Time to read: 6 minutes 48 seconds | Published: October 1, 2025
AI-native networking What is AI-native networking?
AI-native networking refers to computer networking systems that are conceived and developed with AI integration as a core component to enable simpler operations, increased productivity, reliable performance at scale, and an assured user experience.
Unlike systems where AI is added as an afterthought or a "bolted on" feature, AI-native networking is fundamentally built from the ground up with AI and for AI.
AI-native networking explained
As with all modern AI systems, AI-native networking systems are designed to learn from data, adapt to new situations, and improve over time. This continuous learning capability is a fundamental characteristic, allowing the system to become more efficient and effective as it gathers more data and experiences.
An AI-native network that is trained, tested, and applied in the correct way can anticipate needs or issues and act proactively, before the operator or end user even recognizes there is a problem. This saves IT and networking teams time, resources, and reputation, while simultaneously enhancing operational efficiency and improving overall user experiences.
Why does AI-native networking matter?
From digital transformation to high-profile AI initiatives to explosive user and bring-your-own-device (BYOD) growth, networks are experiencing tremendous and ever-growing pressure. Given IT budgets and constraints related to skills availability and other factors, the combination of complexity and unpredictability of traditional networks is a growing liability.
AI-native networking simplifies and streamlines the management of these complex networks by automating and optimizing operations. These networks dynamically adjust and scale to meet changing demands and resolve issues without requiring constant human intervention. By optimizing performance based on user behavior and preferences, they ensure seamless and enhanced user experiences.
Removing traditional networking limitations, such as outdated manual processes and poor user experiences, enables organizations to innovate and experiment with new business models, services, and technologies that require robust and adaptive network infrastructure.
What are the benefits of AI-native networking?
Adopting AI-native networking offers a wide range of benefits, including:
- Enhanced efficiency and performance: AI algorithms can optimize network traffic routes, manage bandwidth allocation, and reduce latency. This results in faster and more reliable network performance, which is especially beneficial for bandwidth-intensive applications like video streaming, large-scale cloud computing, and supporting AI training and inference processes.
- Predictive maintenance and downtime reduction: By anticipating issues before they happen, an AI-native network can schedule maintenance proactively, reduce unexpected downtime, and fix issues before they impact end users. This is especially crucial for businesses where network availability directly impacts operations, revenue, and reputation.
- Improved security: With the capability to analyze vast amounts of network data in real time, an AI-native network allows for the early detection of anomalies and potential security threats. This proactive approach to security helps thwart cyberattacks and protect sensitive data.
- Cost savings: Automating network management tasks reduces the need for manual intervention, which can lead to significant cost savings in terms of labor and operational expenses. Additionally, predictive maintenance can prevent costly emergency repairs and downtime.
- Scalability and flexibility: An AI-native network can adapt to changing demands without the need for manual reconfiguration. This scalability ensures that the network can handle increasing loads and new types of devices seamlessly.
- Enhanced user experiences: An AI-native network optimizes network performance based on user behavior and preferences, ensuring continuously exceptional experiences for IT operators, employees, consumers, and users of public internet services.
How AI-native networking works
Good AI starts with the right data. For an AI-native network to be most effective, it needs to not only collect vast quantities of data, but also high-quality data. Bad data, or the wrong data, can lead to inaccurate or biased responses. This collected data includes traffic patterns, device performance metrics, network usage statistics, security logs, real-time wireless user states, and streaming telemetry from routers, switches, and firewalls.
The collected data is analyzed using ML algorithms, GenAI, and agentic AI. These algorithms are trained to recognize patterns and anomalies in data. Learning from the network's behavior over time, they develop and improve, enabling them to make more accurate predictions and decisions.
Applying explainable AI processes and methods allows users to understand and trust the results and output created by the system’s AI algorithms. It’s key to providing insights into how data is being utilized and evidenced for its output.
Based on the analysis and trustworthiness of the data, an AI-native network can provide the right real-time response. The decision-making process is dynamic and occurs in real time, allowing the network to adapt quickly to changing conditions. Potential responses include:
- Predictive modeling: By predicting future network states or potential issues, it can forecast traffic spikes or identify weak spots in the network that are susceptible to failure or attacks.
- Self-optimization: With an AI-native network, if the AI detects that a particular route often becomes congested at certain times, it can preemptively reroute traffic to maintain optimal performance.
- Proactive maintenance and self-healing: The network can identify and diagnose issues before they cause significant problems like hardware failures. It can also take corrective actions automatically, such as rebooting a malfunctioning device or switching to backup systems.
- Security enhancement: When a potential threat is detected, the network can implement security policies, such as isolating affected network segments or blocking malicious traffic.
- User experience management: AI-native networking can tailor the network performance to meet user demands, adjusting priorities and resources based on user behavior and preferences.
AI-native networking use cases
AI-native networking finds its application in a variety of use cases across different industries. These use cases typically fall into one of two categories: AI for networking and networking for AI.
