Vladislav Zaimov is a distinguished figure in the telecommunications landscape, recognized for his deep-seated expertise in enterprise infrastructure and the intricate management of high-stakes, vulnerable networks. As the industry navigates the complexities of 2026, Zaimov’s insights offer a bridge between legacy systems and the cutting-edge integration of intelligence within the radio access network. His work focuses on the intersection of operational stability and the relentless push for efficiency, making him a pivotal voice in how modern carriers sustain their competitive edge while ensuring reliability for millions of users.
Our discussion navigates the practical shift from experimental deployments to the measurable results seen in contemporary field trials. We explore how leading operators are utilizing dual-layer intelligence to balance speed and scale, the rigorous economic and environmental tests a technology must pass to be considered “AI-worthy,” and the evolving role of the network engineer in an increasingly automated environment. Furthermore, we examine the technical frameworks required for open, programmable platforms that allow for seamless vendor collaboration and the future of self-healing networks.
AT&T has reported significant improvements in throughput and spectral efficiency using AI-native features inside the RAN. How do these field results compare to traditional rule-based methods, and what specific metrics should engineers prioritize when evaluating these gains? Please provide a step-by-step breakdown of how these improvements are measured.
In the field, the transition from rigid, rule-based logic to AI-native features feels like moving from a static map to a live GPS that anticipates traffic. Traditional methods rely on fixed parameters that often fail to account for the chaotic, real-world fluctuations of radio signals, whereas these new trials with Ericsson have shown a remarkable ability to squeeze more capacity out of the same expensive spectrum. When we evaluate these gains, we prioritize spectral efficiency and raw throughput, but we do so by first establishing a baseline using standard rule-based configurations under various load conditions. We then activate the AI-native features and capture real-time data on bits per hertz to see how much more data is being pushed through without adding hardware. Finally, we perform a side-by-side comparison of the signal-to-interference-plus-noise ratio (SINR) to ensure that the gain in throughput isn’t being negated by increased network noise.
Verizon utilizes a dual strategy by combining centralized rApps for slow-loop intelligence with local AI inside the gNodeB for faster edge decisions. What are the primary technical challenges in balancing these two layers, and can you share an anecdote regarding a situation where local AI outperformed centralized processing?
The balancing act between centralized rApps and local AI is primarily a battle against latency and the sheer volume of data transit. Centralized apps are fantastic for long-term forecasting and planning, but they are often too sluggish to handle the millisecond-level adjustments required at the radio site. I recall a situation during a high-density event where the centralized system was overwhelmed by the rapid fluctuations in user movement, leading to dropped packets. The local AI inside the gNodeB stepped in, making instantaneous link adaptation decisions that stabilized the connection for users in that specific sector before the central hub even registered the spike. It was a clear demonstration that for real-time user experience, the closer the intelligence is to the antenna, the better the result.
The concept of “AI worthiness” suggests that new features must justify their extra computing power and software management costs. What specific criteria do you use to determine if an AI application provides a measurable benefit to the customer, and how do you mitigate the increased energy consumption associated with these tools?
Determining “AI worthiness” is a rigorous process because we cannot afford to deploy technology just for the sake of novelty. Our primary criteria involve a direct improvement in the customer experience—such as better coverage or higher downlink spectral efficiency—that outweighs the cost of the extra compute and power. To mitigate the energy drain, we are implementing AI models that specifically target power saving during low-traffic periods, effectively allowing the network to “breathe” and conserve energy when demand drops. We also look for a reduction in operational complexity; if an AI tool requires more manual oversight than the system it replaces, it fails the test. Every watt of power and every dollar spent on software management must translate into a tangible, high-quality connection for the person holding the device.
AI is currently being applied to beamforming, interference handling, and traffic steering to manage network load more efficiently. How do these automated optimizations impact the daily workflow of network engineers, and what specific steps are taken to ensure that automated traffic steering does not inadvertently degrade service quality?
These automated tools are shifting the daily workflow of engineers away from tedious manual tuning and toward high-level strategy and oversight. Instead of spending hours adjusting individual signal directions, engineers now supervise the AI as it handles complex beamforming and interference tasks in real-time. To prevent automated traffic steering from degrading service, we implement “safety rails” or policy-based boundaries that the AI cannot cross, ensuring that users aren’t shunted to a congested cell just to satisfy a load-balancing algorithm. We also utilize a phased rollout approach where the AI’s steering decisions are monitored against key performance indicators like call drop rates and latency spikes. This ensures that the automation is actually improving the flow of data rather than creating new bottlenecks.
Distributed AI requires managing and updating numerous models across various network points, which increases operational complexity. What are the best practices for establishing an update schedule that maintains service stability, and how do you handle a scenario where a model update at the radio edge underperforms?
Managing models at the radio edge requires a disciplined lifecycle management strategy that mirrors the best practices of modern software development. We establish update schedules during maintenance windows when traffic is at its lowest, ensuring that each update is first validated in a digital twin or a small-scale pilot cluster. If a model update at the radio edge begins to underperform—perhaps by miscalculating link adaptation—we have an automated “rollback” protocol that immediately reinstates the previous stable version. We also use continuous monitoring tools that flag performance deviations within seconds, allowing us to isolate and fix the issue before the majority of users are even aware of a problem. This level of granularity is essential when you are managing thousands of distributed intelligence points across a massive geographic area.
Open and programmable platforms are being prioritized to allow collaboration between vendors and internal teams. How does this shift toward openness influence the speed of deploying new gNodeB use cases, and what specific architectural requirements are necessary to support third-party AI integrations without compromising security?
The shift toward open and programmable platforms has fundamentally accelerated our deployment cycles, allowing us to integrate specialized rApps from different vendors in weeks rather than months. By moving away from closed, proprietary silos, we can cherry-pick the best AI solutions for specific challenges like traffic forecasting or fault detection. Architecturally, this requires a robust service management and orchestration (SMO) layer that acts as a secure gatekeeper for all third-party integrations. We implement strict API security protocols and sandbox environments to ensure that any third-party AI can interact with the gNodeB without gaining unauthorized access to sensitive network data. This creates a collaborative ecosystem where innovation can thrive without sacrificing the underlying security or stability of the national infrastructure.
Beyond performance gains, AI is being positioned as a tool for fault detection and root-cause analysis. Could you describe a situation where AI identified a network problem faster than a manual team, and what is the typical process for transitioning from AI-detected issues to fully automated fixes?
AI has a remarkable ability to spot subtle patterns in massive datasets that a human team might miss for days. I remember an instance where an AI tool flagged a series of minor interference patterns that suggested a hardware component was beginning to fail long before it actually died. A manual team would have only responded once the site went dark, but the AI caught the anomaly early, allowing us to dispatch a technician during a scheduled window. The transition to fully automated fixes involves a “human-in-the-loop” phase where the AI suggests a fix—like resetting a port or rerouting traffic—and an engineer approves it. As the system proves its reliability over hundreds of successful interventions, we eventually grant it the autonomy to trigger those specific fixes automatically, significantly reducing the mean time to repair.
What is your forecast for AI-driven radio access networks over the next five years?
Over the next five years, starting from 2026, I expect AI to move from being an add-on feature to the very foundation of how radio access networks are built and scaled. We will see the “AI worthiness” test become even more stringent as operators demand that every deployment contributes to a self-healing, zero-touch network that can anticipate user needs before they arise. Spectral efficiency will reach new heights as AI-native beamforming becomes standard, making the most of every megahertz we have available. For our readers, my advice is to embrace this complexity: the future belongs to those who can master the management of these distributed models, as the intersection of software and radio is where the next decade of connectivity will be defined.
