AI Auto Assignment タスク割り当てインターフェース by Vijay Shankar Balijaypalli

AI Auto Assignment タスク割り当てインターフェース by Vijay Shankar Balijaypalli is a highly esteemed winner of the very prestigious 2026 A' Design Award 銅賞 in インターフェース・インタラクション・UXデザイン Competition Category.

銅インターフェース· 2026

AI Auto Assignment タスク割り当てインターフェース

by Vijay Shankar Balijaypalli

A' Design Award 銅賞 Winner in インターフェース Category

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Verified Award Winner

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View at A' Design Award Press Release Award Badge

Colors

The Design

About AI Auto Assignment タスク割り当てインターフェース

AI Auto Assignmentは、施設メンテナンスチームのためのタスク割り当てインターフェースです。本インターフェースは、リアルタイムで更新されるカンバン形式のボードを採用しています。未割り当てのタスクは、各技術者ごとの列の横にキューとして表示され、稼働状況、作業負荷、優先度が一目で把握できます。管理者はタスクを手動でドラッグ＆ドロップすることも、ワンクリックで自動的に割り当てることも可能です。自動割り当て機能は、緊急度、場所、必要なスキルに基づいて、各作業に最適な技術者をマッチングします。これにより、調整にかかる時間を削減し、修繕作業の迅速化を図ることを目的としています。

The Recognition

About the A' Design Award 銅賞

AI Auto Assignment タスク割り当てインターフェースは、創造性、独創性、鋭い洞察力で際立つ傑出したデザインに授与される、高い権威をもつ評価である A' Design Award 銅賞の受賞作品です。銅賞の受賞作品は、十分に巧みで印象的なほど創造的な工芸品であり、デザイナーの豊富な経験、想像力、機知を裏づけています。AI Auto Assignment タスク割り当てインターフェースは、高く評価され、称賛され、愛される品であり、顕著にプロフェッショナルで革新的であることが期待されます。この A' Design Award 銅賞を受賞したデザインは、芸術・科学・デザイン・技術のベストプラクティスを取り入れており、細部への配慮に優れています。AI Auto Assignment タスク割り当てインターフェースは、ニーズを見事に満たし、欲求に触れ、感情を魅了し、好奇心を引きつけます。

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.