Return-to-Office Planning
The Problem
“Optimize office occupancy amid hybrid work uncertainty”
Organizations face these key challenges:
Unreliable and delayed occupancy visibility across buildings, floors, and days of week, causing either wasted leased space or peak-day overcrowding
Fragmented data (access control, Wi-Fi, room booking, HR) and inconsistent definitions of utilization, making benchmarking and forecasting error-prone
High-stakes lease decisions (renewal, contraction, sublease, relocation) made with static assumptions that fail when hybrid behavior shifts
Impact When Solved
The Shift
Human Does
- •Collect badge, survey, booking, and headcount inputs from separate sources
- •Estimate attendance by building, floor, and day using spreadsheets and static ratios
- •Review utilization trends and identify overcrowding or underused space after issues appear
- •Run periodic lease, restack, and amenity scenarios using broad benchmarks
Automation
- •No meaningful AI-driven forecasting or optimization in the legacy process
- •No continuous monitoring of occupancy shifts across teams, sites, and weekdays
- •No automated scenario generation for peak-demand space planning
Human Does
- •Set workplace policy assumptions, planning goals, and acceptable capacity thresholds
- •Review forecast-driven scenarios and approve portfolio, building, and floor actions
- •Decide lease, sublease, renewal, restack, and neighborhood planning actions
AI Handles
- •Unify occupancy-related signals and produce a current utilization view across sites and floors
- •Forecast average and peak attendance by team, location, and day of week
- •Detect anomalies and shifts in attendance patterns, churn, and capacity risk
- •Generate scenario comparisons for rightsizing, seat allocation, staggered schedules, and restacking
Operating Intelligence
How it works
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve or execute lease, sublease, renewal, or restack decisions without review and sign-off from the responsible real estate leader. [S3]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Return-to-Office Planning implementations:
Key Players
Companies actively working on Return-to-Office Planning solutions:
Real-World Use Cases
AI tenant service and churn prediction for commercial properties
Software watches tenant questions, preferences, and service history so landlords can answer faster and spot who may leave before they do.
Predictive spare-parts and maintenance scheduling for critical building systems
AI predicts which parts a building will likely need soon, so managers can stock the right items and schedule repairs at the least disruptive time.
Energy Fault Detection and Diagnostics (EFDD) for buildings
AI watches a building’s energy data and flags unusual patterns that suggest wasted energy or failing equipment, so staff can fix problems early.