I’m thrilled to sit down with Vladislav Zaimov, a seasoned telecommunications specialist with a wealth of experience in enterprise telecom and risk management of vulnerable networks. With a career dedicated to navigating the complexities of modern network systems, Vladislav brings a unique perspective on how operators are leveraging AI and automation to transform their operations. In this conversation, we’ll explore the journey toward autonomous networks, the role of trust in adopting AI technologies, the challenges of data integration, and the future of AI agents in telecom. Let’s dive into how these innovations are reshaping the industry and what lies ahead.
How would you describe the current state of network automation for operators, and why is it such a critical focus for them?
Network automation today is at a pivotal stage for operators. It’s essentially about using technology to manage and optimize network operations with minimal human intervention, which is crucial for handling the growing complexity of networks spanning multiple generations of mobile tech. Operators are dealing with massive data volumes, diverse services, and cost pressures, so automation offers a path to efficiency and better customer experiences. The focus is on reaching higher autonomy levels—think self-healing and self-optimizing networks—because it reduces operational costs and downtime while meeting escalating service demands.
What do you see as the primary hurdles operators face when pushing toward fully autonomous networks?
The hurdles are multifaceted. First, there’s the integration of legacy systems, which often weren’t built with modern AI or automation in mind, creating compatibility issues. Then, data quality is a huge barrier—without clean, unified data, AI systems can’t make reliable decisions. There’s also a significant skills gap; not enough professionals are trained in both AI and telecom to drive these initiatives. Lastly, trust in automated systems is a sticking point. Operators need assurance that AI-driven decisions won’t lead to costly errors, which slows down adoption.
Why is having a unified data layer so essential for advancing network automation?
A unified data layer is the backbone of effective automation. It ensures that all parts of the network—spanning different technologies and vendors—speak the same language. Without it, you get fragmented data that leads to inconsistent insights and poor decision-making by AI systems. For automation to work, especially at higher levels where AI agents make autonomous decisions, data needs to be accessible, accurate, and traceable. It’s like building a house—you need a solid foundation, or everything else falls apart.
How can operators tackle the challenge of ensuring their data is accurate and ready for AI systems?
Operators need to prioritize data governance from the get-go. This means setting up strict protocols for data collection, storage, and validation to minimize errors. Investing in tools that clean and standardize data is also key. Another approach is adopting frameworks like the TM Forum’s Open Digital Architecture, which helps streamline data pipelines across systems. It’s also about continuous monitoring—operators should regularly audit their data to catch inconsistencies before they impact AI performance.
What specific issues do legacy systems pose when integrating with modern AI operations?
Legacy systems are often rigid and siloed, built on outdated architectures that don’t play well with the dynamic, data-intensive nature of AI operations. They struggle to correlate critical data—like network topology or inventory—with real-time assurance data, which is essential for AI to function effectively. Interoperability is a constant headache because these older systems weren’t designed for open APIs or modern standards, leading to gaps in data flow and hindering the scalability of automation efforts.
How are operators addressing the skills gap in AI and telecom, and what role do partnerships play in this?
The skills gap is a real challenge, as operators need expertise in both AI and telecom, which is a rare combination. Many are investing in training programs to upskill their workforce, focusing on machine learning and network-specific applications. Partnerships are also critical—collaborating with hyperscalers and vendors provides access to cutting-edge tools and expertise, acting as a bridge while operators build in-house capabilities. These alliances help accelerate learning and deployment of AI solutions without starting from scratch.
Why is trust such a vital factor in transitioning from reactive to proactive network management with AI?
Trust is everything when you’re moving from reacting to issues after they happen to predicting and preventing them before they do. Operators need to believe that AI systems can accurately foresee problems and act without constant human oversight. Without trust, there’s hesitation to let go of manual controls, which defeats the purpose of automation. It’s about ensuring that AI decisions are explainable and reliable—otherwise, the risk of network disruptions or costly mistakes looms large.
Can you explain what digital twins are and how they help build confidence in AI-driven network changes?
Digital twins are essentially virtual replicas of physical network environments. They simulate real-world conditions, allowing operators to test AI-driven changes in a zero-risk setting before rolling them out. For instance, if an AI suggests a network reconfiguration, a digital twin can model the outcome—predicting performance impacts or potential failures. This builds confidence because operators can see the results without touching the live network, reducing fear of unintended consequences and fostering trust in AI systems.
What are AI agents, and how do they stand out from traditional automation tools in telecom?
AI agents are advanced software entities that can reason, learn, and act autonomously based on data and predefined goals. Unlike traditional automation tools, which often follow rigid scripts or rules, AI agents adapt to changing conditions, coordinate with other agents, and even retain memory of past actions to improve future decisions. In telecom, this means they can handle complex tasks—like proactive fault detection or resource optimization—far beyond the basic, repetitive functions of older tools.
Looking ahead, what is your forecast for the role of AI agents in shaping the future of network operations?
I see AI agents becoming the cornerstone of network operations in the next few years. As trust and technology mature, we’ll witness a shift toward multi-agent systems that collaborate across different network functions, driving end-to-end automation. They’ll handle everything from service assurance to network planning, drastically reducing human intervention. The key will be balancing complexity with reliability—ensuring these agents work seamlessly while maintaining security and performance. It’s an exciting time, and I believe we’re on the cusp of a truly autonomous network era.
