AI Driven Workplace Management
Five progressive levels of AI-powered demand forecasting and space allocation
1 – Basic
- Org team headcounts • via directory integration or CSV • manual input (CSV)
- Maintain accurate, up-to-date headcounts • No forecasting or intelligent sharing at this level
- Auto-update space allocations, assignments or neighbourhoods to match current headcounts • Instant block & stack planning
2 – Weekly Patterns
- Weekly attendance counts (anonymized) • individual IDs matched to teams • count of days attended per week
- Identify team-level in-office patterns • e.g. 50% attend 1 day/week, 30% attend 2 days, 20% attend 3 days - Classify “office personas” • e.g. Type A: 5 days every other week (avg 2.5); Type B: avg 2.5 days/week - Limit: no day-specific ratios → sub-optimal allocations
- Load-balance demand across weekdays • Recommend which days employees should come in - Match complementary schedules • e.g. pair 1-day/5-day patterns with 4-day/2-day to maximize co-attendance
3 (Recommended minimum)
- Daily attendance (anonymized) • did / did not attend each day • IDs matched to teams
- Calculate daily sharing ratios per team • e.g. Team Alpha: 2.7 people/desk (Mon), 2.1 (Tue), 1.3 (Wed), 1.9 (Thu), 3.2 (Fri) - Predict peak weekly demand • e.g. Team Alpha peak 1.3 ratio → 8 desks for 10 people - Compute team co-attendance scores (same-day % of team onsite)
- Auto-allocate the right number of desks per team, per day - Optimize neighbourhood assignments - Recommend day-swaps to boost same-day attendance
4 – Real-Time Insights
- Real-time attendance & location (per user) • IDs matched to teams
- Live building capacity monitoring • Enables on-the-fly re-provisioning - Individual co-attendance recs • Detect low co-attendance patterns & suggest schedule tweaks - Preferred space types • Track Wi-Fi/MAC to learn seating preferences
- Direct people to nearby desks or neighbourhoods based on real-time demand - Push in-office day recs to individuals for better collaborator alignment - Update future allocations by learned preferences
5 – Collaboration-Driven
- Collaboration data (MS Graph, Google, Slack)
- Identify informal teams based on interaction frequency • Reveal cross-org project groups - Surface preferred collaborators • Correlate meeting locations with Wi-Fi/MAC footprint
- Auto-suggest new custom teams based on communication patterns - When users span multiple teams, learn their true “home” teams via shared-wifi behavior
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