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AI in Telecom: 5 Popular Use Cases to Follow

Ai In Telecom
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Last year, we published a blog discussing SIP and AI integration. In that blog, we touched on a few of the ways artificial intelligence has impacted Unified-Communications-as-a-Service (UCaaS) providers. But the technology’s impact goes much deeper.

The global market size for AI in telecommunications reached an estimated US$1.34 billion in 2023 and is projected to reach $42.66 billion by 2033. Key benefits of the technology include streamlined operations, improved network efficiency, and an elevated customer experience.

A recent NVIDIA survey, for instance, found that 95% of industry stakeholders and 100% of telecom decision-makers are engaged with AI, with 65% of total respondents agreeing that AI is important to their company’s success.

Let’s explore a few of the ways these companies are leveraging AI technology — and some of the problems it’s helping them solve.

Solving the Biggest 5 Pain Points in Telecom

Although the sector has evolved considerably over the past decade, the challenges facing telecom businesses have remained relatively constant:

  • Network management: Maintaining a telecom network is complex, often requiring specialized equipment, expertise, and software. Network complexity also increases exponentially with scale.
  • Inefficient data utilization: Telecom companies generate vast quantities of data, but many of them lack an effective method of orchestrating and leveraging it. Data is often fragmented, siloed, and difficult to analyze.
  • Robocalling and spam: Robocalling has always been a huge pain for telecom companies, particularly in the US. Unfortunately, the problem is only going to get worse as spammers begin leveraging new technologies and techniques.

AI has the potential to help companies overcome these challenges while also unlocking new revenue streams and cost efficiencies.

Future Ai Telecom Sip Trends

Top Use Cases for AI in Telecom

Even though technology like generative AI is only a few years old, it has already created significant business value for telecom companies, helping them optimize operations to deliver better, more cost-effective services. Below are a few specific use cases businesses can use to achieve these outcomes.

Network Optimization

Pain Point Solved: Network Management

Through AI, telecom networks can be made more reliable and consistent, with reduced latency, better resource management and lower operating costs.

AI tools enable a telecommunications network to autonomously respond to spikes in traffic, intelligently rerouting connections to mitigate network congestion and adding temporary capacity during periods of high usage. An AI-powered network can also function in the opposite direction, reducing energy and resource utilization during low-demand periods.

AI can also help a company optimize its network architecture when combined with techniques such as digital twinning — by using algorithms to simulate its network, the company gains a better understanding of network performance and can make more informed decisions about its infrastructure.

Advanced Self-Healing Networks

Pain Point Solved: Network Management

Self-healing networks capable of autonomously remediating problems without human involvement aren’t new. Traditionally, however, telecom companies could not deploy them at scale. This has changed through a combination of predictive AI and software-defined networking.

Predictive maintenance is now limited exclusively to replacing failing hardware infrastructure — AI-powered networks can take care of everything else more or less autonomously.

Enhancing the Customer Experience

Pain Points Solved: Robocalling & Spam, Inefficient Data Utilization

Chatbots powered by generative AI are capable of providing round-the-clock customer support for all but the most complex issues, improving both customer satisfaction and turnaround times.

AI can also provide a company’s sales, marketing, and customer success teams with valuable insights that allow them to deliver better, more personalized service. Combining AI and analytics enables more effective data orchestration and greater insight into customer preferences and behavior.

AI will also soon allow telecom companies to replace IVRs with increasingly human-like AI assistants that direct callers where they need to go — or even solve customer problems without the need for human intervention.

AI also has the capacity to automate transactional calls such as appointment confirmations, billing reminders, payment-related calls, and more. This means that very soon, telecom companies will have access to armies of human-like callers that can replace call centers around the world.

Alongside better service, this shift will also greatly increase call capacity, as an AI can manage thousands of simultaneous calls while a human can only manage one.

These AI-driven call centers will also be able to test and change scripts based on data.

Employee Productivity

Pain Points Solved: High Overhead, Inefficient Data Utilization

Using AI, a telecom company can automate and streamline workflows across nearly every department. AI-driven data management also makes it easier to break down data silos, particularly if it also automatically transcribes and summarizes calls.

There is also the potential to apply AI to assist in professional development, offering personalized learning to each employee based on their knowledge, background, and preferences.

