Despite the endless stream of headlines about groundbreaking artificial intelligence (AI) innovations, it’s been barely a year since the launch of ChatGPT. Which means we’re still at the very beginning of the generative AI revolution. That said, machine learning and AI have long been improving operations for enterprises around the world. Regardless of industry or application, the areas where AI is proving most powerful tend to share three common ingredients. These use cases:
- Generate vast amounts of data for AI models to train on
- Feature routine, repetitive functions that are ripe for automation
- Employ largely software-driven processes, creating opportunities for AI to perform ongoing closed-loop reprogramming and optimization
If you work in the telecom space, these characteristics should sound familiar. Modern telco networks, and the operations supporting them, encompass all three. Connecting thousands of devices and millions of users, telco networks generate oceans of data to analyze and train on. Common operational practices—from network lifecycle management to service assurance to capacity planning—involve thousands of hours of repetitive, manual-intensive tasks. And almost all of those tasks involve executing software operations. It all points to an inescapable conclusion: As AI velocity continues to build, communication service providers (CSPs) are excellent candidates for AI-enabled transformation.
How will AI transform the telecom space? Where are the biggest opportunities, and where should CSPs focus their attention and investment? These are among the biggest questions facing the industry today—questions that are too big to explore in a single blog. In this first installment of a multi-part series, let’s start with an overview of why AI holds so much potential for CSP transformation.
Revisiting Why AI Matters to Telecom
It’s a good thing that telco networks and operations are so well suited to AI, as CSPs have significant challenges they need to solve. AI holds the potential to address many of the biggest. By applying AI to telecom networks, CSPs can:
- Drive down costs and complexity: With rising costs for network upgrades and largely flat revenues, CSPs must find ways to lower costs. Technology offers solutions, but finding the skilled people needed to implement them remains a huge industry challenge. With AI, CSPs can reduce operational expenses (OpEx)—as well as pressure to compete for highly skilled workers—by automating many network tasks, and empowering existing engineering and operations teams to do more with less. AI can also reduce capital expenditures (CapEx) by extending the life of network equipment and optimizing resources, especially in radio access networks (RAN).
- Increase revenues and competitive differentiation: Through AI-driven automation, CSPs can dramatically reduce costs and timelines when introducing new revenue-generating services. AI can automate the complex network and operational models needed to roll out game-changing service capabilities, such as network slicing, mass-scale Internet of Things (IoT) connectivity, and low-latency extended reality (XR) applications. CSPs can also capitalize on the massive amounts of network and user data generated in telco networks to better understand their customers and more accurately predict market behavior.
- Improve sustainability: As operators commit to aggressive initiatives to reduce CO2 emissions, they face a monumental technical challenge. At the scale of service provider infrastructures, it’s enormously difficult to measure emissions across thousands of devices and locations, track the mix of green and traditional energy sources, or implement new operational patterns (such as shifting workloads to different locations or energy sources in real time). AI is tailor-made for analyzing such complex systems, and enabling incremental improvements that cumulatively add up to huge wins.
It’s had to overstate the transformative potential of these AI capabilities. Analyst STL Partners estimates that the average telco can realize US$1.3 billion in value per year from AI and automation technologies, equivalent to 8% of their annual revenue. Much of this value—approximately 70%—will come in the form of US$891 million in combined network CapEx and OpEx savings annually.
The Race to Maximize AI Value
The potential to achieve truly transformative benefits like these isn’t lost on telecom providers. No one is more keenly aware of the industry’s difficult financials, operational complexity, and industrywide skills shortage than CSPs themselves. More and more telecom leaders see AI as a potential cure for these industry challenges. They increasingly view AI as essential to succeed in the emerging technology landscape. Indeed, the risk of getting left out of the AI revolution is among CSP leaders’ biggest concerns.
