While HPE is a leader in storage, our FinOps experience lagged behind the multi-vendor reality. Enterprise customers don’t live in a single ecosystem; they navigate a messy, multi-provider landscape. By failing to integrate third-party data and AI-driven recommendations, we were relegated to a secondary tool. The goal of this redesign was to reposition HPE as the primary intelligence hub, bridging the gap between raw consumption data and actionable, multi-vendor financial strategy.

I recognized that Consumption Analytics was trapped in a ‘Reporting’ paradigm, serving as a passive ledger of past spending. My strategic pivot was to transition the product into an ‘Intelligence’ platform.

I challenged the existing roadmap by advocating for a shift from fragmented data tables to a unified narrative flow. By integrating AI-driven sustainability metrics and predictive ‘Savings Opportunities’ directly into the primary cost-monitoring workflow, I transformed the tool from a destination for data extraction into a proactive partner for financial decision-making.

As the UX lead for Consumption Analytics, my first step was to learn and understand the type of user who interacts with FinOps software. I found plenty of information on the internet, spoke with subject matter experts at HPE, and accessed research archives. I wanted to focus on users’ needs to drive business success.

I did a competitive analysis to identify where we were ahead and where we needed to catch up to deliver a minimum viable experience.

I synthesized archival research, SME interviews, and competitive analysis to identify the highest-impact opportunities. Instead of chasing parity, I focused on differentiation: identifying where AI-driven prescriptive guidance could position HPE ahead of competitors still reliant on static dashboards.

User insights directly informed prioritization, ensuring that AI-generated recommendations addressed real operational friction rather than theoretical optimization.

Consumption Analytics has many functionalities that were scattered across several tabs. Every time a new functionality was implemented, it was added to a new tab by default, making navigation more complicated. Therefore, I started working on a new IA that considered both current and new functionalities.

I use mental models to define categories for current and new functionalities, aligning with users’ expectations for what they need from a FinOps application. My main goal was to reduce users’ cognitive load and speed task completion.

I redesigned the product information architecture to unify fragmented capabilities into a scalable framework, reducing cognitive load while creating extensibility for AI-driven features.

Once I had the support of the product manager, I created a roadmap to implement the new IA, improve current user workflows, and add new functionalities that aligned with the FinOps model.