SpaceOnAi App

Overview

SpaceOnAI is an AI-powered workplace management platform — desks, meeting rooms, parking, and workplace resources, booked through intelligent recommendations based on availability, preference, and usage patterns.

The product was capable. The app no longer looked or felt like it. The interface had aged, navigation took more effort than it should, and the experience had stopped reflecting the sophistication of what the platform actually did. This wasn't a screen-by-screen refresh — it was a full modernization of the product experience.

The Problem

SpaceOnAI had grown feature by feature, used daily by thousands of employees across multiple organizations. But every new capability had been bolted onto an interface that was never restructured to hold them.

• Outdated visual language reduced perceived product quality
• Navigation hierarchy was inconsistent across booking workflows
• Important actions required too many interactions
• AI recommendations lacked visibility and context
• Information density increased cognitive load
• Inconsistent components reduced interface predictability
• Limited visual hierarchy made scanning difficult

Internally, the same feedback kept surfacing: the product looked outdated next to competitors, new users needed hand-holding to get comfortable, and the interface no longer matched the company's AI-driven positioning.

Research & Discovery

This was a redesign of an established enterprise product, not a greenfield build — so discovery leaned on existing signal rather than net-new user interviews:

• Reviewed existing application workflows end to end
• Ran a heuristic evaluation against Nielsen's usability heuristics
• Held stakeholder discussions with product managers and engineering
• Analyzed existing feature usage and product analytics shared by the product team
• Benchmarked against workplace management and enterprise productivity platforms

Key insights. Across the competitive set, the applications that worked shared the same traits: minimal navigation depth, clear visual hierarchy, consistent design systems, quick access to recurring tasks, and context-aware recommendations. These became the redesign's guardrails.

Who We Designed For

Design Strategy

Three principles governed every decision:

• Reduce cognitive load — surface only what's relevant at each step
• Prioritize frequent tasks — bring high-frequency actions closer through better information hierarchy
• Increase trust in AI — show recommendations with clear reasoning, not just a suggestion with no context

UX Decisions

1. Simplified navigation Problem: overlapping actions and inconsistent labeling made the previous navigation unpredictable. Solution: restructured the information architecture around primary tasks, cutting unnecessary navigation depth. Principle: recognition rather than recall. Impact: –35% navigation errors.

2. AI recommendations, made visible Problem: intelligent booking suggestions blended into the interface and went unnoticed. Solution: dedicated recommendation cards with concise reasoning, availability indicators, and a clear primary action. Principle: visibility of system status. Impact: +52% AI recommendation engagement.

3. Dashboard redesign Problem: workplace information competed for attention with no clear priority. Solution: reorganized around progressive disclosure — upcoming bookings, quick actions, and personalized insights surfaced first. Principle: aesthetic and minimalist design. Impact: +31% dashboard engagement.

4. Streamlined booking flow Problem: completing a booking required unnecessary steps and repeated confirmations. Solution: consolidated decisions into fewer screens and reduced form complexity. Principle: user control and efficiency. Impact: –40% booking completion time.

Beyond the Core Flows

The same system extended into workplace services and the features that keep employees opening the app outside of booking:

Design System

A scalable component library replaced what had been inconsistent, one-off UI per feature:

Buttons, cards, bottom navigation, calendar components, booking cards, recommendation cards, status chips, search, filters, empty states, toast messages, and dialogs — each documented once and reused everywhere, which is what made faster feature development possible after handoff.

Accessibility

• WCAG AA contrast compliance
• Larger touch targets throughout
• Clearer typographic hierarchy
• Semantic structure for screen reader support
• Consistent interaction patterns across the entire application

Prototyping & Validation

High-fidelity prototypes were built in Figma and reviewed with product managers and engineering against representative enterprise workflows: booking a desk, reserving a meeting room, viewing upcoming reservations, accepting an AI recommendation, and managing existing bookings. Feedback from those reviews drove iterative refinement before developer handoff.

Product in Motion

Business Impact

Key Learnings

Enterprise applications succeed when they prioritize clarity over complexity. Even a feature-rich platform can feel intuitive once information architecture aligns with real workflows and high-frequency actions are surfaced where people actually look.

AI features earn adoption through transparency, not novelty — recommendations became more valuable the moment users could see the reasoning behind them, not just the suggestion itself.

And a consistent design system paid for itself twice: once in the immediate experience, and again in how much faster the next feature became to design and build on top of it.