What is AI networking? Use cases, benefits and challenges
AI networking overview
“AI networking offers great potential to disrupt long-standing traditional networking operations to create a massive productivity increase.” Gartner, Innovation Insight: AI Networking Has the Potential to Revolutionize Network Operations
Artificial intelligence (AI) networking is an evolution of AIOps (AI for IT operations) that focuses on ongoing “day 2” management, maintenance and optimization of a network. It combines AI with networking infrastructure to automate and optimize IT operations.
Where AIOps has a broader focus on the information and operations (I&O) infrastructure level, AI networking is specific to the networking domain (data center switching, wired, wireless, LAN, WAN, SD-WAN, multicloud).
The term was coined by Gartner in 2023, but the concept existed previously with different titles that essentially referred to the same function. Vendors have called it intent-based networking, autonomous networks, self-driving networks and self-healing networks.
[Related: Cisco explains why AI networking and Ethernet fabric are a perfect match]
Use cases for AI networking
IT infrastructure is critical to today’s enterprise, but it can be complex and difficult to manage, and IT teams often require specific, high-level skills to identify, troubleshoot and solve network problems. Additionally, network managers are bombarded with alerts from all angles that can be difficult to sift through and prioritize. All of this is complicated by the ongoing talent shortage of IT workers, which makes automation an urgent matter.
AI networking seeks to transform traditional IT operations and make networks more intelligent, self-adaptive, efficient and reliable. The technology uses machine learning (ML), deep learning, natural language processing (NLP), generative AI (genAI) and other methods to monitor, troubleshoot and secure networks.
Core capabilities include:
Network automation
AI networking automates tasks including network configuration, monitoring and troubleshooting. This helps to improve performance, optimize resource allocation and reduce downtime. Tasks including configuration and incident management, software updating and others are also automated, as is recommendation and response.
Optimized ITSM
AI networking can optimize IT service management (ITSM) by handling the most basic level 1 and level 2 support issues (like password resets or hardware glitches). Leveraging NLP, chatbots and virtual agents can field the most common and simple service desk inquiries and help users troubleshoot. AI can also identify higher-level issues that go beyond step-by-step instructions and pass them along for human support.
AI networking can also help reduce trouble ticket false-positives by approving or rejecting tickets before they are acted on by the IT help desk. This can reduce the probability that human workers will chase tickets that either weren’t real problems in the first place, were mistakenly submitted or duplicated or were already resolved.
These AI handoffs can improve response times and reduce IT staff workloads, allowing them to focus on strategy and more advanced tasks. AI networking can also enhance operational efficiency and reduce human error caused by alert burnout.
Improved network management and performance
AI can analyze large amounts of network data and traffic and perform predictive network maintenance. Algorithms can identify patterns, anomalies and trends to anticipate potential issues before they degrade performance or cause unexpected network outages. IT teams can then act on these to prevent — or at least minimize — disruption.
AI networking systems can also identify bottlenecks, latency issues and congestion areas. Through ongoing analysis of workloads, resource utilization and demand forecasts, AI can allocate network resources, scale infrastructure, reroute traffic where needed and improve quality of service (QoS).
AI networking can also accomplish the following:
- Determine probability of hardware failure — such as a faulty CPU or flash drive — and resolve it when convenient.
- Correlate multiple datasets to determine source of latency or other issues.
- Respond to spikes in demand by requesting more bandwidth or rerouting to alternative channels based on noise, interference or congestion.
- Determine the cause of high server response times.
- Generate vendor-neutral troubleshooting or provisioning so that the IT team doesn’t have to be versed in every vendor’s specific platforms or jargon.
Incident management
Through correlation analysis, pattern recognition and other methods, AI algorithms can target incident causes and suggest remediation actions. This can reduce the IT team’s time and effort in identifying, diagnosing and resolving issues.
Intelligent security
AI can look at traffic, user behavior and system logs to pinpoint anomalies and flag potential security breaches or attacks. This can support proactive threat detection, response time, mitigation and network protection. AI can also respond to cybersecurity issues in real time.
Day 0 and day 1 functions
While it homes in on day 2 operations, AI networking also supports day 0 and day 1 functions, including network design, setup and recommendations to optimize network performance.
Main components of AI networking
AI networking incorporates numerous technologies, including the following:
Predictive analytics
Through data analysis and statistical models, AI can learn to understand a network and its policies. It can study and process predefined metrics, traffic flows, trends and patterns and compare them against established baselines.
Trend analysis and pattern recognition
Algorithms can analyze trends and use pattern recognition to make sense out of real-time and historical data. Through monitoring and observability, AI can process event and telemetry data to detect incidents as they occur.
