Some use cases for the combination of AI and IoT (AIoT), to optimize processes, reduce costs, increase safety, and improve quality of life.

Smart City:

• Environmental monitoring: Sensors measure air quality, noise levels, and other environmental factors. AI analyzes these data to detect environmental pollution and develop measures to improve quality of life.
• Intelligent traffic control and street lighting: AI analyzes traffic data from sensors and cameras to optimize traffic light timings in real time, reduce congestion, and improve traffic flow. Sensors detect brightness and movement. AI controls street lighting as needed and saves energy.
• Parking management: Sensors record the occupancy of parking spaces. AI guides drivers to free parking spaces and optimizes parking space management.
• Waste management: Sensors in waste containers report the fill level. AI optimizes the routes of waste collection vehicles and reduces unnecessary trips. 

Security and Surveillance (as you mentioned):

• Object recognition in security cameras: AI algorithms analyze video streams in real time to detect suspicious objects (e.g., unattended bags, people behaving abnormally, get injured or incur damage) and trigger (still) alarms.
• Detection of missing protective clothing (e.g., helmets): AI identifies individuals without required protective gear (e.g., helmets on construction sites, safety glasses in factories) and sends warnings to the responsible parties.
• Fall Detection: Sensors in wearables or environments detect falls of individuals, especially in hazardous work environments or for elderly people living alone. In case of a fall, an emergency call is automatically placed.
• Perimeter surveillance: AI analyzes data from motion detectors, cameras, and other sensors to detect unauthorized entry to premises and alert security forces.
• Early fire detection: Sensors monitor temperature, smoke, and gas concentrations. AI algorithms analyze these data to detect fires early and minimize false alarms.

Industry (Industry 4.0/IIoT):

• Predictive Maintenance: Sensors on machines monitor vibrations, temperature, noise, and other parameters. AI analyzes these data to assess the condition of the machines and predict failures. This allows maintenance work to be planned in good time and production downtime to be minimized.
• Quality control: Cameras and sensors monitor the production process. AI analyzes the data to detect errors and defects early and improve product quality.
• Energy efficiency: Sensors monitor energy consumption in production. AI analyzes the data to identify potential savings and optimize energy consumption.
• Process optimization: AI analyzes production data to identify bottlenecks and inefficiencies and optimize the production process.
• Robotics and automation: AI enables robots to perform complex tasks in production autonomously and adapt to changing conditions. Connectivity with the OPC UA protocol and Microsoft IoT: https://blogs.microsoft.com/iot/2018/04/11/why-the-opc-ua-standard-and-whats-next/

There are much more Use Case and I get a lot experience how to identify those but also to establish results to get best benefits.