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How utility companies can leverage AI and RPA to strengthen their enterprise mobility management

image credit: © Tero Vesalainen | Dreamstime.com

Advances in digitalisation now means that utility companies can work smarter, faster and more efficiently than ever before. This also extends to how they manage their mobility devices. However, many companies within the sector are still adopting basic legacy mobile device management (MDM) solutions and strategies to manage thousands of work devices on their networks.

This simply isn’t enough as mobile device failure is still deemed a massive cause for concern amongst field workers with over half of them experiencing at least one mobile device issue per month that inhibits their ability to carry out their job roles. Utility companies often have to manage thousands of mobile devices used by their field workers, and are using MDM solutions to perform the basic tasks of provisioning and updating these mobile devices and associated apps as needed. These MDM solutions, while adequate for making changes, are woefully inadequate for seeing issues in real-time affecting field workers. Since issues in the field can often mean the worker is unable to complete their tasks using their mission-critical mobile device, the industry is yearning for something more effective. Emerging tech such as Artificial Intelligence (AI), and in particular Robotic Process Automation (RPA), is showing particular promise in the field of enterprise mobility. It can help utility companies to do much more than just simple MDM.

Going beyond simple MDM with RPA

RPA is a facet of AI that can extract and process different types of data to provide actionable insights. This can streamline business processes and automate the more mundane and repetitive tasks. This kind of data automation is what’s driving the potential of utilising RPA technology in enterprise mobility.

Managing enterprise mobility is not just knowing the number and the locations of company devices - it’s about so much more. AI and RPA can enable utility companies to go beyond basic MDM by providing their IT teams with high level intelligence on their enterprise devices such as geolocation, battery health status, as well as device performance and usage – all with little to no human interference. So how exactly does this work and is it tangible to achieve?

It is worth thinking about how AI and RPA can be used to identify emerging patterns in mobile device data. Mobile device data patterns can reveal unique insights into how efficiently a mobile operation is run. RPA technology can be implemented on top of most application programming interfaces (APIs) that can provide access to analytical results on mobile data from devices within the network. Once implemented, the technology can use APIs to directly query the available mobile data and autonomously act upon the intel.

For instance, the RPA automation systems will firstly capture and process the data unique to each mobile device. Through data pattern recognition, the health and usage of each mobile device will be monitored with a trigger rule that calls for real-time alerts when devices or apps exceed expected thresholds.  As examples, this level of real-time actionable insight and results can make troubleshooting poorly-performing batteries, or excessive reboots due to an application update, a seamless and efficient operation. The key thing here is how you act upon the available mobile data analytics.

Placing data analytics at the heart of your enterprise mobility strategy

Utility workers are key to ensuring that everyday people feel safe and secure in their homes and places of work, therefore any disruptions or downtime to their work could be costly from both a business and customer service perspective. Despite the obvious benefits of using RPA technology to collect valuable device data insights, some IT teams often see it as the cure as opposed to the prevention of device issues. Many are yet to realise that AI and RPA empower IT teams to keep close tabs on the health of all their company devices. What’s more they also enable them to act quickly and efficiently to mitigate any disruptions to services caused by device issues.

Real-time mobile device data can reveal valuable insights into device battery health, device functionality, geolocation, and generally help run a far more efficient enterprise mobility network. This means that companies will be able to monitor the health status of their devices and will be able to proactively spot any anomalies in device function. Any device that is running low on battery, requires a software update, or is experiencing a software malfunction, can quickly be identified, and proactively managed before any potential device malfunction or battery failure affects the field worker.   The process is completely autonomous and requires little to no human interference to gather information as AI and RPA enable the system itself to spot anomalies and issues. This is a far cry from the simple management of mobile devices that many utility companies currently rely on today using just MDM.

With the adoption of AI and RPA, utility companies can go beyond simply ensuring the basic management of a mobile deployment. The technology creates a self-sufficient system that can augment the efforts of IT teams in managing enterprise mobility devices in a more proactive manner.

In today’s enterprise mobility landscape, data is king. In many ways it is what’s driving digitalisation. Therefore, to harness the full potential of AI and RPA, companies need to look at mobile device data in a different way. An effective enterprise mobility strategy or system is only as good as the data it utilises.

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Gary Lee's picture

Thank Gary for the Post!

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