EIA is a structured process used to identify, predict and evaluate the potential environmental effects of proposed developments before planning decisions are made, and across the sector AI tools are increasingly being applied to support data processing, analytical consistency and clearer, evidence-based reporting.

While it is still early days, the pace of advancement creates a genuine opportunity to address some of the long-standing challenges that the profession has grappled with, including the scale, cost, complexity and accessibility of Environmental Statements, with the government's 2023 Post-Implementation Review of the EIA Regulations 2017 citing evidence that an average Environmental Statement for a 500-home development cost £150,000-£250,000, took 8-18 months to complete, and ran to 4,350 pages.

Where AI is adding value

• Technical and procedural tasks are where the most tangible gains have come so far, where AI is improving efficiency, rigour and analytical depth. In ecological baseline surveys, for instance, machine learning can process thousands of hours of passive acoustic monitoring data to identify bird and bat species, while computer vision can automate camera-trap image sorting and significantly reduce manual processing time. Across other technical disciplines, AI tools can interrogate complex datasets and accelerate scenario testing, which not only improves the quality of predictive modelling but frees specialist time to focus on higher-value analysis and professional interpretation.

• Cumulative effects assessment is another area where AI can add significant value, especially for major infrastructure projects where a long list of reasonably foreseeable developments is required. Natural language processing tools can search text from planning registers, extract project parameters, apply spatial filtering criteria and cross-reference consented schemes at a speed that manual compilation cannot match. The result is greater uniformity and less repetition, which matters most for large or complex schemes where the administrative burden of cumulative assessment can be considerable.

• Document and project management stands to benefit in more immediate ways. Environmental Statements are lengthy, multi-authored documents where inconsistencies between chapters are common and time-consuming to spot. AI systems can support version control, track mitigation commitments across chapters, flag cross-referencing errors and help prepare clear, non-technical summaries. Generative AI tools can summarise complex technical material into accessible language, though human verification remains crucial to ensure that the substance and defensibility of the assessment are preserved.

• Public engagement presents a different kind of opportunity, with AI tools capable of categorising and summarising consultation responses, identifying recurring themes and flagging areas of particular concern, helping project teams to understand stakeholder positions more quickly.

The Institute of Sustainability and Environmental Professionals (ISEP) 2025 guidance on AI in impact assessment highlights these opportunities, while also noting the need for safeguards to reduce the likelihood of bias in automated summaries. When applied transparently and with appropriate human oversight, AI can shorten programme timelines, improve consistency in submissions and increase the overall quality and clarity of EIA outputs, while reinforcing rather than displacing professional judgement.

Challenges and considerations

• Accuracy may be compromised. AI can introduce accuracy and reliability issues, particularly where outputs are based on incomplete or poor-quality training data, and there is a well-documented tendency for large language models to generate plausible but fabricated information, including non-existent case citations, misleading statistics and incorrect regulatory interpretations. A 2024 study by the Ministry of Housing, Communities and Local Government on AI in planning found accuracy rates ranging from 43% to 76%. While large language models have notably improved since 2024, it remains important to treat AI outputs as a starting point for expert review rather than a finished product.

• Transparency presents a related challenge, given that many AI models – particularly deep learning systems – function as opaque processes where it can be difficult to explain precisely how a conclusion was reached. In an EIA context, where the Town and Country Planning (Environmental Impact Assessment) Regulations 2017 require that Environmental Statements are prepared by competent experts and where assessment methodology must be transparent, replicable and defensible, this presents a distinct challenge for tasks such as scoping and significance assessment. The Planning Inspectorate's (PINS) guidance on the use of AI in casework evidence, updated in February 2026, requires practitioners to disclose where AI has been used, to identify the tools employed and to confirm professional responsibility for the accuracy of the content. Proportionate transparency in this area supports professional credibility rather than undermining it.

• Professional judgement must not be displaced. There is the important question of where the boundary falls between useful automation and the displacement of professional judgement, because automated analysis or drafting cannot substitute for the nuanced reasoning needed when determining the significance of environmental effects or interpreting complex, site-specific data. Significance assessment requires integrating quantitative findings with qualitative understanding of receptor sensitivity, policy context and local circumstances, and this remains a judgement that the named competent expert must be able to explain and defend independently of any AI tool.

• Data confidentiality compounds these concerns, given that EIA work frequently involves sensitive information including pre-application site strategies, protected species locations and commercially sensitive designs. Feeding such data into public AI tools risks breaching confidentiality obligations and potentially data protection law. As such, organisationally-controlled AI deployments with appropriate data processing agreements are a prerequisite for sensitive work.

Looking ahead

AI is already beginning to reshape EIA practice, and the direction of travel suggests that its role will continue to grow as tools mature and the profession develops clearer governance frameworks. As explored in a previous Savills article, the potential for AI to support more efficient, transparent and responsive planning processes is considerable, and the broader shift towards digital, data-driven Environmental Statements, advocated by ISEP's Roadmap to Digital Environmental Assessment, published in 2024, creates a natural context for responsible AI integration.

Recent announcements on Environmental Outcomes Report (EOR) reform from the Nuclear Regulatory Review signal a push to modernise reporting, with EORs expected to be implemented before the end of 2027. A key expectation is that future EOR submissions will use consistent, machine‑readable data structures. For EIA practitioners, this has direct implications for AI: enforcing common data standards in baseline reporting, significance matrices and monitoring outputs would create more comparable, structured datasets, reducing inconsistencies between projects and making assessments far more amenable to automated analysis. In practical terms, this would support AI tools in identifying trends, checking compliance and generating defensible summaries, while allowing competent experts to focus on the professional interpretation that remains essential to EOR and EIA judgements.

However, realising that potential will require the profession to invest in training, establish clear internal governance protocols that distinguish between low-risk and high-risk AI applications, develop shared standards for disclosure and verification, and maintain the kind of proportionate transparency and human oversight that the integrity of the EIA process demands.

The government's AI Playbook, published in February 2025, and the recent guidance from PINS and ISEP referenced above, all signal the direction in which professional expectations are moving. With careful and honest adoption, AI has the potential to enhance the quality, efficiency and accessibility of environmental assessment, provided that the professional judgement and accountability at the heart of EIA remain firmly in place.

Further information

Contact Ciaran Hagan or Rhys Williams