As AI continues to mature, its integration into the enterprise network will not be optional. It will be foundational to deliver the performance, resilience, and agility that modern enterprises require.
The strategic importance of enterprise networking has increased exponentially since the early days of the internet, becoming a cornerstone of an enterprise’s operational platform and customer experience. The role of artificial intelligence (AI) is far-reaching in revolutionizing the next generation of networking technology and services through its dynamic functionality, which drives faster speeds, greater reliability, improved efficiency, and enhanced security. In parallel, AI is transforming enterprise IT more broadly as enterprises seek to integrate AI into their layered IT infrastructure to enhance productivity and accelerate market responsiveness.
AI’s impacts on enterprise networking span multiple facets from intelligent traffic routing to real-time threat detection, playing an integral and important role in how networks are monitored, managed, and secured. These critical network functions utilize AI technologies and applications, including machine learning (ML), data analytics, and automation, to make real-time decisions, predict network issues, adapt to changing operational conditions, and optimize network resources.
Beyond the benefits of automation, efficiency, and optimization, AI is reshaping the entire networking landscape. It is placing new demands on infrastructure, altering security postures, and accelerating the evolution of data center and cloud connectivity. AI’s powerful algorithmic and predictive capabilities are adding intelligence to the network with use cases spanning core IP transport, network management, and network security. This includes the ability to optimize and simplify policy and configuration management, automate orchestrator and service assurance tasks, enhance root cause and predictive analytics, and strengthen attack prevention and threat detection.
Ultimately, AI serves as a catalyst for change across the entire networking stack. Service providers and enterprises alike must re-evaluate traditional network strategies and adopt more flexible, intelligent, and scalable approaches to keep pace with this rapidly changing environment.
See also: How AI Is Forcing an IT Infrastructure Rethink
Improving Network Management Efficiency
One of the most immediate and visible impacts of AI is on network management. This includes network visibility, observability, and performance optimization. Enterprise networks today are more distributed, dynamic, and complex than ever before. Additionally, networking has become increasingly critical to an enterprise’s core IT infrastructure as business applications transition to a cloud-based configuration. AI plays a critical role in managing networks to ensure the highest levels of efficiency, reliability, and security.
AI technology, applied through AIOps (Artificial Intelligence for IT Operations) in the enterprise networking context, enables real-time anomaly detection and predictive analytics, driving pre-emptive and automated network issue resolution. According to a Cisco survey, 60% of IT leaders expect to have AI-enabled predictive automation in place across all domains to simplify network operations within the next two years.
The positive impacts of AI as a driver of greater network management efficiency can be seen through metric-based improvements in MTTR (mean time to resolution), policy enforcement accuracy, and the prioritization of IT help desk tickets. Consequently, the user experience is substantially improved in addition to the benefits to overall business operations from a more reliable and efficient IT infrastructure.
Precision-based Network Routing and Capacity Planning
AI is also revolutionizing the way network administrators plan and manage network resources. By leveraging ML algorithms, AI-based tools can predict traffic patterns, optimize routing, and predict capacity requirements.
The very applications driving AI adoption, from generative models to intelligent automation platforms, are bandwidth-intensive and latency-sensitive. As a result, enterprise networks must be able to scale to handle significant increases in data movement between edge devices, centralized data centers, and multi-cloud environments. This has spurred a reassessment of network capacity planning, architecture design, and connectivity strategies. Meanwhile, the increasing capacity requirements driven by AI are leading to greater demand for higher speed network deployments upwards to 400G and 800G.
AI significantly improves enterprise network capacity planning by refining prediction accuracy, automating resource allocation, optimizing capacity utilization, and enhancing overall network performance. AI-powered tools utilize machine-to-machine learning to analyze historical data, identify patterns, and predict future network demands, enabling more informed capacity planning decisions. This results in optimized resource utilization, reduced costs, and enhanced network reliability.
Enhanced SD-WAN
Software-defined wide area networking (SD-WAN) has revolutionized enterprise networking for the cloud IT model by enabling efficient alignment of bandwidth allocation with business application requirements in a dynamic manner. AI extends the intelligent capabilities of SD-WAN to a new level, resulting in better performance and improved efficiency. AI-enabled SD-WAN delivers a more flexible network fabric to satisfy enterprise performance, security, and availability requirements. AI functionality includes application-aware routing that intelligently steers traffic based on administratively defined policies and real-time network conditions, automatically selecting the best network path. The benefits include more efficient bandwidth utilization, lower costs, and improved application performance.
Fortified Network Security
Security dynamics are also shifting in the enterprise networking context. According to a Cisco survey, 40% of IT leaders and professionals indicated that cybersecurity is the number one issue impacting their enterprise network strategy. In the security context, AI is both a tool and a target. On the one hand, it enables more effective detection of anomalies and threats across the network. On the other hand, malicious actors are deploying AI to develop more sophisticated attacks. Enterprises and network service providers must now address this duality by ensuring that AI-enhanced security tools are part of a broader zero-trust strategy that continuously adapts to emerging risks. AI can be integrated into security devices, such as firewalls and intrusion detection systems, to enhance functionality by leveraging AI’s data processing capabilities, predictive analytics, and real-time decision-making. Secure Access Service Edge (SASE) solutions, which pair SD-WAN with cloud and identity-based security in a zero-trust network access framework, are incorporating AI technology to enhance security and performance capabilities. According to the Cisco survey, 54% of IT leaders plan to have AI-enabled SASE cloud architecture in place by 2026.
AI’s Impact on Data Center Networking
AI is a key driver of increased demand for network capacity, especially within data centers. As AI models are increasingly adopted, fueling larger volumes of data processing in more complex IT environments, there’s a need for faster data transfer speeds and higher bandwidth speeds. AI workloads are resource-intensive, often requiring specialized infrastructure and closer proximity to data sources to reduce latency. This is leading to increased demand for high-density and higher speed connectivity within and between data centers, as well as the need for seamless integration across edge, core, and cloud environments. According to a Ciena report, data center interconnect (DCI) bandwidth will increase 40-60% CAGR over the next five years, which is at least double the historical rate of growth.
The Increasing Value of a Managed Network Solution
AI and ML technologies are enhancing the strategic value of solutions offered by managed network service providers through improved cost efficiencies, optimized network performance, and faster incident response that ultimately improve business continuity, reduce operational costs, and drive better user experiences. With AI, managed network service (MNS) providers can ingest and analyze vast volumes of network data in real time with end-to-end visibility, enabling predictive maintenance, faster root cause analysis, and automated incident response. This reduces downtime and empowers IT teams to focus on strategy rather than firefighting.
With the aim of zero-human-touch incident response, MNS providers are implementing AI and automation techniques to improve both the efficiency and efficacy of managing network operations. These AI models, underpinned by high-powered data analytics and predictive capabilities, are being trained to operate in more of an unsupervised, fully automated construct. Additionally, the adoption of AI-powered chatbots, such as ChatGPT, and natural language processing (NLP) technologies are automating technical support interactions, as well as network performance and root cause analysis reporting. The result is simplified network management, improved incident response, and optimized network performance while driving substantial efficiency gains and lowering enterprise costs.
As AI continues to mature, its integration into the enterprise network will not be optional. It will be foundational to deliver the performance, resilience, and agility that modern enterprises require. This reality is propelling enterprises to re-evaluate their network strategies and partner with an MNS provider partner to help navigate the AI-driven, transformed IT landscape, ensuring a sustainable competitive edge. The potential advantages of this approach to leveraging innovative AI technologies in the enterprise networking environment are substantial.
