When commercial insurance clients submit their ESG documentation, someone has to read it. Every utility bill, every emissions certificate, every compliance report. At Marsh McLennan's CIS division, that someone was an analyst — and it was taking four hours per case. I was brought in to fix that. But the real problem turned out to be more interesting than speed.
The CIS analysts were doing something that felt like it should have been solved already. They'd open a PDF, find the Scope 2 emissions figure on page 34, type it into the legacy system, then move to the next document. Over and over, for every client, every quarter. Four hours per case. Constant transcription errors. And with the EU's CSRD regulation coming into force, the error rate was about to become a legal problem, not just an operational one., and a growing backlog as EU regulations started demanding more granular ESG data in every premium calculation.
The thing that made this hard wasn't the AI. It was the people. The analyst needed to trust the output before acting on it. The compliance officer needed a legally defensible audit trail. The engineer needed the system to not touch a 15-year-old database that nobody fully understood anymore. Three people, three completely different definitions of "this works." If I got one wrong, the whole thing would get rejected.
I've run Double Diamond processes on maybe twenty products. Aura was the first time I had to adapt it fundamentally. The problem with applying standard UX process to AI is that you're designing for an output you can't fully predict. The AI might be right 94% of the time — but you're designing that 6% as hard as you're designing the rest. I started treating the AI's behaviour as a design material, like a constraint, rather than a feature.
One decision I'm most proud of: the data never leaves the private cloud. I pushed for this early, even before the engineers had a strong opinion on it. Under GDPR, if client emissions data touches an external API, you have an egress problem. By making the architecture closed-loop from the start, we didn't just solve a compliance requirement — we made hallucination structurally impossible. The AI can only reason about documents that are already inside the system.
The legacy database was built in 2009. It's been extended seventeen times since then by teams who are mostly no longer at the company. Every global insurance calculation Marsh McLennan runs touches it somewhere. The engineers were clear: nothing writes to it directly. Not the AI, not any new code. We had to design the entire approval workflow around that constraint — which meant the human approval gate wasn't just a trust feature. It was the only safe path to the system.
The thing I kept coming back to in research was this: the most dangerous thing isn't an AI that's wrong. It's an analyst who trusts a wrong AI without checking. Automation bias. We ran six weeks of Wizard of Oz testing — a human playing the role of the AI — specifically to find the moments where analysts would stop reading carefully. We found three. We fixed all three before a single model was trained.
Every tool was mandated to be enterprise-licensed. Consumer-grade AI tools were explicitly prohibited by Legal — any tool processing client data required contractual GDPR compliance and EU data residency guarantees.
I had one rule for every visual decision in the interface: does this reinforce trust, or does it communicate uncertainty? If it does neither, it doesn't belong in the screen. No decorative anything. The confidence bar isn't branding. The source citation isn't metadata. They're the product.