Softphone apps may also one day integrate assistive AI capable of managing everything from IVR to caller qualification. This AI will function as something of a personal assistant that answers calls before passing them to an agent, making it easier for the agent to understand and fulfill the caller’s needs.

Sip Ai

Lastly, employees can use AI to gain a better understanding of customer preferences, patterns of behavior, and their own performance.

This will be beneficial in several ways:

  • Proactively retaining customers whose behavior suggests they are at risk of churning.
  • Identifying new revenue and upsell opportunities based on customer data.
  • Predicting and preparing for upcoming industry and market trends.
  • Assessing potential new markets and opportunities for expansion.
  • Identifying key areas where an employee can improve.

Fraud Prevention

Pain Points Solved: Security, Robocalling & Spam

Not all network anomalies are due to network congestion or issues with infrastructure — some anomalies exist due to the actions of bad actors. Through AI, telecom providers can detect, identify, and remediate threats in real time.

AI can also augment the expertise of human fraud analysts, equipping them with the capacity to respond to potential fraud with speed and at a scale that would otherwise be impossible.

AI can help a telecom provider identify suspicious billing patterns, addressing not only fraud but also possible system errors while improving the accuracy, transparency, and efficiency of its billing process.

However, keep in mind that spammers and fraudsters also have access to AI technology. They will be able to transmit thousands of simultaneous calls with AI that can test and change scripts based on data. These calls will also leverage increasingly human-like AI agents and deepfake technology, making spam even more challenging to mitigate.

For example, the Federal Communications Commission recently outlawed AI-generated robocalls due to a spate of deep fake calls using a vocal clone of President Joe Biden. This will likely be the first of many regulatory efforts to curb the rise of increasingly-sophisticated telecom scams. Fortunately, the law is not the only recourse for telecom companies.

Tools that use AI to detect automated spam calls already exist today — and it is not much of a leap to say that they will evolve to also detect AI robocalls.

Telecom Will Only Continue to Get Smarter with AI

AI has revolutionized multiple industries, and the telecom sector is no exception. From smarter network management to better customer service, AI technology has helped telecom companies deliver smarter, more personalized experiences while also optimizing spend.

But even with how impactful AI has been up to this point, we’ve likely only seen a fraction of what the technology can achieve.

There are still growing pains that AI must overcome — and once it does, its potential is nearly limitless.

Frequently Asked Questions

AI in Telecom: Frequently Asked Questions

  • What are the most important considerations for implementing AI as a telco?

  • If you’re thinking of deploying AI within your company, make sure you:

    • Have a clear objective in mind — what do you want the AI tool to achieve?
    • Ensure the AI model will have access to the data it requires to function.
    • Take measures to ensure both privacy and data security.
    • Consider how the AI tool will integrate with existing infrastructure.
    • Train your people on how to use the technology before deploying it.
  • What challenges are holding back AI adoption in telecommunications?

  • At the moment, the AI market remains relatively fragmented, without established standards, limited observability, or an accepted means of addressing bias.

    Before we can see what AI is truly capable of accomplishing, it must first overcome these growing pains. Bias is also a problem with no easy solution — without a complete data set, AI will generate inaccurate or even outright false predictions.

    On an organizational level, integration, implementation, and technical expertise are all challenges that, if not handled effectively, can impede the success of an AI initiative.

  • What's the difference between AI and Generative AI?

  • Traditional AI models are relatively narrow in scope and designed for a specific activity or specialization. They can identify and recognize patterns in data and even output predictions based on those patterns.

    Generative AI(GenAI) is an evolution of the traditional AI model. In addition to being more flexible and versatile, GenAI, as the name suggests, is capable of generating novel content based on what it knows.

    In the context of telecommunications, traditional AI might provide an analysis of a user’s behavior to a GenAI model which could then create or curate personalized content for that user.

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Senior Copywriter
ABOUT THE AUTHOR:
Eric Carriere
Senior Copywriter
Eric is an experienced B2B SaaS copywriter with over a decade of experience working with tech companies in telecom, AI, cybersecurity, and other leading-edge industries. Eric takes a data-driven approach when creating content for Acrobits — blending his extensive telecom experience with his desire to create trustworthy content that's accurate, sharable, and designed for today's busy professionals.
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