Overview
The fleet management industry was rapidly evolving, with OEMs tightening control over vehicle data, AI reshaping operations, and fleets demanding smarter, higher-ROI solutions. As competition intensified and base services became commoditized, Zonar’s legacy SaaS offering and new platform solution faced new constraints.
To stay ahead, Zonar needed to offer flexible data solutions, predictive analytics, and task automation. To better understand what this would look like, Zonar explored an AI-retrofitting and agent AI strategies to create an AI-assisted decision-making layer across data gathered from its telematics units, tablets, and smart cameras.
To explore AI's role in Zonar’s innovation, Amanda Parkhurst and I led a multi-stage workshop to generate AI solutions for customer value.
Business Value: Product Vision
Team: Myself & Amanda Parkhurst; participants
Contributions: UX Design, Visual Design
“What would it look like if AI was a core part of our platform’s offering?”
How can AI help?
Jobs, Data, and UX
To rethink our product experience and how AI might be incorporated, we needed to consider the overlap between the domain knowledge and associated data, the Job To Be Done, what AI capabilities provide to improve those jobs, and how we might approach the user experience to support the new capabilities with technology patterns that users might be less familiar with.
Questions we needed to ask:
What does our product look like with AI as part of the offering?
What would the experience look like if it continuously learned and adapted to users needs?
How does the human fit in the “loop”?
What are the ethical considerations of this kind of approach?
Work Session
Identifying pain points and business value
We began the session by diving into customer’s problems, irrespective of technology, with these AI use case guidelines in mind:
AI is good at…
Prediction of future events
Personalization improves the user experience
Natural language understanding
Recognition of an entire class of entities
Detection of low occurrence events that change over time
An agent or bot experience for a particular domain
Showing dynamic content is more efficient than a predictable interface
The work shop yielded several ideas, but we needed to identify a key area of customer value and business need that could benefit from an AI solution.
“The focus of the framework is not to start with the technology and try to force-fit it into a solution. Rather, it starts with the problem and works its way forward”
After identifying our target problem space (speeding and driver behavior), the next step involved incorporating additional user context. I quickly generated an archetype based on our in-house data from customer care, backlog tickets, and sales inputs:
I then leveraged Claude to develop a User Journey framework based on existing data and conceptual models for Safety Managers. What is cool is that this User Journey employs a distinctive approach by accounting for AI-driven contextual awareness (see red boxed area below)—the system's ability to understand the user's current state and dynamically generate relevant user stories based on that context. For instance, AI agents can provide targeted recommendations for evolving safety scenarios by analyzing Safety Manager Sarah's historical decision patterns and actions.
Maintaining Focus When AI Enables Unlimited Options
A key challenge during this process (comping ideas) was ensuring that our ability to rapidly generate AI-powered concepts didn't pull us away from solving the core user problem and business need. When you can create anything quickly, it's easy to lose sight of what you're actually trying to accomplish.
To address this, I had Claude create a Story Map artifact that captured our original objectives, key assumptions, and unknowns we needed to validate. This served as our north star—a reference point we could return to whenever our iterations started leading us down unproductive paths.
This approach allowed us to maintain strategic alignment while still taking advantage of AI's creative capabilities for rapid ideation and exploration.
“What does the user experience look like as it shifts from designer curated to AI enabled & user self serve?”
Envisioning Solutions
Critical questions to add structure
After narrowing down the key use cases from our pain point exercise and focusing on Sarah the Safety Manager, we shifted focus to how AI UX patterns could be integrated into the existing experience. We also had to consider how these patterns aligned with the core Job To Be Done, as well as the timing and type of data needed to support the task.
As we talked it through, it became clear that this wasn’t an “AI-first” experience—it was more of an AI retrofit. Some parts of the flow would need to lean into a more traditional chat-like AI interaction, while others wouldn’t. With that in mind, we explored examples like Microsoft’s HAX Copilot and alerting patterns from tools like Tableau Pulse. It was clear the solution would need to support a mix of AI-forward interfaces, depending on where and how users engage.
