AI in Shared Spaces

Discover how AI is transforming co-living and co-working spaces with automation, predictive analytics, and tenant experience tools — helping operators cut costs, raise occupancy, and deliver smarter spaces.

1. The Shared-Space Revolution

The global rise of co-living and co-working is reshaping how people think about space. But managing these shared ecosystems — dozens or even hundreds of tenants with rolling leases, dynamic pricing, and constant upkeep — is complex.

According to CBRE, flexible space now accounts for over 12% of office supply in major urban markets, while co-living continues to expand by 30–40% annually in Europe and Asia. As these models scale, the traditional property-management playbook breaks down.

That’s where artificial intelligence (AI) enters the picture.

From predicting occupancy to automating maintenance and personalising tenant experience, AI helps operators transform shared spaces from logistical headaches into efficient, data-driven businesses.

2. The Core Problem: Manual Systems in a Fluid Market

Managing shared spaces has unique challenges:

• High tenant turnover → frequent onboarding/offboarding

• Unpredictable demand → variable occupancy and pricing

• Constant wear and tear → intensive maintenance schedules

• Community-driven models → balancing comfort with efficiency

A 2023 JLL report estimated that operational inefficiencies can erode 15–25% of revenue in shared space businesses. Manual processes — spreadsheets, calendar coordination, and reactive maintenance — create friction, delay responses, and drive up costs.

AI solves this by connecting data streams — occupancy sensors, booking platforms, energy systems, and tenant feedback — into an adaptive ecosystem that learns and optimises in real time.

3. Dynamic Pricing and Occupancy Forecasting

AI-driven pricing is one of the biggest revenue levers for co-living and co-working operators.

Tools such as PriceLabs, Wheelhouse, and Beyond Pricing use historical data, competitor rates, local demand trends, and seasonality to recommend optimal daily prices. For co-working, systems like Desana and Nexudus Analytics integrate desk bookings and usage data to forecast demand across multiple sites.

A McKinsey analysis found that AI-based pricing models can increase revenue by 3–7% while improving occupancy by 10–15%. For a 50-unit co-living building or a 100-desk workspace, that’s tens of thousands of pounds in additional annual yield.

4. Predictive Maintenance and Energy Optimisation

Shared spaces suffer from heavier usage than conventional buildings. AI makes it possible to anticipate equipment failure before it happens.

Platforms like Facilio, BrainBox AI, and Honeywell Forge analyse sensor data from HVAC, lighting, and water systems to predict anomalies and trigger preventive maintenance.

Research by Deloitte shows that predictive maintenance can reduce unplanned downtime by 45% and cut repair costs by 30%. In addition, AI-driven energy management can trim power consumption by 10–20%, saving thousands annually in electricity and HVAC bills.

Example: BrainBox AI reported that its customers achieve an average 25% reduction in energy costs and 20–40% lower carbon emissions within the first year of deployment.

5. AI-Powered Tenant Matching and Retention

In co-living, tenant harmony drives satisfaction and retention. Some operators are experimenting with AI models that match residents based on personality traits, lifestyle, and routines.

Start-ups like Badi, RoomAI, and Flow Living use machine-learning algorithms to pair compatible tenants and build stronger communities. When residents stay longer, turnover costs drop and community cohesion strengthens.

Similarly, in co-working spaces, AI-driven scheduling tools group complementary businesses and freelancers in proximity, fostering collaboration and higher engagement.

According to Cushman & Wakefield, tenant retention improves by 18–25% in smart buildings with personalisation and community features.

6. Smart Access and Automation

Access management and security are critical pain points in shared properties. AI-enabled systems like Kisi, Salto KS, and Proxy combine mobile credentials, facial recognition, and occupancy analytics to ensure seamless entry for authorised users while reducing the need for on-site staff.

Paired with IoT sensors and chatbot integrations (e.g., Tawk.to or ChatGPT-powered service bots), operators can automate 24/7 support — from booking meeting rooms to reporting issues.

This shift not only reduces staffing costs but also elevates tenant experience, as users interact with an always-available, consistent digital concierge.

7. Data-Driven Community and Space Design

AI doesn’t stop at operations — it informs design. Heat-mapping and behavioural analytics platforms like Density, VergeSense, and Envoy Workplace collect anonymised occupancy data to reveal how people move through spaces.

These insights help operators:

• Reallocate under-used zones

• Resize meeting rooms or lounges

• Identify the best spots for premium desks or shared amenities

Over time, AI-assisted layout optimisation increases utilisation per square metre — a key metric for co-working profitability.

CBRE’s 2024 Future of Work report found that data-driven workplace design can boost revenue per sq. ft. by 11–18% through better utilisation and design alignment.

8. AI in Financial & Asset Management

AI also enhances back-office efficiency. Cloud-based systems such as Sturppy AI, Re-Leased, and Visitt AI automatically aggregate revenue, expenses, and occupancy data to generate real-time performance dashboards.

Operators can:

• Forecast cash flow

• Model rent scenarios

• Predict ROI for expansion

For investors and asset managers, AI-enabled transparency helps validate valuations and monitor portfolio health, making co-living and co-working assets more bankable.

9. Quantifying the ROI

Bringing these technologies together can transform the economics of shared spaces.

For a mid-size operator with £1 million annual revenue, these efficiencies can add £80,000–£120,000 in profit — equivalent to adding a whole new property without expansion.

10. Implementing AI in Shared Spaces

Getting started doesn’t require a complete tech overhaul. Operators can phase it in:

1. Integrate sensors for occupancy, air quality, and energy.

2. Adopt analytics tools like Facilio or Re-Leased for portfolio insights.

3. Automate communication via chatbots or CRM systems.

4. Test pricing AI on a single property to benchmark gains.

5. Expand and train staff for cross-functional digital workflows.

The key is incremental adoption — layering AI tools on existing operations while tracking measurable ROI.

11. The Future: Autonomous Communities

As data ecosystems mature, the line between co-living and co-working will blur. Future spaces will use unified AI platforms that coordinate pricing, maintenance, and experience in one self-learning loop.

Imagine a system that adjusts room rates, schedules cleaners, predicts tenant churn, and suggests ideal housemates — all autonomously. This is not far off. Platforms like WeWork’s Workspace AI and Common Living’s Flow Engine are already developing these capabilities.

The next decade will see shared spaces operate less like landlords and more like tech-driven service providers — adaptive, data-first, and customer-centric.

🏁 Conclusion

AI is no longer a luxury for large operators; it’s becoming the foundation for profitability in co-living and co-working. By automating routine tasks, predicting demand, and personalising user experience, AI empowers operators to run leaner, smarter, and more resilient portfolios.

The shared-space economy thrives on flexibility — and AI is the key to delivering it at scale.