4 ways telecoms should be using A.I. and automation
Telecoms is going through a unique time at the moment. The surge in demand for digital comms and emerging technologies like 5G are great in terms of driving systems to become more powerful. The trouble is, the volume and complexity of data that operators now have to manage is also surging.
This creates a new commercial challenge, as telecoms companies try to balance processing greater volume at greater speeds, without hiking prices for customers. In other words, telecom capabilities (and customer expectations) are advancing, but the prices paid by customers per GB or minute of airtime are not as great as they should be.
So how do telecoms companies counter this predicament?
They apply some smart technologies of their own to digitize human activity in as many functions as possible. In other words, they turn to A.I. and automation.
Since this is well within our field of expertise, let’s dive into what we at Intelygenz see as the top four uses of A.I. in telecoms right now:
As the name suggests, AIOps is artificial intelligence applied to IT operations. That means taking the big data processing and analytics power of A.I. and using it to automate and enhance core IT functions.
When it comes to telecoms, AIOps provides exciting possibilities in overcoming the commercial challenge mentioned above. Perhaps most notably, it gives providers complete and instant visibility of the traffic flow throughout their networks. Insights from this visibility create a range of new possibilities; from revealing upsell opportunities and informing pricing strategies, to providing customers with self-service capabilities.
02 Virtual Assistants
Speaking of self-service capabilities, telecom companies can take customer support to new heights with conversational A.I. – or what we’ll call in this context, Virtual Assistants.
Capable of handling the often standardized and repetitive technical issues faced by customers, Virtual Assistants can perform tests, run system checks, provide service information and status, log incidents, and much more.
This means human customer service operators no longer need to be overwhelmed by the ever-growing number of support requests for everything from installation and set up, to troubleshooting and ongoing maintenance. Instead, interactions can be completely automated, saving the service provider invaluable time and ensuring customers get their problem solved much quicker by easily doing it themselves.
03 Predictive maintenance
Telecoms businesses on a healthy growth trajectory will likely see an increased pressure to maintain their products and services at an increased volume. Fixing issues begins to cost more, take more time, incur downtime and sometimes cause service interruption.
A.I. has the ability to ease these growing pains through predictive maintenance. Machine Learning algorithms surface patterns in historical data and use them to anticipate and warn about hardware failures before they even occur. This makes the business exponentially more proactive in preventing disruption to customers and operations.
For unavoidable failures, A.I. can also be used to diagnose root causes, allowing businesses to address and contain core issues before they cause further impacts.
04 Fighting fraud
Fraud is a never ending challenge for today’s businesses, with cyber attacks continually becoming more frequent and sophisticated. The telecom industry is far from immune to this, and companies must take proactive action against malicious attacks.
The analytical capabilities of A.I. makes it an invaluable tool in this effort. Instead of being reactive to incoming malicious attacks, telecom companies can use it to predict and therefore reduce fraud risk from impacting their business. 100% prevention is impossible, but by automating both the detection of suspicious activity and fast actions such as blocking users or services, companies can minimise operational, financial, and reputational damage before attacks take hold.
With the exception of AIOps, these uses may not directly work to reduce the volume of data that telecoms companies have to handle, but the sheer scale of value that can be created across the organization goes some way to regaining a balance.
Another point to consider, is that no matter the use, A.I. and automation solutions are centered around data – and lots of it. The chances are, your business is likely already sitting on a high volume of data that could be utilised without overhauling the systems you already have in place.
That’s because, when designed correctly and specifically for your business, A.I and automation can work to enhance processes that are built around your existing tech stack.
We show a great example of this with the TNBA project we did for MetTel, where they used A.I. enabled process automation in their incident management process flow for enhanced ticket resolution.
We’ll be keeping a close eye on how A.I. and automation in the telecommunications industry evolves throughout 2022, so watch our social channels for updates, insights, and the occasional free resource!
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