Development of AI for smart buildings continues to accelerate, albeit from a small base at present. Of the broader AI market, commercial smart buildings accounted for only 0.5% of total revenues in 2020. By 2028, the smart building share of global AI could reach 0.8% according to estimates — still small but significantly expanded on today’s figure in relative terms, McHale says.
To date, most real-world deployments remain narrow in scope, but AI’s immense promise has spurred over 300 identifiable companies to launch offerings into this space.
Memoori’s report notes that security and access control is “one of the most mature and established categories of AI use cases,” with the makers of major video surveillance and access-control systems already offering commercialized AI solutions.
Memoori says key applications leverage facial recognition, multi-factor biometric authentication, anomaly detection, intrusion alerts, behavior monitoring, automated visitor management, object detection, vehicle access control and crowd analytics.
For example, facial recognition for access control is commonly used, allowing authorized individuals to securely enter restricted areas without physical credentials. Video feeds are analyzed by algorithms that match faces to a database of approved personnel.
Biometric multi-factor access combines facial recognition with additional verification factors like fingerprints or iris scans for heightened security, with AI matching across biological characteristics.
Anomaly detection solutions establishes baselines of normal activity, continuously evaluating real-time data streams for deviations that may indicate security events or breaches, Memoori notes.
Better Emergency Response
There is also demand for AI and ML services in emergency and safety systems, focusing on the critical area of building safety. This domain explores the application of AI in enhancing emergency preparedness, detection and response.
Through continuous monitoring and analysis of data from various sensors and systems, AI facilitates real-time incident detection, optimized evacuation strategies, health and safety monitoring, and environmental risk management, the report concludes.
In real-time incident detection AI algorithms rapidly identify potential safety threats, such as unauthorized access or fire, enabling swift response to mitigate risks.
When leveraging real-time data for incident response, AI guides optimal evacuation and response strategies during emergencies to ensure occupant safety.
There is also critical importance of safeguarding interconnected smart building systems against cyber threats, Memoori notes. As buildings become smarter, integrating systems for lighting, HVAC and security, they also face increased risk from cyberattacks.
The use of AI and machine learning can help to provide a proactive defense mechanism, enhancing threat detection and response capabilities to protect against operational disruptions and ensure occupant safety.
Memoori points out that with network security monitoring, AI and ML can analyze network traffic and detect anomalies, providing real-time threat intelligence. AI-driven orchestration in cyber incident response allows for rapid response to security breaches, minimizing damage and downtime.
In IoT device management, utilizing AI for continuous monitoring and management of connected devices, mitigating vulnerabilities inherent in IoT ecosystems.
Opening More Doors
AI technologies hold immense promise for optimizing building operations and creating more sustainable, responsive environments, the report notes. But there are substantial near-term barriers slowing AI’s penetration across commercial real estate portfolios despite strong buy-in from C-suite decision-makers, the firm says.
Surveys reveal the top obstacles include a shortage of internal skills to deploy and maintain AI systems, integration challenges stemming from aging “legacy” infrastructure still prevalent in properties, and trust issues resulting from a lack of user confidence in AI recommendations.
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