Our customers were heading into their first mandatory CSRD reporting year, and a regulatory first year is when a company decides, once, how it will do this reporting from then on. Slip six months past that window and you are not filling a gap, you are displacing a process they have already built out of spreadsheets or a consultancy.

Building the wrong version first was costed at roughly €280k: a team of five, including me, for six months plus sustainability expert and Head of Engineering overhead. At SAP's scale, that is not the number that frightens anyone. The real exposure was reputational, on a flagship AI product with Gartner and the analyst firms watching, launching to customers already sceptical of AI. Trust costs more to win back than to earn, and enterprise customers talk to their peers.

So the brief was never simply to ship fast. It was to ship fast and not ship something that damaged trust.

I led design strategy, orchestrating across a PM, Head of Engineering, ML engineers, an internal Design and AI Ethics Team, innovation researchers, and sustainability experts. Sustainability reporting was new territory for our team, so my scope started with learning the domain from scratch alongside running customer research, then moved through validation into shaping the production feature.

Decisions and Actions

• Aligned with engineering on a parallel timeline: they built a rough proof of concept while our design team learned sustainability reporting and ran customer research, so progress didn't stall on sequencing. The alternative, discovery first and build second, is the exact pattern that earns design its reputation for slowing things down, and it would have cost months we did not have.
  
  

• Managed expectations with our PM team and communicated to customers that the first testable version was deliberately minimal, we were testing assumptions, not polish.
  
  

• Ran task-based usability testing with five beta customers on their own production data, the only route to real critique of the AI's output quality rather than opinions about the interface. The five were chosen deliberately, not opportunistically: strength of relationship, how far along their implementation was, how much live data they already had running in the system, and genuine interest in the problem. SAP deployments take months to stand up, so the pool who could test on real data at all was small. I presented the main findings to senior leadership.
  
  

• Designed three iterations from raw proof of concept to a production-ready feature, including the visual indicators and review workflows that met internal AI-ethics standards.