Nupixl: AI Team Dashboard

Introduction

Project Overview

The Nupixl Platform is an AI-powered team dashboard designed to streamline collaboration and task management for organizations managing multiple teams. The core feature, the Team Page, serves as a centralized workspace for assigning tasks, tracking project progress, and coordinating team activities. This solution aims to improve efficiency and alignment within teams.

Personal Statement

Before joining Nupixl, I was a student eager to apply my classroom and self-taught design skills in a real-world environment. My experience with Figma and UI design had been primarily through academic projects and self-driven learning. Joining Nupixl, a dynamic startup focused on innovative AI solutions, allowed me to gain practical experience and grow as a designer.

My Role

As one of two UX/UI Designers, working alongside Jacqueline Daley, I contributed to designing the interface and enhancing the user experience through research, iterative design, and collaboration. Jacqueline and I worked closely with the CEO to align the product with both user needs and business objectives. This role allowed me to expand my skills in task delegation, team collaboration, and AI integration.

Project Background

Nupixl was developed to address the complexities of managing tasks and communication across multiple teams. The platform targets businesses needing efficient solutions for team coordination, project tracking, and internal communication, with a focus on leveraging AI to streamline these processes.

Case Study Overview

This case study details the design journey of the Team Page, highlighting the objectives, challenges, research, ideation, and iterative design process that shaped the final product.

The Objectives

Primary Objective

Create an intuitive dashboard that simplifies task management, project tracking, and team collaboration. The Team Page needed to support leaders in overseeing multiple teams and ensure tasks were organized and progress was visible.

Success Metrics

  • Task Efficiency: Reduce time spent on task management by 25%.

  • User Engagement: Increase effective task delegation and progress tracking by team leaders.

  • Adoption Rate: Achieve a 30% increase in platform adoption among teams.

Research Goals and Methods

Understanding User Needs

I conducted research to identify common challenges users face in task management, onboarding, and collaboration. This involved exploring how users manage responsibilities, delegate tasks, and monitor team progress.

Key Pain Points

  • Task Efficiency: Reduce time spent on task management by 25%.

  • User Engagement: Increase effective task delegation and progress tracking by team leaders.

  • Adoption Rate: Achieve a 30% increase in platform adoption among teams.

Difficulty staying organized across multiple projects.

Task Overload

New users struggled to learn the platform.

Onboarding Confusion

Team leaders lacked insight into project progress.

Limited Visibility

Problem Statement

Existing task management tools were not flexible or integrated enough for organizations managing several teams. Users found it difficult to delegate tasks and track project progress. Our goal was to design a streamlined, transparent solution to solve these challenges.

Audience Definition

Target Users

  • Project Managers: Needed tools for task delegation and progress tracking.

  • Team Leaders: Required oversight of multiple teams and projects.

  • Employees: Sought clarity on individual tasks and deadlines.

User Personas

We developed personas such as “Olivia the Project Manager” and “John the Team Lead” to represent user goals and challenges.

Olivia The Project Manager

Goals:
  • Efficient task delegation

  • Real-time Project tracking

  • Streamlined Communication

Frustration:
  • Disorganized tasks

  • Lack of team visibility

  • Manual progress tracking

John The Team Lead

Goals:
  • Monitor team workloads

  • Simplify task assignments

  • Meet project deadlines

Frustration:
  • Overwhelming task lists

  • Poor task prioritization

  • Limited progress insights

Ideation and Brainstorming

Target Users

I analyzed platforms like Trello, Asana, and Monday.com to identify successful features such as task prioritization, project timelines, and AI-powered recommendations.

Design Iteration Process

I sketched components like the Team Overview, Task Section, and Knowledge Bar. Each version was reviewed and refined based on feedback from the CEO and design lead.

Wireframing and Prototyping

Using Figma, I created wireframes and interactive prototypes emphasizing usability and seamless navigation. The Knowledge Bar became a standout feature, enabling users to ask AI-powered questions about projects.

Wireframing and Prototyping

Using Figma, I created wireframes and interactive prototypes emphasizing usability and seamless navigation. The Knowledge Bar became a standout feature, enabling users to ask AI-powered questions about projects.

Testing and Iteration

Feedback and Refinement

Although formal user testing wasn’t conducted, I presented designs regularly to the CEO and design lead. Their feedback guided key improvements.

Key Iterations

  • Introduced color-coded priorities in the Task List.

  • Refined the AI Assistant to be more intuitive and less intrusive.

Final Design

Complete Dashboard Solution

The final Team Page design allowed seamless switching between teams, efficient task management, and clear project tracking. AI-driven suggestions enhanced productivity.

Key Features

  • Team Overview: Displays leaders, members, and projects.

  • Task Section: Status indicators and priority markers for tasks.

  • Knowledge Bar: AI-powered search for project insights.

Impact and Future Directions

Project Outcome

The final dashboard streamlined team collaboration and task management. The AI Assistant made workflows more intuitive and efficient.

Personal Growth

This project refined my iterative design approach and taught me the value of aligning design decisions with business objectives.

Future Opportunities

Further development could enhance AI predictive features and improve user onboarding through formal testing.

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