Bronze Interface, Interaction and User Experience Design · 2026
AI Auto Assignment Interface
- A' Design Award 2026 Bronze Winner in Interface, Interaction and User Experience Design
- Awarded April 15, 2026
- Estimated Number of Design Views: 9,771
- Verification code 21D7D7F656 · Verify this award
About AI Auto Assignment Interface
AI Auto Assignment is a task assignment interface for property maintenance teams. The interface uses a Kanban style board that updates in real time. Unassigned tasks appear in a queue beside a column for each technician, with availability, workload and priority shown together. Managers can drag and drop tasks manually or assign them automatically with a single click. The automatic assignment matches each job to a suitable technician based on urgency, location and required skills. The aim is to reduce coordination time and speed up repairs.
Vijay Shankar Balijaypalli's AI Auto Assignment Interface represents a thoughtful engagement with contemporary challenges in digital collaborative work environments, where the coordination of distributed tasks across team members requires both systematic clarity and intelligent automation to maintain productive flow, positioning this work within the broader discourse of enterprise user experience design that has evolved significantly since the early 2010s as organizations increasingly adopted software-as-service platforms and cloud-based collaboration tools, moving away from desktop-centric paradigms toward responsive, accessible, multi-device experiences that prioritize information clarity and reduce cognitive friction in daily professional workflows. The compositional achievement of this interface design demonstrates sophisticated understanding of visual hierarchy principles, employing a restrained chromatic palette that directs attention through strategic accent color deployment rather than chromatic saturation, allowing the neutral gray foundation to recede into the perceptual background while teal interactive elements and amber priority indicators emerge as focal points guiding user attention toward actionable opportunities, this color strategy reflecting contemporary best practices in accessible design where sufficient contrast ratios ensure legibility for users with varied visual capabilities while the limited accent palette prevents overwhelming sensory input that could impede rapid task comprehension. The technical facility evident in the systematic grid structure and modular card-based architecture suggests careful consideration of scalability and maintainability concerns inherent in complex software systems, where design components must function consistently across numerous contexts and use cases while remaining adaptable to evolving functional requirements, the repeated container pattern establishing a predictable visual grammar that reduces learning curve for new users while providing experienced users with rapid pattern recognition enabling efficient information processing. Thematically, this interface explores the intersection of human agency and algorithmic assistance, presenting a scenario where artificial intelligence capabilities propose task distributions based on criteria including availability and workload balance, yet maintaining human oversight through the confirmatory modal dialog that invites user acceptance or modification before commitment, this design choice reflecting broader cultural negotiations around automation, augmentation, and the appropriate division of decision-making authority between human judgment and computational recommendation systems. The work invites consideration of how digital tools shape professional identity and team dynamics, as the visual representation of task assignment transforms abstract collaborative relationships into concrete visible structures where individual accountability and collective progress become simultaneously legible, the interface serving not merely as a neutral transmission medium but as an active participant in constructing shared understanding of project status and individual responsibility, one might observe how such visualization systems influence team culture and interpersonal dynamics by making previously invisible coordination work explicit and therefore subject to collective scrutiny and negotiation.
Press Kit
- Unique Properties
- The Lumina Auto Assign interface redefines how maintenance operations are visualized and managed. Built on a Kanban-inspired framework, the design presents a living, real-time board where unassigned tasks queue up alongside individual technician columns — each displaying availability, workload, and priority at a glance. What sets this apart is the seamless marriage of human control and AI intelligence; managers can either drag and drop tasks manually or let Lumina's AI auto-assign with a single click — making it one of the most intuitive and decisive maintenance workflow tools in the industry.
- Dimensions & Packaging
- Lumina Auto Assign was designed entirely in Figma, built on a 1440px wide desktop frame following standard web UI specifications. Components were constructed using Figma's Auto Layout system, ensuring responsive behavior and design consistency across the interface. Interactive prototypes were brought to life using Figma Make AI, simulating real user flows including drag-and-drop task assignment and AI auto-assignment interactions. The design utilizes an 8pt grid system, structured component libraries, and clearly defined typography and color token hierarchies.
