Artificial intelligence (AI) is now entering life cycle assessment (LCA) and eco-design for the same reason as in many other technical fields: too large a share of time is still devoted to repetitive tasks. Data collection, document sorting, structuring and matching of datasets, consistency checks, hotspot analysis for eco-design, or even the drafting of deliverables often account for most of the project timelines, even when modelling choices are clearly established.

Parallely, the regulations and markets require more numerous environmental results, delivered more quickly and in formats that are easier to reuse. The momentum driven by Environmental Footprint (EF) 4.0, with its requirement for structured, machine-readable background data, is a strong signal of this shift.

For a long time, the main constraints in LCA production were internal: methods, tools and availability of expertise. Today, the pressure is also external. Companies are being asked to provide LCA results earlier in the product development process, within shorter timeframes, and often as a condition for market access or for responding to tenders.

It is precisely in this context that AI finds its relevance: not as a “black-box calculator”, but as a lever for reducing friction within workflows.

What works today

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The current state of the art does not show an “AI that performs LCA”. It shows an AI that assists certain targeted stages of the LCA process, under explicit methodological control and with systematic human validation.

La littérature scientifique récente identifie de manière convergente les données d’inventaire du cycle de vie et leur structuration comme le principal goulot d’étranglement que l’IA peut traiter de façon réaliste. Les méthodes d’apprentissage automatique et les grands modèles de langage sont de plus en plus mobilisés pour extraire et structurer des paramètres pertinents à partir de publications scientifiques, de documents techniques et de bases de données. Elles permettent de réduire les efforts de tri manuel et d’améliorer la couverture des données (Hairong Wang, 2025; Zhang et al., 2021 ; Nwagwu et al., 2025).

Ces approches sont généralement mises en œuvre via des chaînes de traitement dites « retrieval-augmented », qui préservent la traçabilité vers les sources d’origine et évitent la génération directe de valeurs d’impact. Leur apport principal réside dans la pré-structuration des données d’entrée destinées à la modélisation, tout en nécessitant une validation experte de leur pertinence, des frontières de système et de la représentativité (Zhang et al., 2021 ; Kumar et al., 2025).

In parallel, several studies demonstrate the use of supervised learning models to estimate environmental impacts when the modelling space is stabilised, notably for construction products and consumer goods. These models, trained on existing LCA datasets, aim to provide orders of magnitude for product variants and constitute decision-support tools in the early design phase, rather than substitutes for full studies, given their sensitivity to the quality of the training data (Koyamparambath et al., 2022; Baehr et al., 2024).

From a practitioner’s perspective, GreenDelta’s work shows that AI is primarily integrated into database scaling and quality management processes, notably through the MSDB concept, which focuses on the harmonisation, validation, and comparability of datasets (GreenDelta, 2025a). Validation studies further indicate that AI-generated values can deviate significantly from reference datasets for certain parameters, confirming the need for systematic checks and expert oversight whenever AI contributes to the generation or selection of LCA data (GreenDelta, 2025b).

Why progress appears slow in LCA

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The adoption of AI is progressing more rapidly in engineering and business functions than in the field of LCA. Three major barriers explain this situation.

1. Risks related to certification and verification

Many LCA results are used in verified contexts, such as Environmental Product Declarations (EPDs), Environmental and Health Product Declarations (FDES) or tenders. Teams therefore fear that the use of AI may complicate verification processes. This concern can be addressed when workflows are designed in such a way that:

• AI proposes and extracts, without ever making decisions;
• each extracted value remains linked to its source;
• each modelling choice remains explicit;
• checks compare the results with known ranges and comparable products.

2. Fragmented and non-standardised data

Even the best AI systems cannot compensate for the absence of primary data. When such data do exist, they are often scattered across PDFs, spreadsheets and heterogeneous nomenclatures. AI can provide significant support, but its effectiveness is greatest when sectors adopt structured, machine-readable formats.

3. Lack of reference frameworks for AI-assisted LCA

The community does not yet have shared benchmarks that make it possible to compare, across sectors, AI-assisted workflows with conventional practices, using clear error metrics and documentation standards. Until these references are stabilised, adoption will remain cautious.

What developments lie ahead?

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LCA will become more continuous

More frequent updates, variant comparisons, and faster responses to commercial requests and tenders. The model of “an LCA every few years” is no longer aligned with market realities.

The use of AI will evolve from isolated conversational tools towards task-oriented workflows

Extraction, structuring, matching, checks and pre-drafting of results, with clear checkpoints for expert validation… This shift is crucial to maintaining trust: effort is reduced through automation, but credibility is preserved because modelling choices, data sources and checks remain explicit and auditable.

These trends converge towards a clear conclusion: AI can significantly reduce the effort required to produce LCA results, provided it is integrated into controlled workflows based on traceability and human supervision. It is this balance, rather than automation alone, that will determine whether AI strengthens or weakens trust in LCA.

Our approach at WeLOOP

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At WeLOOP, our position is straightforward: to use AI to assist practitioners, not to replace modelling.

Concretely, we are developing workflows aimed at reducing the time spent on repetitive tasks, maintaining traceability and making assumptions explicit, and preparing organisations for increasingly demanding reporting requirements and large-scale environmental assessments, particularly in the context of the transition towards structured data.

A recent application involved analysing anomalies and identifying their causes in a large EPD database, by combining AI and statistical analyses. A task that would have required several weeks of manual processing was completed in just a few hours, prior to expert validation of the detected anomalies.

We regularly share our progress at national and international conferences, through applied R&D work and scientific publications, because trust is built when methods are open to scrutiny.

If you are exploring responsible uses of AI in LCA (EPDs, FDES, product footprints, databases or internal tools), we can support you in defining what can be safely automated, what must remain under expert control, and how to design a workflow that remains fully auditable.