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 our article “AI and data: The engines powering the agile telco model”, we’ve discussed the agile telco model with regard to AI-powered automation and 5G optimisation.
In this article, we will explore how AI and data are enabling the agile telco model, with use cases ranging from predictive maintenance to customer personalisation, fraud detection and automated service provisioning.
Personalising customer experience with data and AI
In the Agile Telco model, customer experience (CX) is a central focus – and AI plus big data are enabling a new level of personalisation and responsiveness to customer needs. Telecoms operators sit on a goldmine of customer data (e.g. usage patterns, location, preferences), and they are now leveraging this data with AI to tailor services and interactions. As one telecoms AI officer observed, telcos have access to very “unique and special data” on user behaviour that can drive personalised experiences. By applying advanced analytics to this data, operators can both improve customer satisfaction and capture more value per user.
Personalised service recommendations and marketing offers are a prime use case. Machine learning models analyse subscribers’ past behavior to predict what new plan, handset upgrade, or value-added service a customer is likely to want. This allows carriers to present highly relevant offers – for instance, an international roaming plan offer right before a customer’s trip, or an upgraded streaming package for a video-loving user. These tailored offers improve uptake and customer loyalty.
Similarly, churn prediction models are now common – AI identifies which customers are at risk of leaving by finding patterns in data (such as drops in usage or service complaints) and triggers targeted retention actions. Industry reports indicate that data-driven personalisation and proactive retention tactics have helped some operators significantly reduce churn and increase average revenue per user. In essence, AI gives telcos the ability to treat each customer as a segment of one, delivering bespoke experiences at scale.
Customer service is also being transformed by AI. Many telcos have rolled out AI-powered virtual assistants and chatbots (accessible via mobile app or call center) to handle customer inquiries in natural language. These bots use natural language processing (NLP) to understand questions – from billing issues to technical support – and provide instant answers or troubleshooting steps. Over time, they learn from interactions to improve their effectiveness. For example, Telefónica’s Aura digital assistant (deployed in several markets) can answer questions about a user’s mobile data usage, recommend new services, or even proactively suggest ways to save on their bill, all through a conversational interface.
This kind of AI-driven support not only lowers call center loads but also improves CX by providing quick, personalised help. As Geoff Hollingworth of Rakuten Symphony noted, AI combined with large language models now enables telcos to “speak with customers in a normal, natural language way,” ultimately leading to better network and service quality. In Rakuten’s case, the company has integrated a custom large language model that lets users interact with the network or services using everyday language – a leap forward in customer-friendly design.
Crucially, personalisation extends into the network experience itself. With the advent of 5G and software-defined networking, operators can dynamically allocate network resources per user or application. AI plays a role here by analysing each customer’s usage in real-ime and adjusting quality of service accordingly. For instance, an AI system might automatically detect a mobile gamer and assign them to a low-latency network slice, or identify a VIP customer’s device and ensure top QoS during a video call. These behind-the-scenes optimisations translate into a smoother, more responsive experience for customers – often without them even realising the network was intelligently tuned on their behalf.
By mining the vast data they generate, “telecoms is actually one of the most data-rich industries” according to Geoff Hollingworth, and once that data is harnessed properly, the rate at which they can realise the power of AI is significant. In summary, AI and analytics allow telcos to deeply understand each customer and to personalise services in ways that simply weren’t feasible before, driving higher satisfaction and loyalty.
Detecting fraud and securing the network
Running an agile, innovative telecoms business also requires vigilance against fraud and security threats – another domain being revolutionised by AI and data. Telecom fraud (such as fraudulent call transfers, SIM swap attacks, subscription fraud and billing scams) costs the industry billions annually. Traditional rule-based systems often struggle to catch new or sophisticated fraud patterns. AI and machine learning provide a powerful tool to detect anomalies in call records and usage behavior that could indicate fraud, often in real time.
For example, machine learning models can be trained on millions of call detail records to learn what normal behaviour looks like for each subscriber or trunk line. If an account suddenly exhibits atypical usage – say, a prepaid phone that normally uses 1 GB of data per day spikes to 10 GB, or a consumer mobile starts making dozens of international calls in an hour – the AI flags it for investigation.
Unlike static fraud rules, these models can adapt and catch subtle changes (such as fraudsters rotating through numbers or mimicking user behaviour). An AI-based fraud management system can automatically suspend suspicious activity or alert the security team within seconds, minimising losses. Many leading telcos are now employing such systems to combat issues like SIM swap fraud (where attackers hijack a user’s number) and international revenue share fraud (IRSF) by identifying telltale anomalies in network signals or account changes.
AI is also enhancing security in areas like network intrusion detection. Telecoms networks face constant attacks on their infrastructure – from DDoS attacks on mobile networks to attempts to hack into core network elements. Here too, AI models analyse traffic patterns and system logs to identify potential cyber threats that human administrators might miss. For instance, anomaly detection algorithms can pinpoint unusual signaling patterns that could indicate a rogue cell tower or a malware spreading through network devices. By quickly isolating these threats, AI helps maintain network integrity and customer trust.