No surprise then, forward-looking CSPs are already taking major steps in this space. Some are collaborating to accelerate adoption of AI to transform business operations. The Global Telco AI Alliance, for example, aims to develop an industrywide Telco AI Platform to advance new AI-driven services. As early telco adopters use AI to cut costs and grow revenues, the rest of the industry will have to join in or get left behind.
Meanwhile, the field of AI itself continues to rapidly mature. As more businesses adopt AI, telco customers will come to expect the more personalized, higher-quality experiences that AI enables—even as telco networks grow exponentially more complex. Bottom line, the time for CSPs to double-down on building AI skills is right now.
The Telco AI Opportunity
Where should CSPs look to apply AI in their businesses? STL Partners segments the telco AI opportunity into four overarching categories:
- Network Optimization: These solutions use AI to enhance the performance, efficiency, and sustainability of telco networks. That can include AI-enabled automation of operational tasks, such as lifecycle management of network functions, and predictive maintenance to extend the life of network equipment and resources. In 5G radio networks in particular, RAN Intelligent Controller (RIC) platforms are proving to be hubs for AI innovation, such as powering down cell site components when not in use, and monitoring and optimizing spectral capacity.
- Network and Service Assurance: AI can help CSP operations teams dramatically accelerate troubleshooting and problem resolution, improving network reliability. AI and machine learning models can identify anomalies and perform root cause analysis (RCA) far more quickly than human beings, and fix many problems autonomously. AI-driven predictive maintenance can improve overall network performance and reliability. And leading telcos are already incorporating generative AI (GenAI) to improve customer support interactions.
- Network Planning: AI can help CSPs optimize infrastructure and capacity planning to keep pace with evolving demand. Operators can use network Digital Twins to stage planned changes and understand how they’ll affect the network and users before implementation. CSPs can also apply AI-driven predictive analytics to better understand customers, identify the best markets to launch new services, and optimize ongoing capacity planning.
- Service Innovation: Finally,AI makes it much easier to develop, implement, scale, and manage next-generation services, as well as optimize existing services. Already, leading telcos are exploring the use of AI to accelerate the rollout of network slicing and automate management of edge computing resources.
In upcoming blogs in this series, we’ll take a deeper dive into each of these telco AI opportunities, and explore how VMware is helping to enable them.
Unleashing Telco AI
VMware is an ideal partner to help CSPs capitalize on the AI revolution. In fact, we already provide several of the key ingredients for successful AI implementations, including:
- Multi-cloud extensibility: As a cloud-agnostic vendor, VMware is ideally positioned to provide unified visibility and management across disparate hyperscale and hybrid clouds—reducing the risk of getting locked into one vendor’s AI ecosystem.
- Advanced ML intelligence: VMware already employs some of the industry’s most sophisticated ML capabilities. The ML algorithms in Telco Cloud Service Assurance, for example, collect vast amounts of network data for training AI models, providing a huge head start in analyzing telco networks and accelerating RCA.
- Industry-leading RIC ecosystem: VMware’s groundbreaking RIC platform is bringing third-party innovation to telco networks, with more application developers supporting our platform than any other vendor. That includes 14 AI applications to date to optimize RAN traffic management, energy consumption, and more. And our large, growing ecosystem of partners, such as Nvidia and Intel, can help CSPs ease implementation and maximize the value of their AI investments.
- Groundbreaking Private AI solutions: Amongthe biggest barriers to broad adoption of AI solutions is organizational concerns about the privacy of their data. That’s why VMware is once again leading the industry in advancing Private AI. We offer GenAI solutions with embedded security, auditability, and access control that allow enterprises to maintain strict governance and control over their data, even across multiple private and hybrid clouds. And we’re continually folding these and other innovative AI capabilities into our Telco Cloud offerings, so that CSPs can capitalize on them as well.
At VMware, we appreciate the immense potential of AI for telecom, and we’re committed to making sure our customers can take advantage of it. In upcoming blogs, we’ll take a closer look at the most exciting innovations in this space, and explore how CSPs can use them to stay on the vanguard of the AI revolution.
