The increasing network complexity, demand for high, uninterrupted performance, and rising costs has put the telecommunications sector under considerable pressure. As information highways get overloaded by billions and billions of users, while revenue growth is cooling down, telcos need more than a band-aid solution. There is a demand for structural transformation that changes the way the telecom model operates. However, what will be the trigger for that transformation? According to 90% of telecom operators, AI can be the technology that tips the scale through increasing revenue while optimizing expenses. But how does AI implementation look like in practice? To shed more light on this question, this article explores the specifics and applications of AI in networking and network operations.
Telecommunication challenges: What’s holding the growth back?
To see how AI adds value to network operations and covers pain points, it’s important to start with looking at the pain points themselves. Aside from the demand/revenue discrepancy, there are several significant constraints that affect telecommunication network:
- Financial pressures
Although globally telcos demonstrate steady financial performance and invest 18% of their revenue into CapEx, only a small fraction sees tangible growth. So, with considerable investment and vague returns, companies need to make every cent count. This need extends into network expansion and upgrade expenses. While simply maintaining the structure is no longer enough, organizations remain cautious about potential technology investments. - Maintaining interoperability
The larger is the network provider, the more diverse platforms, vendors, and protocols it operates with. Therefore, securing interoperability remains a top priority for Telecom operators. Securing this objective, however, isn’t a smooth sail. Configuration overload, traffic spikes, and vendor dominance, complete with cybersecurity risks create the demand for an agile, intelligent layer capable of shedding light on pitfalls and hidden costs. - Infrastructure modernization
Despite investing in advanced technologies like 5G highways and eSIM, Telecom industry is still built on an outdated infrastructure that compromises meaningful progress. According to 81% of telcos across the United States, Canada, and UK, aging legacy system are the common culprit for unsuccessful new service rollout. From expensive maintenance to blackouts leading to business downtimes, older networks continuously introduce new challenges that lead to uncontrolled budget spending, postponed upgrades, and failed network expansion efforts. - Security challenges
55% of network operators reported dealing with a security breach in the last year. This number underscores the drastic increase in cybersecurity challenges and vulnerabilities exposed due to ongoing innovation. The instances of cyberespionage and target attacks at critical telecom infrastructure by China and Russia create even more pressure and prompt telecom operators to strive towards greater clarity and transparency across their systems.
These challenges highlight that the current telecom model lacks visibility. Investors need more information and clarity into the returns they will gain. Telecom operators need to see and detect performance issues before their clients do. Telecom decision-makers need to introduce upgrades that work and last through a detailed view of their existing systems and innovative capabilities. The data is out there. The question is, how can stakeholders reach it and use it?
Resolving telecom pain points: What is AIOps and how does it fit in?
AIOps (AI for IT Operations) is the direction that leverages analytics, automation, and machine learning for monitoring, optimizing, and managing telecommunications network infrastructure. It became the object of interest for telecom decision-makers following the increasing complexity, network expansion, and scale of work across the sector.
Numerous telecommunications providers are prompted to pursue long-term objective—transparent and autonomous networks that replace reactivity with proactivity and inject agility into every process. Due to its potent automation capabilities and modernization potential, AI became the piece that can finally complete the network modernization puzzle.
Network operations automation isn’t new. It has always existed, but it was fragmented and still relied on NOC engineers doing research and discovering areas for improvement. However, this approach doesn’t work as efficiently in the modern telecom landscape. The environments became more complex. The number of variables has increased, exposing the need for more controlled, systemic, and intelligent automation. This is where AIOps comes in.
Currently evaluated at $3.2 billion and expected to hit $14 billion by 2034, the AIOps for Telecom market is experiencing an explosive growth matching the growing optimism of telecom operators and decision-makers. The optimism is not unfounded: after entering it active GenAI and machine learning implementation stage, the Telecom industry reported the following positive outcomes:
- 28% annual cost reduction reported after AI implementation
- 84% of adopters reported increased annual revenue
- Up to $690 billion in impact is expected to be unleashed across the Technology, Media, and Telecommunications sector through AI application
With this value discovered and documented, there is a clear potential for AIOps. Starting with tangible loss prevention and outage minimization and ending with next-level network performance, 40% of telcos were actively introducing AI to their planning and operations in 2025.