- Tags
- AI, Auto Assignment, Task Management, Technician Management, Efficiency
- Production Technology
- Lumina Auto Assign was designed using a user-centered design methodology, beginning with in-depth discovery sessions with property managers and maintenance teams to map real pain points. From there, we moved through iterative wireframing and prototyping stages, stress-testing task assignment flows before arriving at the final UI. The Kanban framework was deliberately adapted — not adopted — to suit the unique demands of property operations. AI assignment logic was woven into the interface as a natural interaction rather than a feature bolt-on, ensuring the experience felt intuitive, purposeful, and production-ready.
- Design Challenge
- The greatest creative challenge was designing for trust — convincing experienced managers to hand decision-making authority to an AI system. Historically, task assignment has been a deeply human, relationship-driven process built on institutional knowledge and personal judgment. Convincing users to trust an algorithm required us to design transparency into every interaction, ensuring the AI's reasoning felt visible and override-able rather than mysterious. On the technical side, balancing automation with human control without overwhelming the interface was a constant tension. Externally, the fragmented nature of property management workflows — varying across properties, teams, and regulations — meant we couldn't design for a single scenario. We had to craft a flexible system that adapted to real-world complexity while remaining simple enough for everyday use. Ultimately, every obstacle pushed us toward a more thoughtful, human-centered solution.
- Project Duration
- 6 months. July 2025 - Jan 2026
- Operation Flow
- Lumina Auto Assign works like a smart notice board for maintenance teams. Tasks come in, and instead of a manager scrambling to figure out who handles what, the AI instantly reads each job's urgency, location, and skill requirement — then assigns it to the right technician automatically. Managers can also drag and drop tasks themselves if needed. The board updates in real time, making it immediately clear who is busy, who is available, and what needs urgent attention — turning a chaotic process into a calm, controlled workflow.
- Research
- We conducted two rounds of research — preliminary discovery research to uncover real pain points, followed by usability testing to validate our design decisions. Our objective was simple: understand how maintenance teams currently assign tasks, where the process breaks down, and whether our design genuinely made it easier. We interviewed property managers and maintenance supervisors, observing their workflows firsthand before translating those findings into design concepts. Usability testing was then conducted using UserTesting, where real users interacted with the prototype and completed task assignment scenarios while we measured ease, speed, and confidence. Participants included property managers and maintenance coordinators actively working in multifamily housing. Results revealed that AI auto-assignment dramatically reduced decision-making time, while the Kanban-style board gave managers an immediate sense of control and clarity they previously lacked. The research confirmed that the biggest pain point wasn't the work itself — it was the coordination overhead surrounding it. In real-life terms, faster and smarter task assignment means quicker repairs, happier residents, and less burnout for management teams — ultimately improving operational efficiency across entire property portfolios.
- Inspiration
- Having worked closely with the multifamily property management industry, we witnessed firsthand how property managers and maintenance managers were constantly overwhelmed by the logistical puzzle of task assignment. Watching them juggle spreadsheets, phone calls, and manual schedules — all while trying to match the right technician to the right job based on skill, expertise, availability, and location — made it clear that there was a significant gap between the complexity of their daily operations and the tools available to support them. The inefficiency wasn't just an inconvenience; it was costing teams valuable time, delaying critical repairs, and ultimately impacting the resident experience. This frustration became our spark. We asked ourselves: what if this entire process could be handled intelligently, in seconds, without the back-and-forth? That question led us to explore how Artificial Intelligence could step in to take on the cognitive load of task assignment — automatically matching Service Requests and Inspections to the most suitable technicians based on real-time criteria. Our goal was never just to digitize an existing process, but to fundamentally reimagine it. The UX design we developed is a reflection of that vision — a carefully crafted flow that shows how AI-powered assignment can work hand in hand with the human oversight of property and maintenance managers, bringing clarity, speed, and confidence to a process that was long overdue for innovation.