Discover how artificial intelligence can automate data collection, hotspot analysis, and reporting in your Life Cycle Assessment workflow.
Artificial Intelligence (AI) is rapidly reshaping Life Cycle Assessment by automating steps that were once slow, manual, and error-prone. Instead of spending hours standardizing spreadsheets or hunting for missing information, teams can now focus on interpreting results and making better sustainability decisions. AI is not replacing expertise — it’s expanding the capacity of small and large teams alike by simplifying analysis, improving consistency, and enabling faster iteration.When embedded into a broader sustainability workflow, AI becomes a practical lever: it keeps data organized, accelerates modelling, and helps non-experts navigate complex choices with confidence.
Traditional LCAs require painstaking work across data entry, material mapping, assumption tracking, and sensitivity analysis. Many organisations — especially SMEs — struggle to maintain momentum because so much time disappears into formatting and manual validation.AI reduces these friction points by streamlining tasks such as:
Identifying missing or inconsistent data
Recognizing patterns in large datasets
Suggesting relevant process matches
Organizing information into structured formats
Producing clean documentation for internal and external stakeholders
The result is a more agile, data-driven sustainability process where teams can update models quickly, test alternatives at scale, and collaborate using a shared source of truth.
AI doesn’t replace sustainability expertise — it amplifies it by removing repetitive work and surfacing insights earlier.
Large quantities of supplier and factory data often arrive in inconsistent formats. AI can help categorize materials, normalize units, and align inputs with relevant sustainability datasets — giving teams a clean starting point without hours of manual prep.
AI models can analyze complex systems and highlight where impacts concentrate. This makes it easier to prioritize improvements, identify outliers, or spot unexpected contributors to emissions, water use, or resource depletion.
Design teams often want to know: What if we switch materials? What if we use more recycled content? What if we change suppliers?
AI accelerates this process by helping users test alternatives and understand trade-offs without fully rebuilding models.
Many stakeholders are unfamiliar with technical LCA language. AI-generated summaries turn dense results into clear, audience-appropriate narratives — perfect for executives, customers, product teams, or sustainability reports.
Use AI to communicate results in plain language, but always review the output for nuance and accuracy — especially when sharing externally.
A well-designed workflow doesn’t replace human judgment; it supports it. AI becomes most powerful when it is woven across the entire lifecycle of an assessment:
During data intake: standardizing formats, units, and terminology
During modeling: suggesting mappings and spotting gaps
During iteration: running fast comparisons and exploring options
During communication: crafting summaries and visual explanations
This connected approach makes LCA not just a technical exercise, but a collaborative sustainability tool that product, procurement, R&D, and leadership can all understand and act upon.
Centralize Data
Reduce duplication and maintain consistent assumptions across teams.
Scale Analysis
Reuse data structures and generate assessments faster with AI.
Built for teams that want accessible, data-driven sustainability workflows. Sustainly’s transparent AI copilot helps standardize inputs, flag inconsistencies, and guide beginners through structured steps — no steep learning curve required. Its shared company hub centralizes data so everyone works from the same reliable foundation.
Frameworks like Brightway or open LCA ecosystems allow researchers and technical users to experiment with advanced modelling. These can be powerful, but they often require programming skills and dedicated expertise.
Some organisations mix their LCA tool with general analytics platforms to visualize hotspots or share progress. AI can support these layers by generating clean exports, structured tables, and narrative summaries.
Integrating AI into your LCA workflow isn’t about replacing human judgment — it’s about enabling more people to participate meaningfully in sustainability work. When AI handles the tedious parts, teams can spend more time interpreting results, designing better products, and driving sustainable business value.The future of LCA is fast, collaborative, and deeply data-driven — and AI is the connective tissue that makes it scalable.
