Genie — EY's ambient AI layer, bringing GenIAus's intelligence to the exact moment auditors need it.
GenIAus worked, but auditors wouldn't leave their page to use it — every query meant a separate tool and lost context.
Rebuilt AI as an ambient, embedded layer — a contextual widget that comes to the user instead of the other way around.
Auditors valued GenIAus — but wouldn't leave their page to use it.
Genie wasn't a feature addition to GenIAus — it was an architectural rethinking of how AI should relate to the user's workflow.
GenIAus was a destination — you go to it. Genie was an ambient presence — it comes to you. That distinction drove every design decision.
I worked directly with the VP of Product to define the product strategy across three pillars:
Technology. Establishing experience design operations and customer insights frameworks that could inform AI behavior across multiple tools — not just audit.
People. Designing for user trust, agency, and control in a domain where AI skepticism runs high and professional liability is real.
Business. Aligning Genie with EY's broader digital transformation — creating a scalable AI interaction layer that could eventually serve as the foundation for AI-assisted workflows across the entire VIA ecosystem.
A chat panel sounds simple — three constraints made it anything but.
The Risk Monitoring dashboard is already information-heavy — donut charts, heat maps, risk tables, entity breakdowns. An AI panel had to complement that density, not add to it.
Genie queried multiple sources at once — document stores, help centers, internet, live page content — slower than a typical chat exchange.
A generic answer, a page-aware answer, and a task-embedded action are three different products, not one.
I designed Genie around a layered integration model — three distinct contexts of use, each with increasing depth of contextual awareness.
More context, more depth — but always the user's choice how much AI is active.
Each decision below was a deliberate response to a specific design problem — the constraint, the resolution, and the reasoning behind it.
7 problems, 7 targeted fixes — each preserving context, control, or trust.
Designing Genie meant building the design system EY didn't have yet.
EY's design libraries had no components for conversational UI, AI states, citation patterns, or source selection. I built a dedicated AI/chat design system from scratch:
This design system wasn't just for Genie. It was designed to be the foundation for AI interactions across EY's entire product suite — portable enough to embed in any tool, consistent enough to build user familiarity across products.
Shipping Genie's first iteration was only half the job — the harder question was whether Data Source Selection and Data Query Observation were actually understood. I ran remote moderated think-aloud sessions on Microsoft Teams with practicing auditors to find out.
Two users, ambiguous labels, one buried toggle — and the same confusion twice.
The research question: "How might we ensure that Genie's new features are intuitively understood and seamlessly integrated into the auditor's existing workflow?"
"I think I understand what it is, but the label is unclear."
"It's placed too low on the interface."
"What are these sources? Are these internal databases or external ones?"
Both participants converged on the same root cause from different paths: ambiguous labels and a buried toggle were costing users time they were otherwise willing to spend exploring. Testing also surfaced three findings beyond the labeling issue:
Users worried whether audit details were visible to others through the AI.
Users wanted its knowledge cut-off date and whether it covered current events.
Users wanted a capability intro on first open, and to keep working on other pages while Genie processed.
The research findings drove three concrete design changes. The carousel below cycles through the iterations — from Design A and Design B explorations to the final design and the mid-conversation source switching pattern.
One label change removed every confusion observed in testing.
Renamed "Data Query Observation" to "Page Awareness." The original name was engineer-speak that confused every user tested; the new one describes exactly what the feature does.
Consolidated all data sources into one dropdown. Page Awareness joined Document Store, Help Center, and Internet in a single checkbox list — matching how users actually saw the decision.
Added source labels and descriptions, so users could choose without guessing whether a source was internal or external.
Five metrics, one story: embedding beat building a destination.
These two projects represent both sides of enterprise AI design — the standalone tool, and the embedded layer.
Build trust in a new tool, then carry that trust into a familiar one.
Answered: Can we build an AI tool auditors trust? Yes — with the right onboarding, prompt scaffolding, and information architecture.
Answered: can we embed intelligence without breaking trust? Also yes — with a lighter touch and a layered integration model.
Together, they represent a complete AI design trajectory: from standalone to embedded, from feature-first to context-first, from building trust in a new tool to maintaining trust inside a familiar one.
The system was the product — that's the one thing I'd protect first next time.
Measure response time by source configuration. The feature was designed partly to improve performance, but I lacked the instrumentation to prove narrower scopes actually reduced latency.
Prototype Deep Integration earlier. Only Widget and Page Awareness shipped in my tenure — starting the highest-value layer sooner would have de-risked it.
The best AI products don't feel like AI products — they feel like the tool you already use, but smarter. That's what "ambient" means.
Embedded AI is a design systems problem as much as an interaction design one. Without the component library, every future integration would start from scratch — the system was the product.
I'm open to senior product design roles, advisory work, and selective collaborations. Whether you have a defined brief or a fuzzy problem space, let's talk it through.
damleaalvee@gmail.com