The telecommunications industry is evolving rapidly, and artificial intelligence (AI) is playing a pivotal role in shaping its future. Clear, uninterrupted service is the key to growth. The quality of the customer experience has long been a differentiator, but existing networks were never meant to support current traffic volumes.
Business leaders are under pressure to transition to 5G and beyond while simultaneously evolving their networks from a cost center to a profit center. As a result, all carriers are now looking to leverage innovation and technology to improve the customer experience, optimize network efficiency and performance, enhance efficiency and drive revenue.
Here are eight essential AI use cases in telecom that demonstrate how carriers can leverage AI and other technologies going forward.
A network that’s not working is a network that’s not earning money. Constantly optimizing existing networks has therefore become an operational competency for all network operators. All carriers have deep historical data on every aspect of their network performance. The right combination of AI, data science, machine learning, cloud and edge computing will enable them to leverage to its fullest extent.
When carriers combine the right technologies in the right ways, the future of AI in telecommunications is incredibly bright. Using custom tools, advanced dashboards, and centralized access to key network metrics and measures for remediation. These AI-enabled tools can even begin to conduct root-cause analyses and provide recommendations on how to make precise adjustments to antenna placement, power, tower height, frequency and more to keep the network performing at its peak.
Anticipating failures before they happen also lets carriers provide swift remediation in the field and avoid or minimize downtime.
Commercial equipment is typically maintained on an assumptive basis. For instance, an airline assumes the need to replace or service jet engines within a specified Time Between Overhauls (TBOs). They plan to briefly remove each engine from service within that TBO, and the number of engines that are out of service—and not driving revenue—affects everything from ticket prices to departure times.
Until recently, telecom carriers have operated their networks on a similar basis. But combining the right technologies can enable them to shift to predictive maintenance, in which they leverage the vast stores of data that reflect how their infrastructure components are actually being used. Predicting failure rather than assuming it enables operators to maximize the life of each asset. Nothing is removed from service while it still has significant useful life, and nothing stays in service long enough to fail.
This enables the telecom carrier to maximize network uptime, plan for CapEx and OpEx spending, and drive efficiency.
Telecom fraud has been a challenge since the industry began. Today, AI-enabled tools let carriers stay ahead of malicious actors’ evolving tactics. They also enable constant monitoring to identify when bots are using your network, prevent malicious actors from accessing personal customer information and other sensitive data, and prevent other unauthorized access.
When paired with the right mix of other technologies, typically Internet of Things (IoT), data and cloud, AI-enabled tools are ideal for constantly monitoring your network and infrastructure. These regular audits and risk assessments let you monitor call traffic and usage patterns to detect suspicious activities and irregularities so you can respond to incidents more quickly.
Once AI finds the holes, you can patch them quickly and update encryption protocols, data-storage practices and disaster-recovery plans to safeguard sensitive information, eliminate vulnerabilities and harden your infrastructure. This enables you to minimize financial losses, avoid reputational damage, and maintain legal and regulatory compliance.
A network that’s not working is a network that’s not earning money.
Combining machine learning (ML) and AI with natural language processing (NLP) and conversational search powers chatbots and other virtual assistants that already handle routine customer inquiries. This requires the carrier to determine the ideal balance of human skills and machine capabilities, but once that’s done, this powerful combination can free up human workers to take on more complex and valuable tasks.
AI use cases in telecom can go beyond just a standard chatbot that puts people in a queue. In many cases, telecom companies can use AI to handle a large amount of customer service issues, keeping your employees free for the bigger escalations. Adding retrieval-augmented generation technology empowers bots to leverage a far greater range of internal documents to serve customers in even more sophisticated ways, yet still return answers in conversational formats.
Beyond just chatbots and customer service assistants, a strong customer data platform (CDP) enables marketers to create customer journey maps and update them in real time. Coupled with the right analytics program, a good CDP will let the carrier understand not just what the customer is doing, but why they’re doing it and what they’re likely to do next. With that insight in hand, marketing teams can tailor promotions and offers to drive upsells and cross-sells.
Customer service and related issues are a constant struggle. Some carriers have successfully differentiated their entire brands purely on customer service. Now carriers can begin to leverage AI’s ability to parse vast data sets in near-real time to improve their customer relationship management (CRM) efforts.
When coupled with the right data and hyper automation, the right CDP and real-time customer journey maps can automate lead scoring and prioritize the potential value of each customer by predicting their cross-sell and upsell potential.
Carriers can then use this insight to improve the customer experience, prompt employees to better serve customers with next-best actions, enhance interactions everywhere the customer meets the brand, predict and reduce churn and recommend personalized solutions.
It can also enable conquesting and re-conquesting campaigns, help carriers win back lost customers and drive repeat business.
When brands are doing well, social media can add vast amounts of value and drive revenue consistently. But if a problem crops up that the brand is unaware of and that’s shared virally, the negative impact can be vast.
AI-enabled social-listening tools crawl the Internet searching for sentiment about the brand, both good and bad. These tools can gauge customer satisfaction from social media, reviews, and other feedback, identify issues before they escalate, and suggest remedies that the brand can take to not just fix the situation but share what they did and regain or rebuild sentiment in ways that strengthen the brand.
The newly increased demand for high-speed mobile data services and the rapid expansion of mobile networks have placed immense pressure on telecom base stations. Continuing rollouts of enterprise 5G technology has also increased the need to improve capacity and coverage.
The high cost of base station equipment and the need for skilled professionals to deploy and maintain these systems create an ideal use case for AI-enabled tools. From deciding where to place base stations to optimizing their power consumption, carriers can achieve tangible business outcomes with AI and keep both single-band and multi-band base stations running at peak efficiency.
By leveraging AI, we not only predict failures but maximize the life of each asset, ensuring nothing is removed from service while it still has significant useful life.
Planning network growth is a strategic exercise; it determines the carrier’s future direction. As customer needs grow, so must networks. With that, their infrastructure must grow as well.
But where are the optimal locations? How will the new network allow for loads to be managed and balanced? And how should the network be expanded to allocate resources efficiently? These questions make network planning and optimization a key use case for AI in telecommunications.
Leveraging the transformative power of AI-driven models lets project teams parse vast amounts of data, enable leaders to make smarter decisions, and create digital twins that simulate real-world operations to test and refine their decisions. The result is a comprehensive set of roadmaps to guide the tactical execution of fiber rollouts, capture astonishing value, and highlight opportunities for the most strategic expansion possible. We have a telecom strategy case study that exemplifies how we helped a company harness AI in telecom and evolve their business.
As the world demands greater and greater connectivity, network operators have an opportunity to evolve and build networks intelligently by using AI and digital twins to analyze and act upon vast amounts of data. Doing so will enable network decisions that resonate positively across the network for years to come.
Sand Technologies has a long history of supporting sector leaders in every aspect of their business. Contact us today to learn more about how we can partner with you to take your networks and operations to the next level.
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