where human expertise remains indispensable. And this is, we observe, particularly evident in problems connected with climate change and sustainability more broadly, where the tensions for organizational decision-making -and governance become even denser as much in the factual, temporal and social dimensions, making them into very complex, 'super-wicked', problem situations. Thus, there is a need to think more in detail about possible hybrid approaches, integrating AI's computational strengths with human interpretive and adaptive capabilities, which may offer promising pathways for advancing organizational decision-making in the overly complex, wicked decision-making scenarios characteristic of just transitions. However, this requires careful consideration of power dynamics, trust-building, and the ethical implications of AI adoption. By moving beyond techno-optimism, this study highlights the need for a nuanced understanding of AI's functional and social plausibility in organizational settings, offering insights for fostering equitable and sustainable transitions in an increasingly complex world.

Indexed as

complexityexpertiseintelligenceinterfacejust transitionsorganizationsscience-policy

Identifiers

PMID40486918
PMCPMC12142336

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.