OVERVIEW
AI Summary +
Schedule Tabs
Real-time visibility into operational outliers, AI-driven recommendations, and predictive schedule planning—empowering restaurant teams to make faster, smarter staffing and cost decisions across every location.
APPROACH
One Chef, Many Hats
1. Discovery
User Research
Interviews
Competitive Analysis
Heuristic Analysis
Market Trends
Consumer Behaviors
2. Ideation
Strategy Development
Key Solution Elements
Brainstorming Findings
User Flows
Iterative Testing
Feedback Sessions
3. Design
Wireframes
Collaborative Iteration
Accessibility Considerations
Edge Cases
Prototypes
Final Usability Testing
OPPORTUNITY STATEMENT
Providing restaurant operators with timely, actionable insights through real-time AI summaries and intuitive scheduling tools so they can adapt operations on the fly, reduce costs, and continuously improve team efficiency and profitability.
WHY THIS MATTERS
Smooth Sailing to Savings
Adapt In The Moment
Managers can spot what’s working or not as it happens, enabling quick adjustments that keep teams efficient, customers happy, and costs under control.
Reduce Costs
Operators can identify wasteful spending and inefficiencies in real time, making targeted changes that lower costs without sacrificing service quality.
Continuous Efficiency
Teams gain ongoing insights into performance trends—empowering them to refine operations continually and drive sustained growth and profitability.
UX PSYCHOLOGY
Let's Taco Bout Behaviors
AI Summary
Insights, Predictions, AI-generated Decisions
Cognitive Load (Sweller, 1988)
Why it matters: Users don’t have the mental space to process dense or confusing information.
Design implication: Visual hierarchy, progressive disclosure, and plain language summaries to reduce cognitive load. Show key insights first, and let users drill down.
Information Foraging (Pirolli & Card, 1999)
Why it matters: People behave like “information predators” scanning for the most useful bits.
Design implication: Group AI insights by relevance or urgency. Use headlines like “Today’s Priority” or “Potential Risks.” Help users get ROI from the tab in seconds.
Fogg Behavior (Fogg, 2009)
Why it matters: For users to act on AI suggestions, they need: motivation, ability, and a trigger.
Design implication: Make the suggestions feel trustworthy and easy to act on. E.g., "Staffing is low for tonight. Add 1 more server?" → Clear trigger + low effort = action.
Trust In Automation (Lee & See, 2004)
Why it matters: If users don’t trust AI, they’ll ignore its recommendations.
Design implication: Add confidence indicators or rationale ("based on 90 days of past reservations"), let users explore how the AI got to its conclusion, and use human-friendly tone, not robotic.
Dual Process (Kahneman’s System 1 and 2)
Why it matters: People make fast gut decisions (System 1) but may want to verify with logic (System 2).
Design implication: Present the AI output in a way that appeals to System 1 (clear, visual, digestible), with options to dig deeper for System 2 users.
Schedule
Shift Planning, Availability, Forecasting
Hick's Law
Why it matters: The more options presented, the longer it takes to decide.
Design implication: Limit the number of visible shift actions or staff filters. Prioritize default states that match common user intent (e.g., today’s schedule first).
Temporal Discounting
Why it matters: People tend to undervalue future consequences.
Design implication: Show how today's scheduling choices impact future staffing needs or costs. E.g., “Booking surge expected Friday – low prep coverage if you don’t staff now.”
Zeigarnik Effect
Why it matters: People remember uncompleted tasks better than completed ones.
Design implication: Use subtle reminders: “3 open shifts left to assign” or “Pending availability updates.” Helps reduce forgetfulness and nudge completion.
Social Proof (Cialdini)
Why it matters: People take cues from others, especially in uncertainty.
Design implication: Show patterns like “70% of locations staffed with 3 servers for brunch” or “Typical schedule pattern for weekends.” This supports confidence in decisions.
Goal Gradient Effect
Why it matters: People work harder as they approach a clear goal.
Design implication: Progress bars or completion counters (e.g., “80% shifts assigned”) can motivate managers to finish scheduling tasks.
DESIGN STRATEGY + UX DECISIONS
An Irresistible Menu
Clear Visual Hierarchy
Color should be used to signal urgency or success, not to overwhelm users. Soft blues to signal achievements, reds/yellows to signal urgency.
Empty States
Visuals that reframe missing data as opportunity. Instead of “No data,” using phrasing like “Once you start logging shifts, we'll show patterns here."
Microinteractions
Subtle hover animations, smooth card expansion, and progressive loading create a sense of responsiveness and care, without slowing the interface.
Role Awareness
Each user sees only what's relevant to them — no excess information, no noise. Simplicity is key here.
Tone
Labels and tooltips should use clear, human language — not jargon. Users are busy, so product should reflect a sense of respect of their time.
Easy Onboarding
Lightweight, contextual cues that guide users quickly and clearly, without any complex setup or lengthy onboarding.
INTERACTION FLOW
From Tap to Table
AI Summary
Logs In
Views 'Key Operational Insights' on Dashboard Homepage
Uses Toggle to switch between timeframes
Clicks 'View All Insights' to view in detail
Clicks on quick action buttons to make immediate changes
DESIGN CONCEPTS
The Main Course
MENU
AI Summary
Schedule
Leaderboard
Trends
Comments
Invite User
Mr. Oscar
Key Operational Insights This Week
View All Insights
Net Sales
$7,875
18%
Labor Costs
+8.2%
342.65
Food Waste
+12.4%
$89.29
Suggested Next Steps
Reduce Labor Cost
High
Trim 1–2 staff hours in low-traffic hours.
Estimated savings: $80/ week
Adjust Schedule
Optimize Peak Hour Staffing
High
2-4 PM has 30% higher traffic but same staffing levels.
Estimated savings: $120/ week
Analyze Traffic Patterns
Review Food Waste
Moderate
Waste increased by 12%. Cross-check prep volumes and supplier deliveries.
Estimated savings: $45/ week
View Inventory Logs
Portion Control Training
Moderate
Food cost variance suggests inconsistent portioning across shifts.
Estimated savings: $65/ week
Schedule Training
Key Trends and Insights
Labor Cost Forecast - Store #1864
Nov 4
Nov 11
Nov 18
Nov 25
Dec 2
Dec 9
Next 6 weeks
Previous report
6-week projection
$500
$3,000
$6,000
Key Insights
Budget Alert
Store #1864 will likely exceed labor budget by 14% by July 8th.
Trend Analysis
Labor costs increasing at 3.2% daily rate over the past week.
How Stores Compare
Performance Comparison
Key metrics vs network benchmark
Store
#1892
#1864
#1210
N. Avg.
Labor/ Rev
$0.12
$0.32
$0.18
$0.14
Waste %
4%
11%
12%
6%
Rank
3rd
24th
18th
-
Labor Cost Comparison
Labor cost per dollar revenue
01
02
03
04
05
06
BUSINESS IMPACT
Savoring Success
AI Summary
Goal
Increase adoption and trust in AI-driven decision-making to reduce operational errors and improve profit margins.
30-50% Faster Decision Making
Empowers users to make quicker, smarter decisions leading to trust and dependency
Increased Feature Engagement
Creating stickiness and retention
Higher Trust in Automation
Leading to better adoption of AI features across locations
Schedule
Goal
Streamline scheduling to reduce friction, ensure proper staffing coverage, and minimize last-minute changes that hurt service quality.
20-30% Time Savings
On weekly schedule creation
Cost Savings
Through better labor alignment (e.g., forecasting demand better to avoid overstaffing)
Lower Turnover
Consistent, transparent schedules