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OPTIMISED FIELD PLANNING
AI-Driven Territory Optimization for Balanced Workload Distribution
Project Overview
My Role
Lead UI/UX Designer
Team
4 members
Timeline
12 weeks
Tools
Figma, Figjam, Excel
PROJECT DESCRIPTION
Optimised field planning is an AI-assisted feature that helps Field Level Managers balance territories, improve coverage, and minimize disruptions while retaining human control.
By transforming a manual process into a data-driven workflow, it delivers continuous monitoring, intelligent alerts, and actionable recommendations that boost territory alignment and sales productivity.
IMPACT
75%
Review Time Reduction
12-18%
Travel Time Reduction
20%
Coverage Consistency Improvement
THE PROBLEM
Manual & Reactive Process: Monthly reports, static data analysis, hours spent per alignment cycle
Operational Inefficiency: Misaligned territories leading to long travel times and inconsistent decision-making
Business Risk: Target disruption and loss of high-value customer engagement due to poor territory management
Scale Limitation: High effort required to update multiple regions, limiting responsiveness to market changes
RESEARCH & INSIGHTS
Field Level Manager
Zip-level edits taking 25 hours per session, unclear impact of changes.
Sales Ops Leads
Inconsistent application of alignment rules across teams
Data Analysts
Repeated work due to late or inconsistent alignment changes
USER PERSONA
Sarah Martinez
Field Level Manager
8 years experience
Chicago Region
Manages 12 sales reps
Goals
Balance workloads across sales representatives
Minimize travel time and maximize face-to-face customer interactions
Preserve and strengthen customer relationships during territory changes
Make data-driven territory adjustments quickly and confidently
Frustrations
Manual zip-level edits take 2-5 hours per cycle
Unclear impact of territory changes until after implementation
Error-prone manual processes leading to suboptimal territory designs
Reactive approach to territory management instead of proactive optimization
4 Hours
Current review time per cycle
12 Hours
Avg. weekly travel time per rep
68%
Current coverage consistency score
Michael Chen
Sales Operations Lead
12 years experience
Northeast Division
Oversees 5 regions
Goals
Optimize regional performance across all territories
Enforce compliance with corporate guardrails and policies
Standardize territory management processes across teams
Reduce operational overhead and increase efficiency
Frustrations
Inconsistent decision-making across different FLMs and regions
Delayed alignment adjustments affecting quarterly targets
Misaligned metrics and KPIs across regions
Lack of visibility into territory change impacts
8%
Current error rate in alignment
5 Regions
Under Management oversight
45%
Time spent on manual coordination
KEY FINDING
Shared Pain Points
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Manual, time-intensive processes
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Lack of transparency in decision impacts
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Inconsistent application of best practices
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Reactive vs. proactive territory management
Design Opportunities
-
AI-powered suggestions with human oversight
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Transparent reasoning and impact visualization
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Configurable guardrails and policy enforcement
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Streamlined workflow automation
AI INTEGRATION
Input
Sales data, workload metrics, call activity, travel time analysis, zip adjacency mapping, island/point zip identification
Output
Intelligent alerts for imbalance / contiguity /coverage issues, ranked suggestions with impact analysis, metric comparisons (current vs proposed), root cause analysis and improvement recommendations
Human-in-Loop Design
Accept/reject/edit functionality for all suggestions, with guardrails always applied before display to ensure compliance with business rules
PROCESS TRANSFORMATION
Data Review
Download monthly report manually
Continuous monitoring with intelligent alerts
Manual filtering and zip code editing
One-click view of ranked suggestions
Analysis
Manual travel time calculations
Instant metric comparison with disruption/travel impact
Impact Assessment
Implementation
Submit changes and wait for updates
Accept/reject with instant draft updates
Process Steps
Before AI
After AI
KEY USER FLOW
User receives intelligent
alert notification
Click alert to open detailed suggestion
Navigate through recommendation options
Accept/ reject suggestion
Review and confirm changes
VISUAL COMPONENT
Alert Chips: Color-coded system for imbalance/contiguity/coverage alerts with clear iconography
Metric Tables: Inline bar indicators for quick visual comparison of before/after scenarios
Action Controls: Primary Accept/Reject options for customization
Configuration Panels: Intuitive interfaces for setting constraints and scheduling parameters
ACCESSIBILITY CONSIDERATIONS
Table View : alternative to map interface for users who cannot interact with spatial visualizations
WCAG AA compliant : contrast ratios for all map overlays and metric tables
Full keyboard navigation : support through alerts and suggestions
QUANTITATIVE RESULTS
Review Time
4 hours
0.42 hours
12 hours
10 hours
Travel Time/Week
-75%
-12-18%
68%
82%
Coverage Consistency
+20%
Error Rate
8%
2%
-75%
Metric
Before AI
After AI
Impact
CONCLUSION
This project showcases my ability as a Lead Designer to:
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Translate complex operational needs into user-centered, AI-powered workflows.
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Balance efficiency gains with human oversight and trust design.
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Deliver measurable improvements while preparing the system for next-gen Agentic AI capabilities.
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The result is a scalable, accessible, and future-ready solution that transforms territory planning from a manual burden into a strategic, AI-powered process.
COPYRIGHT @2025 | DESIGNED BY ROSHNI ♥️
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