Could a multi-trillion-dollar industry like telecom soon run on autopilot with agentic AI?
This is not out of the realm of possibilities, given how AI has made its way into multiple industries, especially the Technology, Media, and Telecommunications (TMT) industry. Why? Since this sector constantly grapples with soaring data demands, 5G rollouts, and next-gen connectivity, there is a need for better optimization. This is where we bring AI-powered telecom network automation into the conversation.
As an industry leader in telecom services, your role is not only to self-optimize bandwidth, but also to predict failures early on and reconfigure networks in real-time with minimal human intervention. And agentic AI offers immense potential to make this possible. Let’s dive in and understand more about AI in network automation and how it represents a paradigm shift for telecom and media providers.
What is AI in Network Automation?
Network automation powered by AI simply refers to the use of artificial intelligence and machine learning to manage and optimize network operations. The way it works is quite simple - AI tools analyze vast amounts of network data to identify patterns, find potential issues, and trigger automated responses that self-heal autonomous networks.
The purpose of AI in network automation lies in speeding up troubleshooting processes, reducing manual interventions, enabling self-healing network capabilities, and improving overall network reliability. As such, it transforms the traditional manual network management into intelligent, self-optimizing systems that adapt in real-time and deliver superior user experiences.
Agentic AI Explained — Beyond Traditional AI in Networking
Here’s the common distinction between traditional AI and agentic AI: while the former is typically static, following a rule-based approach, the latter turns the tide with autonomy. As a telecom leader, it means more opportunities for you to move beyond the ordinary, unlocking proactive and context-aware operations. Doesn’t matter whether it's network management, customer engagement, or overall business strategy, agentic AI in network automation is your key to future-proofing telecom networks, even when conditions change.
To add more, AI use in the TMT sector is rapidly growing, with the market size expected to grow from $13.27 billion (2025) to $43.44 billion (2030) (Source). This is a direct reflection of the industry’s acceptance and adoption of AI in telecom, with autonomous agents being a crucial contributor to this statistic. And you may ask the question: Why should I scale these autonomous agents across TMT operations? You’ve got not one, but three good reasons:
- Autonomous network management - The agents automatically monitor infrastructure performance, detecting anomalies, and enacting self-healing protocols for AI in network automation.
- Contextual decision-making - They continuously analyze rich data streams like network KPIs and usage patterns for enabling context-aware actions at every layer of the network.
- Goal-driven adaptability - The agents are generally powered by goal-oriented intelligence. Simply put, they set their own objectives and adapt plans accordingly, learning from new data and outcomes in the process, further developing the potential of AI in network automation!
AI in Network Security Automation
According to a recent Blinkops report, 81% of respondents say AI automation will be critical to their security strategy over the next 3-5 years. (Source) Security threats are prominent even in the TMT industry, which is where AI and ML are brought in to eliminate them. For starters, these technologies continuously monitor network traffic, enforce strict access controls, and automate incident responses.
Beyond that, AI in network automation also supports intelligent aggregation of security events for comprehensive threat intelligence and reducing manual intervention in network defence.
Real-World AI Automation Examples in Networking
Let’s dive into some real-world case studies exploring the use of AI in network automation:
AT&T
This global telecommunications leader faced a common challenge: overseeing an intricate and extensive network that needed a proactive solution for anticipating network issues. To prevent such issues from disrupting services, the company deployed AI-driven network optimization and predictive maintenance solutions. These solutions analyzed real-time data from network sensors, customer usage patterns, and historical patterns to detect anomalies and take preventive action. Additionally, it also integrated AI into its SDN framework, enabling automated responses to network congestion and performance issues. (Source)
Google implemented B4 WAN, an inter-data center network that used ML-based traffic engineering to dynamically allocate bandwidth. By analyzing traffic demand patterns and adjusting network routing, the company achieved better bandwidth utilization of up to 30%, even reducing packet losses during peak demand. This is also a significant breakthrough in the subject of AI in network automation. (Source)
Retraining AI Models — A Critical Task in Network Automation
Retraining of AI in network automation is an ongoing process of refinement for higher network accuracy and resilience. Network environments keep evolving, from the emergence of new security threats to new devices added into the mix. And for network automation, AI models must adapt to these shifts to maintain optimal performance to ensure their predictive value does not decline. We know why retraining works, but ever wondered how it works? Let’s dive deeper:
How retraining works
There are two common triggers of retraining you’ll need to keep in mind when updating deployed AI models with new operational data:
- Performance-based retraining - This trigger is initiated when a model’s accuracy falls below its threshold, indicating a data drift.
- Interval retraining - This trigger emphasizes periodic retraining on a scheduled basis, often enabled by MLOps pipelines. The schedule can be either on a weekly or monthly basis.
While retraining AI in network automation, organizations conduct the following:
- Monitoring model performance and detecting shifts in predictive accuracy or relevance.
- Collecting and validating fresh input data.
- Updating or rebuilding models using the latest data.
Benefits of Agentic AI in Network Automation
Agentic AI offers several benefits in optimizing your network operations. Some of them include:
- Enhanced network performance and reliability - The agents continuously monitor network performance, identifying potential issues and re-routing traffic for high-quality service delivery and minimized downtime.
- Higher security - The agents continuously monitor threats, dynamically isolating compromised systems and implementing countermeasures against them.