- AI for networking: An AI-native network can continuously monitor and analyze network performance, automatically adjusting settings to optimize for speed, reliability, and efficiency. This is particularly useful in large-scale networks like those used by internet service providers or in data centers.
An AI-native network predicts failures or bottlenecks before they occur and prompts preemptive maintenance to reduce downtime and improve reliability. This is crucial for critical infrastructure and services like hospitals, emergency response systems, or financial institutions.
AI-native networking can detect unusual patterns indicative of cyber threats or breaches. This includes identifying and mitigating DDoS attacks, malware, or unauthorized access attempts, crucial for protecting sensitive data in sectors like banking, government, and defense. - Networking for AI: Unique traffic patterns, cutting-edge applications, and expensive GPU resources create stringent networking requirements when performing AI training and inference. AI-native networking systems help deliver a robust network with fast job completion times and excellent return on GPU investment.
AI-native networking and HPE Networking
Mist AI, the industry’s first AI-native networking platform, from the ground up to take full advantage of the promise of AI. Mist delivers the industry’s only true AI for IT operations (AIOps) with unparalleled assurance across the entire network through a unified cloud. From real-time fault isolation to proactive anomaly detection and self-driving corrective actions, it provides campus, branch, data center, and WAN operations with next-level predictability, reliability, and security.
Enterprises rely on Mist, powered by Marvis AI, to significantly streamline ongoing management challenges while assuring that every connection is reliable, measurable, and secure. They are also building highly performant and adaptive network infrastructures that are optimized for the connectivity, data volume, and speed requirements of mission-critical AI workloads.
It all started with a strategic pivot to an experience-first approach that focuses on asking the right questions to deliver the best experiences for both network operators and end users. Mist’s ability to deliver the right experiences is built on three fundamental pillars: 1) AI-native Operations, 2) Comprehensive client-to-cloud Portfolio, and 3) Integral Security AI Native Operations: Success in today’s digital landscape hinges on having both the right data and the right response. By leveraging high-quality, real-time telemetry gathered with deep domain expertise, organizations can answer experience-first questions with precision and clarity. This foundation enables real-time insights and actions that accelerate root cause identification, deliver smart recommendations, and drive automated resolutions—ensuring seamless operations and exceptional user experiences.
- Comprehensive client-to-cloud Portfolio: Digital transformation demands both the right networks and the right cloud. With an end-to-end networking portfolio spanning enterprise, service provider, and cloud provider domains, enriched by AI-enabling telemetry and insights, organizations gain the visibility and intelligence needed to optimize performance. Paired with cloud-native and AI-native hybrid cloud platforms, businesses unlock speed, agility, and scale—delivering innovation with the simplicity and cost-efficiency of the cloud.
- Integral Security: In a world of evolving cyber threats, unified protection is essential—safeguarding users, systems, and data across the entire client-to-cloud environment. With AI-native security at its core, this approach delivers intelligent defense that continuously learns and adapts to emerging threat vectors, ensuring resilience and proactive risk mitigation at every layer of the digital ecosystem.
HPE Networking laid the foundation for its AI-native networking platform years ago when it had the foresight to build products in a way that allows the extraction of rich network data. By using this data to answer questions about how to consistently deliver better operator and end user experiences, it established a new industry benchmark.
AI-native networking FAQs
How do AI-native networking platforms differ from traditional networking solutions?
Unlike traditional networking solutions, an AI-native networking platform is inherently designed with AI integration at its core. It is purpose-built to leverage AI for enhanced network management and operations. This fundamental integration enables advanced capabilities like predictive analytics, real-time optimization, and autonomous issue resolution, setting it apart from conventional networks that rely heavily on manual intervention and oversight.
What problems does HPE Networking’s AI-native networking platform solve?
HPE Networking’s AI-native networking platform solves many problems, including increasing network complexity, constrained resources, network unpredictability, and throttled network responsiveness.
What’s driving the adoption of HPE Networking’s AI-native networking platform?
HPE Networking customers are enjoying benefits like up to 90% fewer networking trouble tickets, up to 85% reduction in networking OpEX, and up to 50% less time to reach networking incident resolution.
What are the key capabilities of HPE Networking’s AI-native networking platform?
It delivers the industry’s only true AIOps with unparalleled assurance in a common cloud, end-to-end across the entire network. It enables real-time fault isolation, automated anomaly detection, and responsive remediation across campus, branch, data center, and WAN environments, advancing operational stability and security.
What solutions/productions/technology are offered with HPE Networking’s AI-native networking platform?
HPE Networking’s AI-native networking platform encompasses the entire HPE Networking portfolio. It leverages AI for assured experiences across every aspect of networking, all based on our demonstrable and proven expertise. Key products include the Marvis® AI engine, Marvis AI Assistant, Marvis Conversational Interface, Marvis Large Experience Model, Marvis Minis, wireless access, wired access, SD-WAN, data center, and enterprise WAN.