Hybrid Work Stabilizes as Occupancy Planning Enters a Data-Driven Era

Why It Matters

The shift signals that hybrid models are becoming the new norm, driving firms to refine space management and data strategies, while AI lag creates a competitive gap for real‑estate efficiency.

Key Takeaways

• Hybrid work utilization 56% vs 74% target, smallest gap since pandemic
• Employees attending 3‑4 days/week rose to 55%, up 19 points YoY
• 62% of firms now require fixed in‑office days, up from 49%
• Over 70% of organizations have not started AI pilots for occupancy planning
• Top AI adoption barriers: data privacy 70%, cost 46%, integration 45%

Pulse Analysis

The pandemic‑era experiment with remote work has given way to a predictable hybrid equilibrium, according to JLL’s latest occupancy benchmark. Global office utilization climbed to 56% while the aspirational target remains 74%, marking the smallest utilization gap in three years. The most significant driver is the rise of a three‑to‑four‑day in‑office cadence, now embraced by 55% of employees—a 19‑point jump from the previous year. Regional patterns diverge: North America and Latin America have largely settled on the mid‑week model, EMEA shows a split between one‑ and two‑day schedules, and APAC still favors five‑day attendance.

These behavioral shifts are reshaping corporate real‑estate strategies. Utilization metrics now underpin 90% of space‑planning decisions, up from 70% a year earlier, prompting firms to re‑evaluate the mix of work environments. Traditional private offices are declining, while demand for phone booths (41%), focus rooms (30%) and small meeting rooms (29%) is rising, reflecting a need for flexible, activity‑based spaces. Companies that can capture granular, floor‑level data rather than relying on badge counts are better positioned to align space supply with employee preferences and cost‑efficiency goals.

Despite the clear business case, AI‑driven occupancy management remains in its infancy. More than 70% of respondents have not yet launched AI pilots, and only 8% have moved beyond the testing phase. Data privacy concerns (70%), high implementation costs (46%) and integration challenges (45%) dominate the hesitation. As data quality improves, organizations that invest early in AI platforms stand to gain predictive insights that optimize desk allocation, reduce real‑estate spend, and enhance employee experience. Building a robust data foundation and change‑management program will be essential to unlock these benefits.