CitizenM is a Netherlands-based hotel group known for its tech-forward approach to hospitality. With properties across Europe, North America, and Asia, citizenM has positioned itself at the forefront of digital guest experience and operational efficiency. This report examines how the company has implemented AI automation across its internal systems and infrastructure — based entirely on publicly available vendor sources.
The Challenge: Delivering Consistent, Scalable Hospitality Without Scaling Headcount
As citizenM expanded globally, it encountered several operational and service-related challenges common to modern hospitality brands:
Manual workflows across departments. Teams in finance, HR, and reservations were burdened with repetitive administrative tasks that consumed time and created bottlenecks during peak demand.
Fragmented infrastructure performance. Inconsistent Wi-Fi, delayed in-room responses, and manual environmental controls disrupted the guest experience across properties.
Staffing inefficiency. Without real-time data on occupancy, maintenance, and usage patterns, resource allocation (from energy use to team schedules) lacked optimization.
Need for centralized operational visibility. With properties spanning continents, citizenM required unified systems to continuously improve service quality and cost control at scale.
What citizenM Did: Automation Systems in Use
1. RPA Deployment for Back-Office Operations
Vendor: Aphy (formerly hivr.ai)
citizenM implemented over 30 Robotic Process Automation (RPA) bots across its finance, HR, and reservations functions. These bots now manage invoice processing, payroll entry, and booking confirmations — reducing human handling by an estimated 40%.
2. AI-Native Network Infrastructure Rollout
Vendor: Juniper Networks (Mist AI)
To improve both guest and staff connectivity, citizenM deployed Juniper Mist AI systems across hundreds of properties. The system controls Wi-Fi optimization, in-room climate and lighting adjustments, and automates issue detection through machine learning.
3. Predictive Maintenance and Resource Optimization
Mist AI systems feed centralized dashboards with real-time analytics. These dashboards inform staffing schedules, guest service adjustments, and preventative maintenance routines — helping optimize both guest satisfaction and internal resource use.
The Results
- 40% reduction in manual processing time from RPA bots (Aphy)
- 1,200 staff hours reclaimed per month for guest-focused tasks (HospitalityNet)
- 60% drop in network-related guest connectivity issues (Juniper)
- 15% decrease in property energy usage through automated climate control (Juniper)
- These outcomes, as reported by the technology vendors, reflect operational efficiencies that scale without increasing headcount or service inconsistency.
