That hesitation is understandable. Product-level environmental data carries real weight. It underpins verified declarations, supports regulatory compliance, and increasingly informs internal decisions about materials, suppliers, and product design. If that data becomes harder to trust or defend, its value collapses. Speed alone doesn’t compensate for uncertainty.
Why AI Often Meets Resistance Here
Much of the scepticism around AI isn’t about the technology itself, but about experience. Environmental product data work already comes with high expectations and little margin for error. Supplier information arrives in inconsistent formats, LCAs need to be updated for seemingly small changes, and EPDs risk becoming outdated almost as soon as they’re published. Every figure may later be questioned by a verifier, a customer, or a colleague.
In that context, AI can easily sound like a risk. If it’s unclear how data is interpreted or transformed, the credibility of the entire dataset feels exposed. Once trust is questioned, it’s difficult to restore.
Start With Data Friction, Not Technology
Problems often arise when AI is introduced as an innovation rather than as a response to everyday friction. The real strain in environmental product data work is rarely the calculation methodology. It’s the preparation, structuring, and ongoing maintenance of data. Large amounts of time are spent extracting values from documents, aligning datasets across products, updating assumptions without breaking downstream results, and reusing the same information for slightly different purposes.
When AI is framed as a way to reduce this friction — rather than as something that replaces judgement — it becomes grounded and relevant. It’s no longer about automation for its own sake, but about making existing work more manageable.
Clarity Builds Trust Faster Than Speed
Vague promises of “AI-driven reporting” tend to create more concern than confidence. What matters most in this context is clarity. People need to know exactly where AI is involved and where it is not.
When it’s clear that AI supports tasks like reading and structuring input data, checking consistency, or helping reuse information across product variants — while established methods, assumptions, and final decisions remain firmly in human hands — scepticism softens. Transparency matters more than speed. Being able to trace a value back to its source and understand how it flows into an EPD or footprint calculation is what builds confidence over time.
Beyond Compliance: Supporting Better Internal Decisions
Environmental product data serves more than one purpose. It’s used for verified EPDs and external communication, but also for internal analysis — comparing materials, evaluating suppliers, and testing the impact of design or sourcing changes before decisions are made.
When AI helps keep this data consistent, up to date, and reusable across these internal scenarios, its value becomes easier to see. The conversation shifts from “faster reporting” to “better decisions earlier,” which is often a far more compelling argument.
Introduce AI Quietly and Let Confidence Grow
Large, top-down initiatives tend to amplify scepticism. A quieter approach works better. Introducing AI in a limited scope — a specific product group, a data-heavy supplier flow, or a recurring EPD update — allows people to explore the results in detail. Seeing how inputs, assumptions, and outputs behave in practice builds confidence organically.
Over time, something else tends to happen. As repetitive data handling is reduced, expertise becomes more visible. More time is spent on interpretation, improvement, and collaboration. The work shifts from assembling information to actively using it.
A Final Thought
Introducing AI into environmental product data work is ultimately about trust. When it’s implemented with clear boundaries, strong traceability, and respect for established standards, AI doesn’t weaken credibility. It reinforces it — by making product data easier to maintain, explain, and use with confidence.
EandoX is designed around these principles. It supports environmental product data work by combining AI-assisted data extraction with a structured product data library and fully traceable calculations — so EPDs, LCAs, and internal analyses all build on the same reliable foundation.
If you’re curious how this works in real workflows, book a demo to explore the EandoX platform.
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