AI Auto Assignment የተግባር ምደባ በይነገጽ by Vijay Shankar Balijaypalli

AI Auto Assignment የተግባር ምደባ በይነገጽ by Vijay Shankar Balijaypalli is a highly esteemed winner of the very prestigious 2026 A' Design Award የነሐስ ሽልማት in የበይነገጽ፣ መስተጋብር እና የተጠቃሚ ተሞክሮ ዲዛይን Competition Category.

ነሐስበይነገጽ· 2026

AI Auto Assignment የተግባር ምደባ በይነገጽ

by Vijay Shankar Balijaypalli

A' Design Award የነሐስ ሽልማት Winner in በይነገጽ Category

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The Design

About AI Auto Assignment የተግባር ምደባ በይነገጽ

ኤአይ አውቶ አሳይንመንት (AI Auto Assignment) ለንብረት ጥገና ቡድኖች የተዘጋጀ የተግባር ምደባ በይነገጽ ነው። በይነገጹ በእውነተኛ ጊዜ የሚዘመን የካንባን (Kanban) ስልት ሰሌዳ ይጠቀማል። ያልተመደቡ ተግባራት ለእያንዳንዱ ቴክኒሻን በተዘጋጀ አምድ አጠገብ በተራ ወረፋ ውስጥ ይታያሉ፣ ይህም ተገኝነትን፣ የስራ ጫናን እና ቅድሚያ የሚሰጠውን ደረጃ በአንድ ላይ ያሳያል። አስተዳዳሪዎች ተግባራትን በእጅ ጎትተው መጣል ወይም በአንድ ጠቅታ በራስ-ሰር መመደብ ይችላሉ። ራስ-ሰር ምደባው እያንዳንዱን ስራ በአስቸኳይነት፣ በአካባቢ እና በሚያስፈልጉ ክህሎቶች መሰረት ተስማሚ ከሆነ ቴክኒሻን ጋር ያዛምዳል። ዓላማው የማስተባበር ጊዜን መቀነስ እና ጥገናዎችን ማፋጠን ነው።

The Recognition

About the A' Design Award የነሐስ ሽልማት

AI Auto Assignment የተግባር ምደባ በይነገጽ የA' Design Award የነሐስ ሽልማት አሸናፊ ነው፤ ይህም በፈጠራ፣ በብልሃት እና በአስተዋይነት ለሚታወቁ ድንቅ ዲዛይኖች የሚሰጥ እጅግ ከፍተኛ ክብር ያለው እውቅና ነው። የነሐስ አሸናፊዎች የዲዛይነሮቻቸውን ሰፊ ልምድ፣ ምናብ እና ብልሃት የሚያረጋግጡ እጅግ ችሎታ የተሞላባቸው እና በሚያስደንቅ ሁኔታ ፈጠራ ያላቸው ሥራዎች ናቸው። AI Auto Assignment የተግባር ምደባ በይነገጽ ከፍ ያለ ግምት የሚሰጠው፣ የሚደነቅና የሚወደድ፣ እጅግ ሙያዊና ፈጠራ የተሞላበት ነገር እንደሚሆን ይጠበቃል። ይህ የA' Design Award የነሐስ ሽልማትን ያሸነፈ ዲዛይን በኪነ ጥበብ፣ በሳይንስ፣ በዲዛይን እና በቴክኖሎጂ ምርጥ ልምዶችን ያካትታል፤ ለዝርዝርም በሚሰጠው ትኩረት ምርጥ ነው። AI Auto Assignment የተግባር ምደባ በይነገጽ ፍላጎቶችን በግሩም ሁኔታ ያረካል፣ ምኞቶችን ይነካል፣ ስሜቶችን ይማርካል እንዲሁም የማወቅ ጉጉትን ይስባል።

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

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.
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.
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.