Today, sustainability teams are being asked to do something much harder: not just calculate impact, but explain it, defend it, and use it to guide real decisions — at product, portfolio, and company level. And that shift changes what “doing LCA” actually looks like in practice.
The real work starts long before the calculation
When people imagine LCA work, they often picture models, software, and calculations. In reality, most of the effort still happens much earlier.
A typical workflow begins with fragmented inputs: supplier documents in different formats, partial datasets, assumptions copied forward from previous studies, and questions that can’t be fully answered yet. Before any calculation is meaningful, this information has to be interpreted, structured, and aligned.
This is where many sustainability teams quietly lose time — not because the analysis is complex, but because the data foundation is fragile. When inputs are inconsistent or poorly traceable, every result becomes harder to trust, explain, or reuse.
Over time, this creates a familiar pattern: LCAs that are technically correct, but operationally disconnected from how products are actually developed and managed.
From one-off studies to connected product knowledge
What’s changing now is not just tooling, but expectations.
Product Directors and Sustainability Managers are increasingly asked the same questions, in different forms:
- How does this material choice affect our footprint across the portfolio?
- What happens if a supplier changes?
- Can we prove improvement year over year — not just once?
Answering those questions with project-based LCAs is difficult by design. Each study starts from scratch, even when the products, materials, or assumptions are largely the same. The work becomes repetitive, and insights remain locked inside individual reports.
The shift many teams are making is subtle but significant: treating LCA data as a living product asset, not a static deliverable. When materials, components, transport, and assumptions are structured once and reused across analyses, the role of the sustainability team changes.
Less time is spent rebuilding models. More time goes into interpreting results, testing scenarios, and supporting decisions upstream — where they actually matter.
Traceability is no longer optional
As sustainability data moves closer to executive decisions and external scrutiny, traceability becomes critical.
It’s no longer enough to say what the result is. Teams are expected to explain why it looks the way it does, what assumptions sit behind it, and how confident they are in the underlying data. This is especially true when numbers are used for CSRD reporting, customer disclosures, or investment discussions.
From an LCA perspective, this means being able to trace every figure back to a source — a supplier document, a dataset, a transport assumption — without relying on personal memory or undocumented spreadsheets.
Teams that invest in traceable, connected data early find that reviews become easier, discussions become more productive, and internal trust increases. The work shifts from defending numbers to using them.
Insight is created between disciplines, not inside silos
Another change I see clearly is how sustainability work intersects with other functions.
Product teams want faster answers. Procurement wants clarity on supplier impact. Management wants a coherent story across hundreds or thousands of products. None of this can be delivered by sustainability teams working in isolation.
The most effective workflows today are shared ones — where data is accessible, assumptions are transparent, and updates propagate automatically. This doesn’t reduce the need for LCA expertise; it amplifies its value. Specialists spend less time maintaining models and more time guiding interpretation, trade-offs, and improvement strategies.
In practice, this is where sustainability stops being a reporting function and becomes a business capability.
From output to outcome
The ultimate shift is moving away from asking “Can we produce this report?” to “What decision does this data need to support?”
When sustainability data is structured, reusable, and continuously updated, answers no longer take weeks or months. Scenario questions can be explored early. Improvements can be demonstrated credibly. Reporting becomes a byproduct of ongoing work, not the reason for it.
This is the direction many teams are already moving — sometimes deliberately, sometimes out of necessity. Regulations are accelerating it, but the real driver is internal: the need for clarity, speed, and confidence in a far more complex sustainability landscape.
At EandoX, this shift is something we see every day: teams moving from fragmented, reactive LCA work toward connected systems where raw data is transformed into real, decision-grade insight. Not by replacing expertise — but by giving it a foundation that finally scales.