Key applications of AI in networking
To further understand how and where AI can drive value in telecommunications, it makes sense to explore its core applications and the principles behind them.
Predictive maintenance
Industry digitization allowed the Telecom sector to deploy network services as software, ultimately divorcing them from hardware dependencies through cloudification. While doing so allowed to bypass limitations of hardware, it also considerably increased management complexity—teams now have to work with the large and growing volume of metrics and logs as well as constantly monitor and respond to numerous events. The burden of work is immense—and the price of failure is even greater.
Technology-based incidents like the CrowdStrike glitch that disrupted over 7000 flights, crashed 8.5 million computers worldwide, and caused $500 million in financial damage to the Delta airlines alone show how one flawed update can create a massive ripple effect across the globe. That means that NOC engineers have one more “what if” scenario” to add to their list of concerns.
The hardware is also not taken out of the equation completely: in 2026, the United States experienced an outage every month, mostly due to harsh weather conditions overwhelming the outdated grid. Considering that an hour of downtime can cost businesses up to $5 million, outages were a direct hit on many companies’ financial stability rather than a temporary inconvenience.
The scale of challenges often exceeds the capabilities of traditional monitoring software—this is where value from AI begins.
- Predictive analysis
By processing data from power grid, transmission systems, and equipment sensors, machine learning algorithms identify patterns and predict potential failures by comparing system behavior to historical data. Utilization of AI takes predictions even further through smart alerts that warn about potential equipment failure, evaluate risks, and suggest changes to maintenance schedules. - Root cause identification
Whenever an incident happens, teams have to perform an extensive and time-consuming search to find the issue. Using AI enables consistent network health monitoring and instant anomaly identification. As a result, artificial intelligence can find budding issues and prevent them from snowballing thus maintaining stable performance. - Automated troubleshooting
Aside from monitoring and reporting, AI can also act. Through utilizing intelligent agents that automatically correct failures or drops in performance (for example by automated network reconfiguration or restart), telecom organizations enable a self-healing option for their network. Such a network is smart, automated, and requires minimum manual interactions
The outcomes of leveraging AI for predictive telecommunications maintenance have been positive so far, delivering 43% network downtime reduction and demonstrating a 92.7% prediction accuracy score. Recent findings also report prolonged asset lifespan due to improved incident prediction and precaution measures.
Anomaly detection and security
As telecom networks continue to be targeted by hackers, it’s important for decision-makers to build up resilience and evolve their approach to preventing attacks. On average, the Telecommunication sector deals with the following types of cybersecurity risks:
State-sponsored attacks
Attacks on the telecom infrastructure ordered by other state’s government, either for preventing communication or establishing surveillance.
Customer data theft
Stealing customer data for the purpose of blackmail, money, or identity theft.
Supply chain compromise
Exploiting third party vendor software, hardware or other tools to gain access to the entire network.
Aside from intended attacks, there is also a consistent risk of human error and outdated legacy software creating new vulnerabilities. As a result, 60% of investors cite cybersecurity risks as a reason for collapsed deals.
Telecom cybersecurity is a complex and multi-layered process rather than a single, one-time solution. But monitoring is the key. Telcos must be constantly aware of what is going on within the system and stay up to date with the latest regulations. This is why this area benefits from intelligent, automated monitoring.
Since AI makes a good fit for consistent data analysis and pattern detection, it makes a considerable difference in security monitoring.
By overviewing system performance 24/7, an AI-driven platform can instantly take note of suspicious behaviors within the networks, run a detailed analysis, initiate response protocols—and instantly alert security experts. Advanced AI models can be integrated with GTP, SS7, and other industry protocols for enhanced attack identification. So far, this approach has made meaningful impact, with 35% of CSP executives reporting greater, faster fraud detection and reinforced cybersecurity.
Network optimization
Most telecom customer complaints stem from poor access and quality of service, which, in turn, stem from rigid, rule-based approaches to bandwidth management, such as queueing or data transfer cap. These practices often resulted in slow performance during peak hours and service interruptions, much to customer frustration.
The main problem here is that telecom services evolve—but bandwidth management fails to keep up. Static systems are no longer the answer. Instead, networks need mechanisms that scale and respond according to the situation, not rules set years ago. AI allows them to get there.
