Revenue Realization

Revenue realization confirms that closed deals actually deliver the revenue they were sold on. I led its redesign onto the iQ platform, gave deal teams product-level visibility they’d never had, and, midway through, moved the whole team’s prototyping into code with AI.

The problem

The Deal Control Team was dependent on a tool built over 20 years ago; Commercial Banking’s Business Managers followed a different process entirely. Reviews were manual: email, spreadsheets, and offline conversations. At year-end, one manager I spoke to spent a full day just reconstructing where revenue stood.

The tool that deal teams had used for two decades.

Grounding in the real process

Deal data is confidential, so I ran shadowing sessions with the deal control team as well as Treasury Management Officers (TMOs). I also worked closely with a content designer from day one, modeling the content types that are displayed and how they related before drawing a single screen.

Content model to understand what is displayed on the deal dashboard

Lo-fi mockups helped us with the structure of the new pages

The reframe that changed the design

Mid-project, we caught the comment drawer covering the exact data reviewers needed to read. We treated that as proof the interaction model was wrong and rebuilt so analysis and annotation stay visible together.

Before

The comment drawer covered the data reviewers needed.

After

Rebuilt so analysis and annotation stay visible together.

Showing how, not just whether

When presenting concepts, the Deal Control Team expressed the need not only to see whether a deal was realizing; they wanted to know how it was realizing. They needed to see percentages at the product level: which products inside a deal were carrying it, and which were dragging.

I created a new visual treatment so users could quickly gauge at a high level how many products made up a deal and the status of each of those products, without having to dive into the details of each deal.

Showing the new treatment to users elicited positive feedback.

Moving prototyping into code

Figma could show what a table looked like, not what it felt like to sort, filter, and watch real data respond. I built prototypes directly in VSCode with GitHub Copilot, wired to real development data. Stakeholder conversations flipped from “what will this look like?” to “this is what we need to build.”

Built in VSCode with GitHub Copilot, wired to real data.

Bringing the org with me

Rather than keep the method to myself, I designed and led a AI-Asssited Prototyping session teaching it to more than 200 designers and leaders across six offices: Palo Alto, San Francisco, New York, Chicago, London, and Glasgow.

Presenting a Munch & Learn across several regions to hundreds of designers.

Impact

The realization experience has rolled out to Global Corporate Banking, with new features now validated as prototypes built through AI-assisted coding before any production code is written. New features for the Commerical Banking line of business are soon to be released.

The shipped realization view in iQ.

What I’d carry forward

Capture everything: you never know what will resonate upstream. What started as a routine user session ended up quoted in leadership presentations. And AI-assisted coding changed who could participate, not just how fast I worked, because stakeholders could handle a working prototype instead of reading a spec.