Telecommunications companies are reinventing themselves as agile telcos – adapting quickly to market needs, deploying new services faster, and continuously innovating. A key driver of this agility is the intelligent use of artificial intelligence (AI) and data. By infusing advanced analytics and machine learning across operations, networks and customer touchpoints, forward-looking telecoms operators are transforming their businesses.
The result is not only improved efficiency and cost savings, but also faster go-to-market and more personalised, high-quality customer experiences. In part 1 of our 2 part agile telco description series, we explore how AI and data are enabling the agile telco model, with use cases of 5G network optimisation.
Transforming operations with AI-powered automation
One of the core promises of AI in telecoms is streamlining operations through automation and predictive analytics. Communications service providers (CSPs) are increasingly investing in AI solutions to drive network automation and improve operational efficiency.
In network management, AI can automate routine and time-consuming tasks – such as monitoring network performance, detecting faults and adjusting parameters – that were traditionally done manually. Machine learning algorithms can analyse network data to predict and preemptively address issues before they impact customers. This predictive maintenance capability means networks can self-heal or alert engineers to fix equipment before a failure occurs.
In practice, this reduces unplanned downtime and truck rolls, cutting maintenance costs and improving service uptime. Indeed, Ericsson states that future telecoms approaches are expected to feature “integrated solutions, agile troubleshooting processes, and a proactive approach to service issues” that improve efficiency, reduce operational expenses and increase customer satisfaction. In short, AI-driven operations make a telco far more nimble in responding to problems.
A powerful example of AI operations comes from Vodafone. Vodafone Business has deployed an AI-enhanced service management platform in partnership with ServiceNow to create “the next era of AI-powered service management.” The system gives Vodafone a single view of customers’ networks and applications, enabling faster troubleshooting and incident resolution. By licensing its in-house automation code to ServiceNow’s telecoms software, Vodafone is effectively turning manual service desks into automated, data-driven systems. This kind of end-to-end incident management powered by AI illustrates how telcos can respond to network events or customer issues with greater speed and agility than ever before.
The benefits of AI-automated operations are significant for agility. Tasks that once took hours or days can be handled in real-time or proactively by algorithms. AT&T, as another example, has used AI to automate aspects of its network operations center, reducing the need for manual interventions. AI systems monitoring cell sites and fibre links can identify anomalies or degradation early, then trigger automated workflows to reroute traffic or schedule maintenance.
This ability to swiftly adapt to network demands and streamline operations ensures that CSPs manage resources more effectively and can respond immediately to changes. The net effect is lower operating costs, more reliable services and teams that can focus on innovation instead of fighting fires.
Optimising networks in the 5G era
Perhaps the most game-changing impact of AI and data is in network optimisation, especially with complex 5G networks. Telecoms networks are growing more dynamic and software-driven, with 5G introducing features like network slicing, massive MIMO antenna, and IoT connectivity at scale.
Managing this complexity and tuning the network in real time is a task tailor-made for AI. Accordingly, operators are deploying AI to continuously optimise radio and core networks – improving performance, efficiency, and enabling new 5G capabilities that drive business value.
An example comes from Rakuten Mobile in Japan, which built a cloud-native 4G/5G network from scratch. Rakuten has hundreds of thousands of cell sites, and every day the antennas on those sites automatically adjust their tilt, angle, and power output based on factors like climate and traffic load, using AI algorithms. These autonomous adjustments optimise coverage and capacity as conditions change, ensuring users get the best possible signal.
Rakuten’s network essentially “acts as one machine” – centrally analysing data from all cells and making coordinated optimisations – which dramatically improves service quality across the network. This kind of self-optimising network is a cornerstone of the Agile Telco: it can respond immediately to changing demand (for instance, a sudden crowd at a concert or a spike in streaming during a big sports event) without waiting for human intervention. The result is a more consistent user experience and more efficient use of network resources.
Beyond radio optimisation, AI-driven network automation is helping operators manage 5G’s complexity. Carrier networks generate enormous amounts of performance data (from base stations, routers and switches), far beyond what manual analysis can handle. Machine learning models can sift through this data to detect anomalies, predict equipment failures, and dynamically reroute traffic.
For instance, if a 5G cell site’s throughput starts degrading, AI algorithms might diagnose whether it’s due to interference, hardware issues, or a misconfiguration – and then take action, such as adjusting frequencies or handing off users to neighboring cells. As noted earlier, CSPs are finding that such AI systems can predict and preemptively address network issues, which improves reliability and reduces downtime. In practical terms, this could mean AI forecasting that a particular router will exceed capacity in a week and automatically spinning up additional virtual network functions to handle the load – avoiding a bottleneck before it ever affects customers.
“Network slicing is another area where AI unlocks huge value”
5G allows operators to create multiple virtual networks on the same physical infrastructure, each tailored for specific use cases (e.g. an ultra-reliable low-latency slice for autonomous vehicles or a high-bandwidth slice for HD video streaming). Managing these slices optimally is complex, and AI is pivotal for it. AI-driven orchestration can forecast demand on each slice and allocate resources accordingly in real time.
It also facilitates the creation of on-the-fly slices for new services. For example, a telecom company might use AI to rapidly provide a dedicated slice for a big industry event or an enterprise customer, configured with the precise quality parameters needed – an automated service provisioning feat that previously took weeks of planning.
According to industry analysis, AI-enabled automation can facilitate customisable network slices for various industry needs (from smart cities to remote healthcare), allowing operators to offer differentiated 5G services at premium rates. This capability is key to monetising 5G: by assuring performance through AI, telcos can confidently sell SLA-backed slices and innovative services that generate new revenue streams.
Importantly, AI-based network optimisation is not just about performance – it also yields cost and energy efficiencies. For instance, AI can intelligently turn off or scale down parts of the network during low-usage periods (e.g. overnight) to save power, then reactivate them as traffic rises. It can also optimise backhaul routing or core network function placements to minimise latency and bandwidth use. These optimisations can reduce operating costs and even support sustainability goals (by cutting energy consumption), all while maintaining service quality.
AI and data analytics give telcos a “brain” for the network – one that constantly learns and adapts.
In summary, in the 5G era, where networks must handle unprecedented device density and diverse service requirements, this AI brain is essential for an agile telco to deliver on 5G’s promises. Network automation and AI thus go hand-in-hand: as one report put it, strategic implementation of AI for network automation and 5G differentiation is becoming critical for operators to reduce costs and drive growth.
This is just the beginning for Agile Telco and there is more to discover about this movement in telecommunications. Subscribe to the Agile Telco channel on TechLed to see what we are going to discuss in the second and last part of the Agile Telco description series.
Marion Webber