AI Product Design
Cross-Team Leadership
Accessibility
Bringing AI-Powered Cost Insights to Azure
I led key experience areas for Azure Cost Management’s first generative AI assistant, coordinating across three design teams to define scalable entry points, connect AI-generated insights to existing workflows, and deliver accessible specifications for a Microsoft Build MVP in three months.
Role
Product Designer
Timeline
3 Months
Team
8+ designers across three product teams, three UX researchers and data scientists, Content Design, Product Management, and five engineers
The opportunity
Azure Cost Management contains powerful tools for understanding cloud spending, but customers often need deep product knowledge to locate the right view, interpret changes, and investigate unexpected costs.
Microsoft formed a cross-functional team to explore how generative AI could make that information easier to access through natural-language interaction.
The goal was ambitious: deliver an end-to-end MVP in three months while coordinating patterns across several product teams and ensuring the new assistant felt like a trustworthy part of Azure rather than a disconnected experiment.
My role
I led the design of several foundational parts of the MVP:
Defining how customers would discover and enter the assistant
Creating an approach that worked across diverse Azure pages
Connecting existing Cost Analysis insights to AI-assisted exploration
Producing accessibility specifications required for release
Coordinating decisions with designers across three teams
Partnering with engineering to keep the solution feasible within the timeline
Designing through ambiguity
The team was building a new interaction pattern while Microsoft’s broader AI language, components, and product strategy were still evolving.
There was no single established answer for where the assistant should appear, how proactive it should be, or how consistently it could behave across Azure’s varied layouts.
I used four principles to evaluate decisions:
Challenge 1: Making the assistant discoverable
The assistant needed to feel central enough to be found, but not so prominent that it disrupted established Azure tasks.
I explored passive and actionable entry points across pages with very different layouts. Some surfaces were simple and spacious, while others already contained dense toolbars, cards, charts, and data controls.
A single placement could not work reliably everywhere.
The solution
I defined three complementary entry patterns:
A persistent, toolbar entry point for pages with established command structures
A contextual card entry point for surfaces where the assistant could connect directly to relevant data
A guided entry point to introduce customers to this new feature, with predictable customer questions
All three patterns reused familiar Azure patterns, reduced engineering effort, and gave teams enough flexibility to place the assistant without creating a fragmented experience.
Challenge 2: Connecting AI to trusted cost insights
Cost Analysis already gave customers structured insights into the data visible on screen. Creating an entirely separate AI workflow risked duplicating functionality and separating the assistant from the customer’s current context.
Rather than replacing the existing experience, I treated it as a bridge.
The solution
I used the existing “See Insights” interaction as an entry into deeper AI-assisted exploration.
Customers could begin with a familiar, data-grounded insight and then continue the investigation through the assistant. This created continuity between traditional analytics and generative AI while avoiding the cost of building an entirely separate insight system for the MVP.
Challenge 3: Aligning teams under a compressed timeline
The assistant crossed several parts of Azure, requiring coordination between more than eight designers across three design teams, alongside research, content, product, and engineering.
Each team had its own surface constraints and priorities. At the same time, the product needed to feel like one coherent assistant.
I helped create alignment by:
Sharing entry-point explorations across teams
Evaluating patterns against common principles
Incorporating requirements from different Azure surfaces
Prioritizing reusable, engineering-feasible solutions
Adapting designs as the broader AI experience evolved
Keeping decisions moving within the three-month MVP schedule
Challenge 4: Championing accessibility for a new AI experience
Before the assistant could launch, the new component required an accessibility review.
I had not previously created this type of accessibility specification, but I took ownership of the work rather than treating it as a downstream review task.
I partnered with accessibility-focused designers and Microsoft’s Cloud + AI Accessibility team to define:
Focus and tab order
Heading structure
Screen-reader labels
Keyboard behavior
Accessible component states
I then worked directly with engineering to review implementation and ensure the specifications were reflected in the final experience.
Shipping the MVP
Despite the product ambiguity, number of participating teams, and compressed schedule, the team delivered the MVP in three months.
The early assistant experience was introduced publicly during Microsoft’s 2023 AI product rollout and made available to pilot and preview customers for continued learning and refinement.
From AI Assistant to Azure Copilot
The MVP was part of an early product direction that later expanded into Azure Copilot. Today, customers can use Copilot to investigate cost changes, summarize spending, compare usage, estimate future costs, and identify optimization opportunities through natural-language interaction.
My contribution focused on the foundational experience questions behind that direction: how customers discover AI assistance, how it connects to trusted product context, and how it can be made consistent and accessible across complex enterprise workflows..
3 Months
Moved from an evolving AI concept to a pilot-ready enterprise experience within one quarter.
3 Design Teams
Created shared principles and reusable patterns across teams with different product surfaces and requirements.
2025 Growth
Microsoft Copilot in Azure progressed from its 2023 preview announcement to general availability in April 2025.
100s of Services
Azure Copilot now unifies knowledge and data across hundreds of Azure services and thousands of resource types.
This project changed how I think about designing AI experiences.
The hardest challenge was not drawing the assistant interface. It was introducing a new and rapidly evolving capability into a mature product ecosystem where customers depended on consistency, accurate financial context, and established workflows.
I learned to make progress through ambiguity, create alignment across team boundaries, and design flexible systems rather than one-off screens. I also expanded my accessibility practice by taking ownership of specifications for an unfamiliar interaction model and partnering with specialists to ensure the experience was ready to ship.
Most importantly, the work reinforced that AI creates the most value when it is grounded in customer context and integrated into the tools people already trust.
The most effective AI experience is not simply visible. It appears at the right moment, understands the customer’s context, and helps them move forward with confidence.