The challenge of applying Artificial Intelligence to Life Cycle Assessment without losing technical consistency
In recent years, Life Cycle Assessment (LCA) has entered an unexpected phase driven by Artificial Intelligence. Tools capable of processing inventories in seconds and generating impeccable graphics have become accessible, allowing many companies to obtain quick answers without mastering the methodology. AI has democratized calculation and eliminated barriers. However, after the initial enthusiasm, an inevitable question arises: is what the machine delivers really reliable?
The risk of driving an F1 with a B driver’s license
AI has an indisputable virtue: it does numbers at an astonishing speed and is not fazed by constant changes in the inventory. But there is a critical difference between calculating and understanding.
Using an AI to perform an LCA without a solid technical basis is like getting into a Formula 1 car with just a B driver’s license. Both are vehicles, both have an engine and steering wheel, but the power of an F1 in inexperienced hands is, quite simply, dangerous. AI does not “raise an eyebrow” at an incoherent piece of data, nor does it detect if you are mixing technically incompatible processes; it simply processes, returns results, and moves on.
An LCA is not a flat mathematical exercise, but an interpretation of a complex system where a basic error can completely invalidate a sustainability strategy or an official verification.
The value of the expert: The driver behind the technology
This is where the technician contributes what does not fit into an algorithm: criteria, context, and instinct. An expert thinks before calculating; knows when a piece of data is an “optimistic wish” and what processes can compromise the rigor of a study.
Far from being a threat, AI should be an ally that frees us from mechanical work and allows us to explore scenarios quickly. But experience still weighs more than any algorithm because, in the end, the LCA is a story that must be known how to read and defend before third parties.
Our vision at Solid Forest: A project towards the future
At Solid Forest we are an active part of this evolution, but from the technical responsibility that defines us. Therefore, we are currently developing a strategic project to study and incorporate artificial intelligence capabilities into our Air.e LCA software.
We are not looking for a race for blind automation, but to design a tool where AI supports the technician, facilitating more intuitive modeling and solving mechanical tasks without the professional ever losing control or criteria. Our goal is that, in the near future, technology will accompany the expert so that they can focus their time on what really adds value: analyzing, deciding, and improving.
Conclusion
AI moves the boat, but the technician holds the helm. Adding technology and human criteria is the only way for the future of LCA to be not only more fast and efficient, but above all truthful, real, and purposeful.
