Prompting techniques for QA Engineers
Dipali Pawar
ACL Digital
September 17, 2025
5 Minutes read
How AI-Based Switches Are Redefining Connectivity?
Imagine a network that not only moves packets but also makes decisions.
As enterprises and service providers race to adopt AI, their networks are silently transforming into intelligent digital fabrics. Analysts from Dell’Oro Group and IDC are now signaling a dramatic shift. AI is no longer just an application workload, but a foundational force shaping how networks are designed, scaled, and operated. The question is no longer if networks will get smarter, but how fast they can evolve to keep up.
The Surge of AI Demands High-Performance Networks
With the COVID-19 challenges, the rapid integration of AI has surged forward at an unprecedented rate. According to the IDC Group, the pandemic has pushed AI to the top of the corporate agenda, empowering business resilience and relevance. However, with increased adoption of artificial intelligence and machine learning (AI/ML), there is an extra demand on computing resources and supporting infrastructure.
Modern networks are tasked with processing ever more complex requests and delivering real-time data updates. Operators now require not only higher-performance networks but also robust infrastructure to support AI/ML solutions, while maintaining cost efficiency and operational scalability.
The Data Doesn't Lie: A High-Speed Arms Race
Industry experts are painting a clear picture of a massive network overhaul, driven directly by the demands of AI. “The path to an AI revolution begins with a high-performance network,” states Sian Morgan, an analyst at Dell’Oro Group, noting that while hyperscalers have been leading the charge, enterprise investment in AI infrastructure is only just beginning.
The market numbers confirm this seismic shift:
- According to Dell’Oro Group, by 2027, more than 60% of wireless access points delivered to enterprises will support multi-gigabit ports—almost double the share seen in 2022. Over the same period, shipments of 2.5, 5, and 10 Gbps campus switch ports are projected to climb by more than 150%, reflecting rising demand for higher-speed connectivity.
- At the core network layer, Dell’Oro Group further projects that by 2027, over 25% of switch ports shipped to large enterprises will operate at 400 Gbps or above, a sharp rise from just 9% in 2022. This rapid shift is being fueled by the growing adoption of AI workloads and high-performance computing (HPC) environments.
- IDC’s market data supports this momentum, reporting that in Q2 2024, revenues from 200/400 GbE switches surged 104.3% year-over-year, signaling an accelerating move toward ultra-high-speed switching infrastructure.
“In the data center portion of the market, enterprises and service providers are building ever-faster Ethernet switch speeds to support rapidly expanding AI workloads,” explains IDC analyst Brandon Butler. The backbone of modern business is being rebuilt for the AI era.
What Is AI-Based Ethernet Switching?
AI-based intelligent switching refers to network switches that utilize artificial intelligence (AI) and machine learning (ML) to make informed, real-time decisions about data traffic, rather than relying solely on static forwarding rules such as MAC tables, VLAN configurations, or routing entries.
These switches are designed to:
- Process data at excessively high rates, selecting the most efficient paths to move traffic
- Continuously analyze traffic patterns and dynamically adjust forwarding behavior
- Detect congestion or anomalies early, rerouting traffic or isolating threats instantly
- Learn from historical patterns, adapting automatically as network conditions and user demands change
By embedding AI-driven analytics into the switching plane, these devices transform from passive packet movers into active decision-making nodes that can optimize performance, enhance security, and reduce operational complexity.
Key Capabilities Powering Intelligent Switches
Automation & Efficiency
- Automated setup of VLANs, port assignments, and policies
- Streamlined monitoring, analysis, and troubleshooting with minimal human error
- Simplified policy control to ensure consistent security enforcement
- Cost efficiency by reducing manual effort and downtime
Optimization & Performance
- Real-time traffic analysis to detect and eliminate bottlenecks
- Adaptive resource allocation that adjusts bandwidth based on demand
- Smarter quality of service QoS to prioritize critical business applications (VoIP, video conferencing, CRM)
Proactive Security
- Instant anomaly detection and malware identification using ML models
Predictive protection by spotting vulnerabilities before they are exploited - Automated threat response to isolate compromised devices and block malicious flows
In short: AI-powered switches can self-configure, self-optimize, and self-protect—the three traits traditional networks have lacked.
Where Will This Revolution Happen?
The impact of AI-based switching will be felt across every primary sector:
- Data Centers: AI switches will optimize traffic flow and resource allocation, leading to more efficient processing, lower power consumption, and reduced operational costs.
- Telecom Operators: By building a distributed AI infrastructure, operators can optimize traffic flows and evolve toward AI-RAN, enabling new AI-driven services and improving RAN efficiency.
- Enterprise Networks: IT teams will be freed from routine tasks like configuration and security enforcement, allowing them to focus on higher-value strategic initiatives.
- IoT Networks: For the massive data loads generated by connected devices, AI switches will be crucial for managing traffic and maintaining smooth, reliable performance.
The Road Ahead
AI is turning networks from static, reactive systems into self-learning, adaptive ecosystems. With advanced analytics, IT teams can identify patterns, predict failures, and take proactive measures to maintain network stability and resilience.
As these capabilities evolve, AI-powered switches will continuously learn from traffic and user behavior, automatically tuning themselves for optimal performance and security—without human intervention.
The result? Networks that operate as self-aware digital fabrics: intelligent, resilient, and continually optimizing to deliver the best possible experience for users and applications.
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