Companies that provide internet service via fiber optic cables directly to customers' homes or businesses face extreme pressures to optimize the use of resources, especially technician time. Vision AI is a game-changing technology that helps operators achieve Right First Time (RFT) accuracy.
Fiber to the Premises (FTTP) operators need to complete their network deployments while juggling the challenges of contractor shortages, network overbuild, rising cost bases, and consolidation of companies in the market landscape. Many service providers are finding current market dynamics difficult to manage.
Network deployments are often plagued with quality issues, leading to roll-out delays, additional costs, and customer dissatisfaction. Delays in roll-out, particularly in new construction, also create a missed opportunity to capture demand and revenue.
Once the network is deemed ready for service (RFS), there is also an ever-increasing focus on delivering the optimal installation journey, not just to differentiate, but to manage the cost base better, accelerate time to revenue, and ensure customer satisfaction. Tracking Right First Time (RFT) success helps monitor that technicians are correctly completing tasks, meeting quality standards, and remaining vigilant to operational details, from proper sequencing of tasks to tightening all screws.
RFT is a crucial metric for operators. Whether installing a new customer or resolving network faults, technicians need to ensure the assignment is done correctly the first time to control costs and keep customers satisfied.
In the highly competitive landscape of FTTP, vision AI solutions help operators significantly increase RFT rates through the immediate detection of anomalies and poor-quality operations, enabled by the automated analysis of visual data captured and uploaded by engineers or auditors in the field. The technology can be used to ensure quality compliance across end-to-end full fiber networks, from the fiber exchange to the structure, covering both active and passive network equipment.
Potential use cases include verifying compliance against pre-defined quality standards and detecting network anomalies or issues across an end-to-end full fiber network, from equipment in the fiber exchange to cabinets and chambers housing primary and secondary nodes, to splice boxes and optical sockets at the customer premises.
Shared Infrastructure Access. Many FTTP providers rely on access to third-party ducts, poles, and chambers to deliver and build out their own networks. In a shared infrastructure model, adherence to strict quality standards and compliance with photographic evidence is often mandatory, since sub-standard work can lead to delays and unnecessary costs due to resubmissions or additional engineer visits.
Pre-RFS Quality Control. Quality can be compromised when networks are rapidly deployed. Computer vision can be used to ensure build standards are met before network sign-off, avoiding delays in handover and accelerating RFS network availability. The technology can help with equipment installation across the end-to-end fiber network, automating real-time quality checks, reducing reworks, and the time, effort, and costs needed to complete desktop and site surveys.
As-Built Network Reviews. Visual automation can help with retrospective network documentation and auditing of as-built networks. While reviewing the thousands of photos captured would be logistically impossible for humans, AI-powered analysis can rapidly review the images, enabling post-build audits to be completed across an entire network footprint, which would otherwise be limited to a very small sample of the network.
Customer Installation. Issues with visibility in low-light environments, broadband speeds, reinstatement, or even poorly mounted Optical Network Terminals (ONTs) can result in service delays. Using computer vision to automate the installation of health checks, operators can quickly detect any corrective rework needed, minimizing delays and return visits.
Network Faults and Maintenance. Computer vision empowers field agents to resolve service issues on the first visit. The solution helps analyze images, identify potential anomalies, and propose step-by-step instructions to resolve the problem. This guidance is particularly helpful for complex issues or newer engineers who have less experience.
Vision AI plays a vital role in helping utility providers meet escalating expectations for sustainability and optimal use of resources. Installation of smart meters requires precision and quality control with high standards for compliance. The efficiency gains brought by computer vision improve the customer experience. By integrating computer vision into the installation process for smart meters as well as EV chargers, heat pumps, and other emerging smart home technologies, utility providers will help meet and exceed operational goals.