While specific success stories in fraud mitigation are often kept confidential, the industry trend is clear: AI and analytics are becoming indispensable for telecom fraud and security teams. They enable a more proactive stance – stopping fraud and attacks before they escalate – which is a hallmark of an agile, resilient telco. Operators report that adopting AI-driven fraud detection has measurably reduced fraudulent charges and revenue leakage, directly improving the bottom line. Equally important, it protects customers from scams (like spoofed calls or SIM swaps that enable identity theft), thereby safeguarding the customer experience.
In the agile telco paradigm, every facet of the business, including risk and security management, leverages data intelligence to act quickly and decisively. Fraud detection is no exception: machine learning models can scan vast data streams for the proverbial needle in a haystack, far faster than manual methods. As a result, the business can innovate freely (launching new digital services and mobile payment offerings) with confidence that AI is monitoring for fraud and abuse in the background.
Accelerating service delivery and innovation
A defining characteristic of an agile telco is the ability to roll out new services and capabilities at high velocity. Traditionally, telcos had long development and deployment cycles for new offerings (sometimes taking months or even years to introduce a new network service or pricing plan). AI and data-centric strategies are changing that by supporting Agile methodologies and continuous innovation in several ways.
First, AI and automation enable faster service provisioning in telecoms networks. In a fully digital telco environment, launching a new service might involve spinning up virtual network functions, configuring them, and routing traffic through them – tasks that can be automated end-to-end. AI can assist by intelligently orchestrating this process. For example, when a product team devises a new enterprise VPN service or a low-latency gaming boost add-on, an AI-driven orchestration platform can automatically deploy the required network slices or cloud network functions across the operator’s infrastructure. This could shrink service rollout time from months to days or even hours.
The Vodafone-ServiceNow platform mentioned in our previous article is illustrative – by having a real-time, consolidated view of the network and automated workflows, Vodafone can introduce changes or new configurations much more rapidly than before. In essence, AI acts as the autopilot for network changes, ensuring they happen quickly and reliably, which dovetails perfectly with agile sprints and rapid release cycles on the business side.
Secondly, AI provides the insights that fuel quick innovation decisions. Modern telcos are establishing data analytics centres of excellence (CoEs) or AI labs that continuously analyse network and market data to spot opportunities. These analytics might reveal, for instance, that a certain cell cluster has many online gamers – prompting the idea for a specialised low-latency data plan in that area. Or analytics might show a surge in IoT device traffic, suggesting a new IoT connectivity bundle. By mining data in near-real-time, telcos can iterate on their offerings more like internet companies do – testing, learning and tweaking services frequently.
AI tools also enable A/B testing of telecoms services or customer portal features, by intelligently segmenting customers and measuring outcomes, thereby guiding rapid improvements. All these data-driven practices support an Agile methodology where cross-functional teams have factual evidence to drive their product backlog and can pivot quickly if the data indicates a better direction.
Notably, telcos are also applying Agile and DevOps principles internally, and AI amplifies this. Many network functions and OSS/BSS systems are now software-based, meaning updates can be deployed in a continuous integration/continuous deployment (CI/CD) fashion. AI assists in areas like automated code testing (using AI to generate test cases or detect anomalies in new software builds) and in monitoring the performance of new releases rollout.
This reduces the risk of deploying updates, allowing for smaller, more frequent releases – a hallmark of agility. In fact, looking ahead to the next-generation networks (like 6G), researchers envision telecoms systems that “enable continuous development, allowing coding, verification, and deployment of new services at run-time without disrupting existing services”. This vision, essentially an agile DevOps loop for telecoms networks, will rely heavily on AI to manage the complexity and assure quality as changes are made on the fly.
Finally, AI itself can suggest innovations. With techniques like AI-driven simulation and digital twins, operators can model “what-if” scenarios much faster. For example, an AI model could simulate how a new AR/VR service would perform under various network conditions, or predict customer uptake for a new pricing model by learning from historical data. These predictions guide strategic decisions and encourage experimentation, because teams can fail fast (in simulation) and refine ideas before real-world launch.
As operators become more comfortable with such data-driven experimentation, they shorten the cycle of innovation. A great example is in network planning: instead of physically trialling different network configurations, AI can simulate thousands of configurations in software to find the optimal one – dramatically speeding up the planning phase for network upgrades.
In sum, AI and data-centric strategies significantly amplify agile practices in telcos. They provide automation for rapid execution, analytics for rapid decision-making, and even creativity via simulations for rapid ideation. The outcome is a telco that can continuously deliver new value to the market. As one industry analysis noted, by harnessing AI, CSPs enhance their agility and responsiveness to market changes, ensuring they can quickly capitalise on new business opportunities. In the competitive telecoms landscape, this agility in launching services and innovating is becoming as important as the networks themselves.
This is just the beginning for Agile Telco and there is more to discover about this movement in telecommunications. On TechLed’s Agile Telco channel, you will find the latest reports, articles and events that covers Agile Telco, so subscribe to stay up to date with this movement and its impact on telecommunications.
Marion Webber