Artificial intelligence in impact assessment: the state of the art

Artificial Intelligence (AI) is beginning to reshape impact assessment (IA), offering both promise and disruption. This paper reviews the current state of the art, situating recent advances within a broader historical trajectory. Although early applications of AI in environmental assessment date back to the 1990s, the advent of large language models (LLMs) has driven an exponential rise in interest and experimentation since 2023. Evidence from literature, the IAIA25 conference, and a targeted survey of practitioners demonstrates that AI is already supporting efficiency gains through automation of repetitive tasks, improved data integration, and enhanced accessibility of complex information. At the same time, concerns persist regarding reliability, bias, transparency, and unresolved issues of accountability. Opportunities lie in fostering inclusivity, innovation, and capacity building, while threats include loss of trust, over-reliance, and ethical risks. We conclude that the future of AI in IA will depend critically on safeguards, governance, and professional standards to ensure that benefits are realised while risks are managed.

Authors

Institutions

Publication Details

Journal
Impact Assessment and Project Appraisal
Published
2025-11-27
DOI
https://doi.org/10.1080/14615517.2025.2594274
Primary Topic
Environmental and Social Impact Assessments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial intelligence in impact assessment: the state of the art

Jiří Dusík, Alan Bond
Impact Assessment and Project Appraisal
Environmental and Social Impact Assessments
article

Artificial intelligence in impact assessment: the state of the art

Jiří Dusík, Alan Bond
article en

Abstract

Artificial Intelligence (AI) is beginning to reshape impact assessment (IA), offering both promise and disruption. This paper reviews the current state of the art, situating recent advances within a broader historical trajectory. Although early applications of AI in environmental assessment date back to the 1990s, the advent of large language models (LLMs) has driven an exponential rise in interest and experimentation since 2023. Evidence from literature, the IAIA25 conference, and a targeted survey of practitioners demonstrates that AI is already supporting efficiency gains through automation of repetitive tasks, improved data integration, and enhanced accessibility of complex information. At the same time, concerns persist regarding reliability, bias, transparency, and unresolved issues of accountability. Opportunities lie in fostering inclusivity, innovation, and capacity building, while threats include loss of trust, over-reliance, and ethical risks. We conclude that the future of AI in IA will depend critically on safeguards, governance, and professional standards to ensure that benefits are realised while risks are managed.

Impact Assessment and Project Appraisal
United Nations Development Programme (US), University of East Anglia (GB), North-West University (ZA), American Standard (United States) (US)
Openalex Percentile: Top 53%
Environmental and Social Impact Assessments
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.