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July 15,
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Automated field service scheduling: the power of Computer Vision AI

automated field service scheduling

Many field service organizations recognize the need to modernize their legacy systems and revamp outdated practices. According to an industry survey, 52% of companies still use manual methods for most of their field service tasks, such as scheduling and work planning. These activities almost always have a negative effect on operations. Human schedulers must keep up to date with multiple technicians, including their availability and skills. Human error – such as double booking and mismatching skills – is inevitable, as are job overruns and cancellations. Field service scheduling gets even more complicated when companies outsource parts of their field operations to sub-contractors. Improving efficiency has therefore become a priority for many field service organizations, and some are succeeding by harnessing the power of AI to improve their internal processes, especially for automated field service scheduling.

AI transforms field service scheduling

AI technology overcomes the hurdles faced by manual schedulers by automatically assigning jobs to the right technicians based on history, skills, location, priority, tools and availability, to drive higher job success rates. An effective AI-driven scheduling solution must factor in the following considerations:

Service rules – these are instituted by the company and cannot be overridden; examples include a rule that technicians cannot be assigned jobs that fall on their days off, or those requiring technical certifications that they do not have.

Company objectives – assuming the rules are met, the AI scheduler considers company objectives, such as meeting SLA commitments, prioritizing high-value customers, and reducing travel time as much as possible by grouping nearby jobs together.

Unplanned capacity changes – AI-based systems can automatically recalibrate when the organization receives urgent service requests or unexpected cancellations, which either increase or reduce capacity of the workforce.

Unforeseen circumstances – black swan events impact an organization’s ability to provide service, requiring rapid changes to prioritization and scheduling to ensure optimal service delivery continuity.

Call and technician history – historical data is the best way to determine the average time it takes a particular technician to complete a certain task, in order to identify the worker who can do the job fastest, as well as which staff need additional training.

Computer Vision AI in field service

Computer Vision AI refers to the processing and analysis of digital images and videos to automatically understand their meaning and context. Automating field service processes using visual analysis tools or “smart eyes” is a game-changer in the sector.

A field technician can simply point his mobile camera at the equipment, allowing the AI-based system to recognize the hardware and identify the issue, in order to provide visual resolution instructions from the knowledge base. This process can be carried out either during a visual consultation with a remote supervisor, or completely autonomously with the aid of a virtual visual assistant.

The technician can then upload an image of the completed job, allowing the system to compare it with best practices to verify that the work was done correctly.

How Computer Vision AI takes automated field service scheduling to the next level

Field service scheduling can become even more efficient with the addition of Computer Vision AI technology. Field service organizations can leverage images collected from previous visual interactions – sessions with remote supervisors, on-site recordings, or communications with a virtual visual assistant – to identify which technicians have the most relevant experience, and the highest success rate, with specific issues. The automated field service scheduling system can then assign technicians for jobs they are most familiar with, raising the likelihood of a first-time-fix and and a satisfied customer.

For example, a restaurant owner takes a photo of an error message displayed on one of its walk-in fridges and sends it to the customer service department of the supplier. The company may have various technicians available for the job, but it needs to find the best person for the job, since an extended period of downtime will result in a large amount of spoiled food and significant losses. The Computer Vision AI-powered system recognizes the make and model of the unit, identifies the error message, and works out the various possible fixes. It then searches the work histories of the technicians who have uploaded images of resolved issues affecting this particular machine. The company promptly dispatches the right technician, with the right knowledge and parts, to the restaurant, ensuring that the issue is fixed as quickly and efficiently as possible.


AI is emerging as a key technology to automate field service scheduling, addressing a huge pain point for the industry. Computer Vision AI is now enabling field service organizations to achieve new levels of efficiency by assigning the highest priority jobs to the most skilled and experienced technicians.

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