Rapid Results with Early Prototyping and AI Integration

Background

Fleet managers today are drowning in data but starved for insight. Despite having access to real-time vehicle locations, maintenance alerts, and driver behavior metrics, they struggle to answer the most critical question: “Where should I focus right now to make the biggest impact?” Instead of actionable guidance, they face a flood of dashboards and reports, leading to information overload, missed opportunities, and reactive firefighting.

Inside the company, traditional workflows such as manual prototyping, late-stage feedback, and siloed teams were slowing innovation and making it harder to align around customer needs. The team recognized that to truly empower customers and accelerate product development, they needed a new approach: one that would harness AI not just as a technical feature, but as a collaborative partner in surfacing actionable insights, aligning teams, and driving smarter, faster decisions.

Customer Challenges

Difficulty Turning Data into Action: Customers received large volumes of data but struggled to efficiently interpret and prioritize which issues to address for maximum impact.
Low Trust in AI: Skepticism about AI transparency and accuracy led to hesitation in adopting AI-driven insights.
Reactive Rather Than Proactive Operations: Without clear guidance or predictive analytics, customers often addressed problems only after they became urgent, missing opportunities for proactive management.

Internal Team Challenges

Delayed, Labor-Intensive Prototyping: Prototyping occurred late due to manual effort, slowing feedback and cross-team alignment.
Unclear how to integrate AI: Teams defaulted to traditional solutions, lacking a unified approach for collaborating/integrating AI into problem-solving.

Solution

Use AI as a collaborator & adopting early stage prototyping

Design leadership led a transformation grounded in three areas:

AI as a Creative and Analytical Partner: AI was used to enhance human expertise—automating routine analysis, surfacing insights, and accelerating iteration—without replacing the judgment and context of fleet experts.
Prototyping in the Ideation Phase: Leveraging Lovable.dev, the team generated interactive prototypes during ideation, allowing for real-time collaboration with engineering and product management, and enabling early concept testing and feedback.
Customer focused AI principles to build trust and adoption: Aligned internal teams on delivering an experience that builds trust, gives user control and increases discoverability of AI as a partner

Results

50% Faster Concept-to-Validation: Early prototyping and AI-accelerated research cut the time to validated concepts in half.
Higher-Quality Deliverables: Personas, journey maps, storymaps, and prototypes were richer and more actionable.
Cross-Functional Alignment: Real-time collaboration ensured engineering, PM, and design were aligned from day one.
Reduced Rework: Early user testing caught issues before costly development, saving time and resources.
AI as an Enabler: Teams reported higher engagement and creativity, viewing AI as a supportive partner, not a replacement

Process

1. Empathize & Define: Data-Driven Personas and Journey Maps

AI-Enhanced Research: AI tools aggregated and analyzed user data, revealing key pain points and behavioral patterns.
Human Insight: The design team synthesized findings into actionable personas and journey maps, enabling the product team to develop empathy and context.
Deliverables: Persona, journey map and design principles

**User persona:** AI accelerated persona creation by analyzing user data and generating detailed, data-driven profiles in minutes—helping me quickly identify user goals, pain points, and behaviors to inform the design process.

**Journey map:** Collaborating closely with AI, I identified key opportunities and potential AI-driven capabilities throughout the user journey, ensuring the map highlighted where enhancements could deliver the most impact.

**Design principles**: These principles ensured users understand how AI works, feel supported and inspired, experience smooth interactions even in edge cases, and can easily uncover valuable insights.

2. Ideation with Cross-Functional Team

AI-Powered Brainstorming: Chris Hannon and I led a series of brainstorm sessions with a large cross section of the company to generate ideas to solve the agreed upon problem we wanted to solve. After our large-group brainstorming sessions, we leveraged AI to analyze what had been created by our team and to suggest alternate ideas that the team had not considered. AI was used to cluster and analyze the generated ideas, mapping them to business and customer value. This helped us to rapidly identify and prioritize the problem with the greatest potential impact; ensuring team alignment and a clear, actionable focus for solution development.
Deliverables: Workshops & user flows highlighting areas where AI can drive value

**Workshop**: A snippet of the multi-day collaborative brainstorming sessions where we brainstormed ideas to the key customer problem we were seeking to solve.

**User flows**: This user flow highlighted the key touchpoints where AI could enhance the customer experience, serving as a crucial alignment tool for the team and ensuring everyone focused on the most impactful opportunities.

3. Early stage prototyping to validate concepts

Real time Prototyping: With a smaller team consisting of a PM, lead engineer, data scientist and design, I led a session where we created low-fi prototypes (using AI powered natural language tools) in a fraction of the time it would typically take us to build an interactive prototype. In these sessions my PM and engineer colleagues provided technical feedback and business context as the prototype evolved.
Immediate User Testing: Early concepts were shared with internal stakeholders and customers for feedback, allowing the team to validate assumptions and refine flows before development began.
Deliverables: Interactive prototypes screenshots

**Prototype**: I used Lovable.dev to create this prototype, test with users, iterate on until the team felt we had validated assumptions and had a more clear view of whether this solution exceeded the customer’s expectations.

**Prototype**: This second screen received very positive feedback from customers since they could clearly see how AI would help in the identification of issues, but also assist in recommending what actions needed to be taken to become a safer driver.

4. High fidelity design and usability testing

· High-Fidelity Design & AI Acceleration: Building on validated prototypes, our lead designer transitioned to Figma to create high-fidelity designs using our established design library. Figma’s AI tools accelerated this process by generating layout drafts, suggesting UI improvements, and auto-generating responsive components—enabling rapid assembly and refinement of complex screens.

· Design System Integration: All final screens were constructed with reusable components and styles from the design library, ensuring visual consistency and scalability. Figma AI assisted in maintaining alignment with accessibility standards and design principles by flagging contrast issues and offering real-time suggestions.

· Interactive Prototyping & User Testing: We leveraged Figma’s prototyping features, enhanced by AI-generated flows and micro-interaction suggestions, to build interactive prototypes. These were shared with stakeholders and users for usability testing, allowing for quick iteration based on feedback.

Deliverables: High-fidelity interactive prototypes