Artificial intelligence (AI) adoption in business has tripled across the board in the last twelve months, with an impressive 80% of business leaders claiming that integration can both boost productivity and create jobs. Unsurprisingly, countless industries are looking towards adoption in 2020, and the telecommunications industry is no exception.
In the past year alone, upwards of 60% of telecom companies have implemented AI or machine learning (ML) of some kind. That number only looks set to increase. With maturation in AI technology and growing quantities of data swamping manual processes, 2020 might be a tipping point for AI adoption. AI can simplify processes, improve customer experience, and keep your products and services at the forefront of a fast-growing telecom industry.
But even in the midst of adoption trends, there is still widespread confusion around definitions and what AI/ML can actually deliver. Before AI can bring any benefits to your business, it’s vital that you take the time to understand how communication service providers (CSP) at the top of the telecom game are utilising AI right now. To get you started, we’re going to look at five top use cases.
# 1 - Predictive analytics
The amount of data created and copied annually has continued to double in size every two years. Predictive analytics have become a key feature in many business settings. Telecommunication services, in particular, sit on the conflux of huge quantities of data, and are in a position to apply AI-empowered analytics to make powerful predictive decisions.
By looking at their own customer habits, CSP’s can make predictions on the types of services different customers are likely to want, which channels and touchpoints individual customers will find most convenient, and what motivates change. These outputs can be used to upsell, cross-sell, retain customers, improve experiences and optimise investments.
By turning to predictive analytics that encompass data mining, predictive modelling and machine learning, telcos are in a great position to extract meaningful insight without having to worry about continually growing workloads. As well as simplifying processes, implementation here can guarantee that companies are always mining the right data for actionable goals that they can track moving forward.
Ultimately, this category of AI application (predictive analytics) applies to almost everything else on this list. The main use case for data and AI is to identify predictive patterns and guide decision-making. Some of the more specific use cases are worth exploring. However, this is such a critical bedrock of AI that it deserves a category all on its own.
# 2 - Predictive maintenance
Large telecoms oversee vast quantities of infrastructure. Maintaining and replacing those assets is expensive and time-consuming, and keeping those assets optimised is critical to customer satisfaction and business profits. Applying data and analytics to maintenance is a central focus for many telcos, and AI is paving the way for significant improvements.
Industry frontrunner AT&T is leading the way here, with a recent drone incentive that aims to improve network coverage using predictive analysis of video footage for the enhancement of tower maintenance. And they aren’t alone. Verizon also offers a similar service, which they refer to as ‘condition-based maintenance.’
Telecom frontrunners can use AI systems like these to oversee network operations and their risks in real-time with no increased effort or cost. This is essential for behind-the-scenes telcom efficiency that plays a key role in this age of continual connectivity. Rather than dealing with issues like downtime, such capabilities make preemptive action possible and, more importantly, a breeze to achieve.
# 3 - Network optimisation
As well as helping to reduce workloads where maintenance is concerned, efforts like the AT&T incentive mentioned above are taking companies further in their ability to improve network optimisation. That’s because, as well as spotting issues as they arise, the use of AI in this area ultimately aims to reconfigure and adapt network needs regularly. Pairing this with analytics means that telecoms can foresee and prevent problems as they occur and move forward.
The implementation of self-organising networks (SONs), in particular, offers great promise for telecom interventions. Fuelled as they are by artificial capabilities, services like these offer opportunities to both design networks and self-analyse existing ones for optimisation at all times. As well as ensuring that companies can improve customer satisfaction ten-fold, the extra mile offered by AI-led optimisations like these looks set to become a key indicator of success in this competitive market.
# 4 - Customer Support
While customer experiences play a part in every artificial intelligence system commonly used in telecoms right now, a targeted, AI-led focus on customer support is also taking companies further. Availability and service still matter in the modern market, after all, but long wait times and dissatisfaction are inevitable for companies that still try to take this matter in hand.
That’s why businesses from Spectrum to AT&T are doing what they can to implement intelligent processes that take the pain out of support for both parties. Spectrum, for instance, makes use of a virtual assistant that’s equipped to handle troubleshooting queries and more. AT&T has taken this one step further, with an ‘Atticus’ chatbot that works through the already popular Facebook messenger platform.
Of course, even telcos still need to offer the opportunity to speak with a member of staff for complex queries as necessary. What AI-enabled services deliver is two-fold. First, they ensure faster response times and on-demand, multi-channel access to simple support. Second, they allow customer service agents to focus their expertise on complex problems and customers who want a ‘human touch’. Instead of handling basic support requests, such intelligence frees your team to deal with challenges that they may have struggled to process promptly beforehand.
# 5 - Robotic process automation (RPA)
Last but by no means least, telecom leaders are championing the implementation of robotic process automation (RPA). After all, as well as a focus on customer service, AI is all about simplifying workplace processes. And, RPA is one of the leading ways to do that right now. In fact, a survey by Deloitte recently revealed that 40% of telecom executives reported enjoying significant business benefits from investing in this area.
As it sounds, RPA simply involves automating basic processes that telcos have always managed on a person-by-person basis. Most importantly for improvements, companies are focusing on the automation of processes that are increasingly at risk of falling foul to human error as customer numbers and data amounts grow. Generally, this comes down to streamlining incoming data and the extraction of critical information.
There are, of course, always going to be tasks that are best done with a human hand behind them. But, by removing repetitive jobs from your telecom team’s shoulders, you should find that productivity soars, and results really start to come your way.
Getting started with AI in 2020
The benefits of processes like those mentioned above are self-evident, and these are just some of the AI capabilities that telcos are utilising at the moment. On the whole, spending in this area is on the increase, and you could benefit from joining the $11.2 billion telco-AI market. The only question left to ask is — how do you get started on your first AI project?
For a company on the brink of change, the broad scope of AI in the field right now can feel like more of a curse than the blessing that it is. It can certainly be challenging to determine where you should spend the most money and why.
Ultimately, assessment of your efforts as they stand will become key to your success here. Only by analysing the data that you have coming in, or entrusting a team experienced in AI implementation to do so for you, can you settle on reliable business goals and priorities moving forward.
This, paired with knowledge of what’s already working for industry top performers, is your best chance at finding the ideal AI pairings to boost your processes and profit margins as you move into the future of telecommunication.
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