AI Predictive Maintenance and Proactive Customer Assistance

By Andrew Mort

AI in Customer Service

A study by The Wall Street Journal and Emerson reported that unplanned downtime — 42% of which is a result of equipment failure — costs manufacturers an estimated $50 billion per year. The costs associated with downtime continue even after production has resumed. According to the Customers’ Voice: Predictive Maintenance in Manufacturing report by Frenus, approximately 50% of all large companies face quality issues following an unplanned shutdown.

The Case for Predictive Maintenance

Data from the US Department of Energy indicates that predictive maintenance is highly cost-effective, delivering a 1,000% increase in ROI, a 25%-30% reduction in maintenance costs, ensuring 70%-75% fewer breakdowns and achieving a 35%-45% reduction in downtime.

UPS reports that it has already saved millions of dollars by implementing a predictive maintenance solution that reduces delivery truck breakdowns.

ThyssenKrupp claims that its predictive maintenance solution has dramatically increased elevator availability by employing real-time diagnostics that reduce out-of-service time.

Cisco, meanwhile, uses predictive maintenance to optimize network performance and carry out faster troubleshooting.

Predictive Analytics Applied to B2C

The Internet of Things (IoT) refers to a network of physical objects or “things” that are embedded with smart technology, enabling them to collect and exchange data. According to Gartner, there will be 20.8 billion devices connected to the IoT by 2020.

This proliferation of technology has been primarily driven by the widespread adoption and deployment of sensors and smart devices, advanced analytics and artificial intelligence.

These enablers provide businesses with endless opportunities for real-time data-based insights that make it easier to predict when consumer devices will fail. These insights also enable businesses to provide proactive customer assistance that results in higher customer satisfaction.

The Pillars of AI Predictive Maintenance


OneWatt uses its Embedded Acoustic Recognition Sensors (EARS) device to “listen” to motors, detecting and predicting faults before they happen. Mueller Industries also uses a predictive maintenance solution based on sound analysis. Any change in the tone or vibration level is analyzed in real time to check whether it may be a sign of impending malfunction.


For example, Thermoplan’s smart coffee machines monitor customer data in real time, including statistics about the amount and types of coffee used, and cleaning cycles vs. recommended intervals, enabling effective predictive maintenance analytics. The same technique can be used across the entire IoT spectrum, from smart home appliances to automotive analytics and beyond.


FANUC, for instance, developed a software program that collects images from cameras attached to robots, and then sends them to the cloud for processing in order to identify potential production problems before they occur.

TechSee’s computer vision platform, on the other hand, enables automatic visual identification of consumer devices and their issues/resolutions from images or video, enabling proactive customer assistance.

The Benefits of AI predictive maintenance in B2C

  • Reducing truck rolls: Visual Support uses screen-based technology that allows agents to see the customer’s physical environment via their smart device, identifying any issues, enabling better remote resolution and reducing the need for technicians to be dispatched to a home or workplace.
  • Avoiding product returns: a 2019 survey found that 72% of customers believe proactive customer assistance would dissuade them from returning a product they found difficult to install or operate.
  • Avoiding downtime: Volvo’s Early Warning System uses data to predict each part’s breakdown rate and notifies customers when cars need to be serviced and what parts need to be repaired or replaced before they break.
  • Reducing potential safety hazards: integrating predictive analytics with data from various sources such as SCADA, CIS, EAM-GIS, weather channels and online monitoring systems enables utility companies to proactively address possible safety risks.
  • Improving customer satisfaction: consumers expect to be notified about interruptions of service, device malfunctions, warranty expirations or billing hiccups. Collecting and proactively acting on data insights helps achieve improved customer satisfaction.

Where it’s all heading

The addition of AI capabilities enables companies to predict not only the life of devices, but identify how they can use the data to positively affect customer’s experiences. Integrating predictive maintenance with contact center operations to provide proactive customer assistance delivers a resounding win-win.

This article was first published on the TechSee blog.

TechSee revolutionizes the customer support domain by providing the first cognitive visual support solution powered by augmented reality and AI.