After considering where and how we might expose AI in the experience, we determined that there were two key views to explore for safety manager Sarah. The first is the Daily dashboard.
1. Daily dashboard
Use Case: Sarah needs to assess and triage the fleet and drivers daily to determine course of action. The current “homepage” for Ground Traffic Control is a “maps” view, which is useful for at a glance information on where assets are at, but doesn’t address which things managers need to pay attention to. This aggregate of information below is a new concept for Zonar - personalized for Sarah in this case with “My Metrics” tab relating to fleet safety.
“Daily dashboard” (Cont.) with embedded metrics with assistive AI open
Here, Sarah can open the “Z-Pilot” AI assistant and ask questions about fleet safety information, or take action based on the AI highlights and recommended actions within modules.
2. Speeding violations workflow
Safety Manager Mobile alert with metrics analysis and AI agent for further exploration
Use Case: Safety Manager Sarah gets alerted to trend in driver behavior via mobile. She could use assistive AI prompts to further analyze the situation….
…or go to desktop Posted Speed report with embedded AI analysis highlights in modules and “Z-Pilot” button with tabular data to explore the trend analysis more deeply….
…and then could go to immersive AI “Z-Pilot” experience in the historical tabular data view to further interrogate the data.
Query results with AI guidance, tabular data pushed down but still accessible
And, for comparison…
This is the current speeding report, without AI
Additional Agentic Workflow Concepts
Priorities
The Speed Report and Safety manager concept was a logical direction to explore, as it builds on the broader modernization efforts already underway at Zonar. Features like the Posted Speed report and driver behavior management are already in place, but ready for the next evolution—enhancements powered by AI. Beyond the Safety Manager scenario, we also developed a range of ideas and storyboards addressing real-world challenges our customers face. While some of these use cases extend beyond Zonar’s current platform capabilities, they remain important to pursue. They demonstrate how AI can enable entirely new solutions—solutions that simply weren’t possible within the platform before AI came into play.
We aligned 3 key concepts with the archetypes below, and then story-boarded the solutions to test sentiment with customers.
Envisioning the user experience
We tested these ideas with customers at our annual Zonar Together Conference with users to better understand their attitude towards AI and Zonar products. To help customers better understand and assess the concepts, I envisioned how the concepts would work and translated them into story boards that were posted at a display at the conference. These ideas work on the idea that the AI is analyzing the situation and making decisions for Zonar users with a certain amount of autonomy, I.E., a “human-in-the-loop”.
Results
The customer conference had professionals from diverse roles—transportation coordinators, fleet managers, IT specialists, mechanics, maintenance planners, and engineers.
The first day set the stage with discussions on AI familiarity, perceptions, and whether companies were actively pursuing AI-driven solutions. While some saw promise in areas like predictive maintenance, data integrity, and automation, others remained cautious about its implementation.
On the second day, following an AI presentation by Zonar to customers, reactions were mixed—some found it insightful, while others expressed uncertainty about AI’s capabilities. Conversations then shifted to Zonar’s potential AI initiatives, with attendees showing varying degrees of interest in leveraging AI for predictive maintenance, data analysis, and operational enhancements.
Next Steps
All of these ideas represent an AI focused approach to meeting the customers needs, but we need to perform various exercises to determine feasibility and viability. The agent workflows need to be further explored as they had a better defined scope and understood set of challenges to evaluate.
Prototype testing with Safety Managers: Test the "Z-Pilot" interface concepts with 8-10 Safety Managers to validate assumptions
Data audit and preparation: Assess quality/completeness of telematics data for ML training, identify data gaps for Safety Manager personas
Feature roadmap prioritization: Sequence the three concepts (predictive maintenance → smart alerting → EV planning) based on technical complexity vs. business impact and further test feasibility and desirability.