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Compressing Delivery at
Enterprise Scale
Driving Search to Book Conversion
at Scale
4 min est. read
Overview
InspireIQ gives Aramco direct visibility into the marketing performance being delivered on their behalf, translating dense campaign data into something their own stakeholders can read and act on directly.
Because the dashboard reflected work delivered to one of the most scrutinized enterprise platforms globally, brand accuracy carried as much weight as usability. The timeline compounded it, the delivery expectation was set before the project reached me.
InspireIQ gives Aramco direct visibility into the marketing performance being delivered on their behalf, translating dense campaign data into something their own stakeholders can read and act on directly.
Because the dashboard reflected work delivered to one of the most scrutinized enterprise platforms globally, brand accuracy carried as much weight as usability. The timeline compounded it, the delivery expectation was set before the project reached me.
My Role
Lead Designer
Timeline
Q1 2026
Outcomes
Outcomes
0%
Faster end to end delivery
Faster end to end delivery
0%
Saved in engineering cost
Saved in engineering cost
The Challenge
The Challenge
The data model arrived already defined, dense with performance metrics, budget controls, and AI generated recommendations across multiple functional views. The real problem was not the data itself, it was deciding what deserved permanent visual space and what could live inside an interaction, without losing depth or overwhelming the stakeholders using it. Brand added a second layer of difficulty. Aramco operates under constant global scrutiny, so even standard interface patterns needed to be evaluated for how they reflected on the brand, not just how well they performed. And the timeline left no room to treat design and development as separate phases, the product needed to move from concept to production ready fast.
The data model arrived already defined, dense with performance metrics, budget controls, and AI generated recommendations across multiple functional views. The real problem was not the data itself, it was deciding what deserved permanent visual space and what could live inside an interaction, without losing depth or overwhelming the stakeholders using it. Brand added a second layer of difficulty. Aramco operates under constant global scrutiny, so even standard interface patterns needed to be evaluated for how they reflected on the brand, not just how well they performed. And the timeline left no room to treat design and development as separate phases, the product needed to move from concept to production ready fast.
Key Decisions
Key Decisions
The data model was already defined, so the real problem was hierarchy. I treated the dashboard as a layered system, headline metrics and performance signals fixed at the top, granular detail and AI generated recommendations pushed into interactive states. That kept a data heavy product scannable without losing depth.
The data model was already defined, so the real problem was hierarchy. I treated the dashboard as a layered system, headline metrics and performance signals fixed at the top, granular detail and AI generated recommendations pushed into interactive states. That kept a data heavy product scannable without losing depth.
Brand perception overrode what would normally be a purely aesthetic call. I designed both light and dark mode concepts. Dark mode held up on interface merit alone, but Aramco was explicit about not wanting to echo a tone associated with how the brand is sometimes perceived globally. That closed it out, not a usability gap, a reminder that even a color choice carries business weight at this level of scrutiny.
Brand perception overrode what would normally be a purely aesthetic call. I designed both light and dark mode concepts. Dark mode held up on interface merit alone, but Aramco was explicit about not wanting to echo a tone associated with how the brand is sometimes perceived globally. That closed it out, not a usability gap, a reminder that even a color choice carries business weight at this level of scrutiny.
Timeline pressure changed the pipeline, not the scope. I chained Figma, Figma Make, and Claude Code into one workflow, drafting direction in Figma, generating variants in Figma Make, refining, then building every screen and interaction directly in Claude Code as production ready code. That removed a full front end development cycle from the process entirely.
Timeline pressure changed the pipeline, not the scope. I chained Figma, Figma Make, and Claude Code into one workflow, drafting direction in Figma, generating variants in Figma Make, refining, then building every screen and interaction directly in Claude Code as production ready code. That removed a full front end development cycle from the process entirely.
Impact
Impact
The Group Account Director on the Aramco account estimated this project would typically take significantly longer through a traditional design to development handoff. Based on that assessment, the AI assisted workflow cut delivery time by roughly 70%.
The finance team, who track hours and billing on the account, calculated the front end development cost this project would normally have required. Against that figure, delivering production ready code directly from the design process saved an estimated 65% in engineering cost.
The Group Account Director on the Aramco account estimated this project would typically take significantly longer through a traditional design to development handoff. Based on that assessment, the AI assisted workflow cut delivery time by roughly 70%.
The finance team, who track hours and billing on the account, calculated the front end development cost this project would normally have required. Against that figure, delivering production ready code directly from the design process saved an estimated 65% in engineering cost.
0%
Faster end to end delivery
Faster end to end delivery
0%
Saved in engineering cost
Saved in engineering cost
Key Learnings
Key Learnings
Not every dashboard constraint is a data problem. Brand perception can override a strong interface direction, and catching that early matters more when the product reflects work delivered to a high profile enterprise platform. AI assisted workflows are most valuable when they remove a stage of the pipeline entirely, not just speed up design, which is what made the delivery time and cost gains possible without lowering the bar on execution.
Not every dashboard constraint is a data problem. Brand perception can override a strong interface direction, and catching that early matters more when the product reflects work delivered to a high profile enterprise platform. AI assisted workflows are most valuable when they remove a stage of the pipeline entirely, not just speed up design, which is what made the delivery time and cost gains possible without lowering the bar on execution.