By leveraging agentic AI, deep learning, and intelligent assistants, telecom companies can develop a self-optimizing system that addresses bandwidth issues in real-time through the following capabilities:
Network demand prediction
Preemptively calculating the amount of necessary bandwidth based on historical data and app characteristics.
Traffic flow analysis
Improving allocation through analyzing traffic data and taking note of repetitive patterns (peak and high-demand hours and seasons).
Dynamic adjustment
Processing feedback on service performance and introducing according improvements.
Ongoing optimization
Analyzing results from the previous operations and decisions to polish bandwidth management strategies.
Behavior reporting
Keeping NOC engineers updated on network performance for greater planning and resource optimization.
These implementations are expected to deliver a 30% to 40% productivity gain and 15% to 30% OpEx savings, creating opportunities for new investments.
This AI application hasn’t gone unnoticed by the big-name brands. In 2024, Nokia implemented a self-optimizing network solution that allowed maintaining stable connectivity despite a 40% traffic increase. Additionally, Vodafone Ukraine launched an AI-powered platform for network load analysis, successfully saving up to 5% of electricity. These use cases show that AI can give an opening telecom companies need – a way to maintain quality of services while saving resources for effective upgrades and network expansion.
Implementing AI-driven networks: Challenges to consider
As appealing as AIOps sound, less than 10% of telecom operators can say they have fully automated their desired domain.
This number seems to counter every benefit of AI-driven network operations mentioned above. After all, if AI delivers so much value to the Telecom sector, why isn’t it broadly adopted?
We can’t talk about implementation without addressing the challenges—and there are quite a few. From managing massive telemetry to interacting with legacy structures, AI affects every part of the way telcos operate. This is something that should be considered in advance.
AI for Telecom implementation pitfalls can be broken down into several segments:
- Obsolete OSS/BSS
Operations Support Systems (OSS) and Business Support Systems (BSS) are often decades-old and reliant on an outdated code. Therefore, they can’t handle complex networks and can’t accommodate the AI requirements. Since these systems are also much harder or impossible to modernize, decision-makers often end up torn between risking disrupting their services or missing unique AI opportunities altogether. - Insufficient data quality
Before AI can maximize the value of data, this data has to be prepared for AI. However, Telecom data silos have been a persistent issue, eroding customer trust and negating billions invested in data monitoring. Simply adding AI to the mix would rather make the problem worse than fixing it. Instead, organizations must work on creating a robust, up-to-date data warehouse, where all data is validated, cleaned free of duplicates, and ready to be used by AI. - Governance and security
AI can be the solution to cybersecurity issues—only when there is a reliable security net around the AI. The technology isn’t invulnerable or limited to non-malicious users only: hackers are already exploiting AI tools and the ways to poison AI systems used by organizations. Due to this, telcos must put guardrails in place and work on their policy engineer before they proceed with AI-driven networks.
While these challenges seem daunting and large in scale, their existence shouldn’t dissuade decision-makers from exploring the benefits of AI-driven networks. Every task becomes possible with the right system and framework in place. Here’s what telecom organizations can do to lay the foundation for successful AI adoption.
- Identify a small, yet efficient candidate
Before launching a full-scale AI adoption campaign, adopters should start small and see where value shows. Choosing a direction that requires the least effort, makes it easy to track progress, and demonstrates clear KPIs, decision-makers can bypass the modernization hurdles and create an efficient, easy-to-scale pilot. ` - Establish automation hard stops
Every AI mechanism should come with its circuit breaker in case of anomalies and suspected attacks. Investing in such hard sops allows organizations to minimize potential damages and prevent networks from exploitation. - Create a healthy data environment To address the data silo issue, enterprises should create a consolidated data warehouse, gradually refreshing their data strategy, identifying fragmented systems, and gathering as much diverse data as possible for successful model training. Doing so often requires the presence of domain experts who combine data scientist expertise and telecom acumen.
Are you looking to reinforce your telecom strategy with AI-driven networks? Let’s chat!
At Trinetix, we have over 14 years of experience in driving innovative transformation for major market players across a wide range of industries. With our talented data scientists, business analysts, AI architects and automation experts, we will ensure that your organization enters its AI stage with the right data, foundation, and capabilities for discovering new value and exceeding performance thresholds.
