AI-Driven Design
Optimizing Distribution Messaging
Optimizing Distribution Messaging
A leading distribution company launched a digital transformation initiative to improve efficiency & customer experience. One major challenge was a fragmented and inefficient messaging system between retailers and distributors, causing delays, miscommunication, and operational bottlenecks.
Team: 1 Senior UX/UI Designer + 2 Mid-Level UX/UI Designers
Duration: 6 Months [Design handoff to development]
The primary goal was to develop a unified, AI-powered communication platform that prioritized messages, optimized response management, and streamlined escalation handling, resulting in faster decision-making, enhanced operational efficiency, and improved workflow.
AI powered very stage of development, from backlog grooming to deployment, accelerating the process and ensuring precision. The result: a faster, more efficient platformthat showcased the transformative power of AI.
AI-powered ranking and user feedback streamlined decision-making, reducing cognitive load.
Automation improved synergy between designers and developers, minimizing bottlenecks.
Automated insights reduced manual tasks, enhancing workflow efficiency and user experience.
Conducted in-depth interviews with a select group of distributors and key stakeholders. These conversations helped shape key user personas, capturing their common traits, goals, and frustrations.
Generated using HubSpot, an AI-driven customer engagement platform.
The design and development teams partnered to implement a targeted web scraping strategy, analyzing industry-specific B2B forums, review platforms, and structured industry reports where users actively discuss their experiences. This approach enabled direct access to real, unfiltered user insightshelping identify key pain points, trends, & opportunities.
Visualizing the Web Scraping Process
Four out of five users and stakeholders validated the AI-identified pain points, while AI revealed a previously unnoticed issue. Led directly to solutions that improved distributor efficiency, reduced response time, & prevented escalations.
| Pain Point | User Interview | Distributor Confirmation |
|---|---|---|
| Delayed responses | Found | Confirmed (5/5) |
| Too much manual effort | Found | Confirmed (4/5) |
| No priority sorting | Found | Confirmed (5/5) |
| Hidden Operational Bottlenecks Went Unnoticed | Not Found | "We only act when it’s escalated, not when it first appears." |
To address this, we developed anAI-powered messaging hub that integrates: Smart Prioritization, Intelligent Assistance, AI-Enabled Issue Detection
Insights generated from a hybrid research approach.
In under a minute, AI generates a structured user flow,identifying inefficiencies and enhancing automation. It suggested an additional loop,integratingintelligent feedback across all stages,ensuring continuous learning and a smarter, more adaptive workflow.
User flow diagram generated using Whimsical AI
The process began with hand-drawn wireframes, mapping out the core structureof each screen. These sketches were then transformed into detailed promptsto refine and enhance the design direction. The prompts were fed into Motiff AI, generatingeditable UI designs. This approach allowed for seamless customization and iteration, ensuring a polished, user-centric interface.
Based on the brand guidelines, AI-powered plugins in Figma were used to automate component creation,ensure consistent application of design tokens, and maintainvisual coherenceacross all elements, aligning perfectly with the brand’s identity while enabling seamless and efficient iteration.
User interaction analysis with attention maps refined key actions, while accessibility contrast checks ensured readability and compliance. AI generated insights from these evaluations guided iterative design improvements, enhancing usability through data-driven refinements.
Predictive attention mapping revealed user focus areas using AttentionInsight
AI generated insights derived from heatmap analysis
Accessibility contrast check with AI-Guided color optimization
| Testing Method | Findings | Refinements Implemented | Outcome |
|---|---|---|---|
| Visual Attention Analysis | Certain UI elements (e.g., user profile) drew unintended focus. | Adjusted visual hierarchy to direct attention effectively. | Enhanced focus on critical actions. |
| Heatmap Interaction Testing | Low engagement in sidebar menu and key buttons. | Repositioned key buttons for better visibility and interaction. | Increased user engagement by 30%. |
| Contrast & Accessibility Checks | All text and buttons passed contrast compliance tests. | Ensured stronger text-background separation in critical areas. | Improved readability and usability. |
| Cognitive Load Assessment | High information density in alert sections. | Optimized content grouping for better scannability. | Reduced cognitive overload. |
AI-assisted tools were used to accelerate the transition fromdesign to development. While this improved speed, initial outputs revealed monolithic code blocks impacting maintainability. To address this, the design was refined withclean auto layout practices, logical component splitting, and a well-defined design token system. These choices led to a scalable, developer-friendly codebase aligned with the system.
Design to Code using Builder.io