- Higher efficiency and cost reduction - Agentic AI in network automation also emphasizes cost reduction through automation of complex, high-volume tasks like capacity planning, alarm triage, and support ticket management. Thanks to this ability, you can reduce operational costs and focus more on high-value tasks that may contribute to revenue generation.
Challenges and Considerations
While Agentic AI in network automation is packed with benefits, it also faces significant challenges that hamper network performance and reliability. Some of them include:
Cybersecurity concerns
Lack of cybersecurity management expands the overall attack surface, rendering network infrastructures vulnerable to cyberattacks. Additionally, it also puts your enterprise at risk of online safety and data protection regulations.
Skill gaps
Understanding network data and infrastructure starts from two stages - addressing skill gaps and cultural readiness from top-level executives. To deploy agentic AI in network automation, you’ll need to ensure that enterprise teams are well-equipped with skills in data science and network analytics. Lack of key competencies delays deployments, even stalling projects at the proof-of-concept stage.
Legacy system integration
Integration with legacy systems can limit overall interoperability. And to prevent such a case, AI platforms must mesh with old hardwares and existing orchestration tools. With AI agents unable to perform to their full potential, you may encounter network instabilities, incompatibility risks, and operational overhead.
Best Practices for Enterprises Adopting AI in Networking
As an industry leader in the TMT space, AI in network automation demands a more robust and holistic approach. One where you balance data quality, automation readiness, strong governance, and network scalability. And several best practices can help you achieve these:
Upgrade infrastructure to meet AI demands
This is an obvious one. For smooth AI adoption and rollouts, your IT infrastructure must be compatible with the latest AI tools. AI workloads usually generate massive data flows, requiring high-speed connectivity. Investing in scalable infrastructures like fiber networks is one way to go, as it supports symmetric, high-bandwidth connections and low latency for AI inference. And solutions like SD-WAN also prioritize AI traffic across mult-cloud environments, further supporting integration.
Prioritize data quality and privacy
Your AI systems are only as effective as the quality of data they process. That means enforcing data quality controls, accurate labelling, and centralized data governance. Given the sensitive nature of networking data, privacy measures like automated threat detection and advanced access policies are also non-negotiable.
Follow a strategic, customer-centric approach
Success in an enterprise is not just about achieving customer satisfaction. It’s also about solving business-critical problems while supporting long-term goals. To achieve both, initiatives pertaining to AI in network automation need strategies focused on both measurable business outcomes and the experiences of end-users. Quantifiable KPIs, regular network assessments, and feedback loops are key practices for this.
The Future of AI Networking — Towards Fully Autonomous Systems
AI in network automation is rapidly advancing towards fully autonomous systems, fueled by the integration of ML technologies into core network architectures. Let’s look at some trends that are set to shape the TMT sector moving forward:
GenAI in network design - We may see increased use of generative AI for critical tasks like policy scripting and automated documentation, enabling stronger AI-native networking.
AIOps & Closed-loop control - Through AIOps, networks can observe and actuate changes in a closed loop, driving Level 5 network maturity that is fully autonomous.
AI-native slicing (6G) - 6G networks are on the horizon, and AI is set to enable customized network slices for that. And the entire process will be through dynamic resource allocation across spectrum, compute, and transport stages to meet the performance demands of applications like extended reality (XR) and autonomous vehicles.
Wrapping Up
As a TMT leader, the potential for AI in network automation presents numerous possibilities. Modernizing and maintaining your network infrastructures with AI and ML can offer benefits and leave several challenges in your wake. But with their ability to proactively resolve issues and automatically tune network performance, you will unlock true value for your customers.
At Tredence, we provide the assistance and tech stack you need towards this transformation. And in a time where 5G investments sometimes outpace revenue, our solutions help you fast-track innovation, especially when it comes to AI in network automation. Our services span every aspect of TMT, from optimizing network operations to AI-powered content intelligence and hyper-personalized viewer experiences.
Contact us today and let us be your innovation partner in AI networking!
FAQs
1. What is AI in network automation, and why is it important?
AI-powered network automation applies artificial intelligence and machine learning to optimize, monitor, and self-heal network operations. Its importance spans several reasons, from creating more agile and scalable networks to improved security, performance, and reliability.
2. How does agentic AI improve network optimization in telecom and media?
In TMT, agentic AI can improve network optimization in many ways, starting from predicting failures, dynamic resource allocation, self-healing, and enhanced performance.
3. What are real-world AI automation examples in networking?
Here are some real-world examples of AI in network automation:
- AT&T using AI-powered predictive maintenance in its global network infrastructure to check and anticipate hardware failures.
- Google’s B4 WAN using ML-based traffic engineering to allocate bandwidth and reduce packet loss during peak demand.
- Cisco DNA Center’s AI-based policy orchestration allows companies to deploy intent-based networking at scale.
4. How is AI used in network security automation?
In network security automation, AI detects unusual patterns in network datasets, automates incident responses, and continuously updates security policies, thereby improving network performance and response rates.
5. What is a critical task in the retraining of AI models for network automation?
When it comes to retraining AI in network automation, ensuring the quality and relevance of training data is adaptable is key alongside maintaining overall model accuracy.
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Editorial Team
Tredence