The system does this by creating a baseline from historic data, then continually learning and refining — with or without human-in-the-loop — patterns of events based on data as well as human operator input, guidelines, reaction and interaction.
Event correlation
Using baseline models, time-series and topology information, AI can compress and correlate events across telemetry domains and group-related events, thus reducing the need for human intervention.
Closed-loop problem resolution
AI networking continuously learns and improves associations between events and human responses, whether through explicit actions, guidance or simple observation. This process might trigger a system to offer recommendations or take action itself based on its training and parameters.
GenAI
AI networking will increasingly use genAI and large language models (LLMs), which can offer suggestions or create specific, catered plans of action.
For instance, Gartner posits, an engineer could ask a ChatGPT-like interface to design a leaf-spine network (consisting of two switching layers) that could support 400 servers using Vendor A. Using data (both public and organization- and industry-specific) the platform could then generate the required configurations for this specific prompt.
Digital twins
Using a simulated nonproduction environment, enterprises can validate the impacts of network changes before they are deployed in the physical world. A combination of AI and digital twins can also work into a continuous integration/continuous delivery (CI/CD) pipeline that can allow for “what if” scenarios and to ensure that the network is operating as expected.
Gartner predicts that by 2026, 50% of networking vendors will offer a digital twin capability in their tools, up from 10% in 2023.
AI networking comparison to AIOps
AI networking and AIOps are closely intertwined as they both fuse AI and ML with networking. But there are important differences.
AIOps has been around longer as a term and concept. Coined by Gartner in 2017, the technology enhances decision-making across I&O by aggregating and contextualizing large amounts of operational data. Its stages include initial data collection, model training, automation, anomaly detection and continuous learning.
As opposed to AI networking, which starts at the more strategic day 2 maintaining, monitoring and optimizing of the network, AIOps begins with day 0 planning and design, including defining business strategies and outcomes and identifying customer needs.
The system then ingests historical and real-time streaming data, filters out “noisy” data and identifies patterns in data. As it evolves into day 1 automation and remediation, the platform’s functions grow increasingly sophisticated as the system collects more and more data and continues to learn.
Day 2 and 2-plus capabilities then address user experience — through such capabilities as infrastructure and device health metrics, application-based context and pre/post connection performance — and provide more comprehensive AI-driven support based on closed-loop automation and self-remediation.
AIOps is touted for its impact on user, operations and DevOps/app experiences and location services.
Some AIOps capabilities include the following:
- At-scale automation
- Automated detection and resolution of anomalies before they have an impact
- Ongoing performance analysis and optimization
- Aggregation of operations data from multiple, disparate IT environments
- Reduction of false alarms
- Increased detection of malware traffic and vulnerabilities
- Causation determination through ML-driven root-cause analysis
- Improved operator efficiency and user experience due to conversational interfaces powered by NLP
Benefits of AI networking
Gartner asserts that AI networking can drive operational management savings by up to 25%. This is because it can reduce support calls, allow for improved troubleshooting, increase network availability and optimize end-user experience “that can’t reasonably be achieved by scaling manual resources,” the tech firm says.
Notably, AI networking simplifies network management, security and application infrastructures even as they become more complex due to disparate data center, multicloud, colocation and edge environments, as well as increasing abstraction layers (Kubernetes or containers).
[Related: Arista Networks predicts Ethernet will power AI networking]
AI networking can also achieve the following:
- Quickly respond to problems before humans discover them and before failure occurs
- Predict and prevent network problems
- Correlate data sources to centralize problem identification via typology and comprehension of contextual network correlations
- Determine if an appropriate resolution is available for a certain issue and generate the data flow required for additional investigation
- Optimize resource allocation and streamline ITSM processes
- Strengthen security
- Provide insights to humans and enable data-driven decision-making
Ultimately, Gartner says it has seen enterprises experience savings of more than 50% in areas including troubleshooting and install time.
Furthermore, because AI networking simplifies network management, workers don’t need to have deep network configuration and troubleshooting skills. Enterprises can automate via genAI and remove manual human setup.
And, with fewer workers needed to manage the network, organizations struggling with a skills/experience gap can manage networks in-house rather than outsourcing.
Challenges of AI networking
Still, ambiguity around AI networking — what it means and how it works — remains, as the definition is new and fuses different concepts that vendors have been promoting for some time now.
[Related: AI vendors create fear, uncertainty and doubt — especially among information-hungry IT executives]
This lack of clarity and mixing of terminology has hampered implementation: Gartner estimates that the AI networking adoption rate is less than 10%. This indicates that enterprises are interested, but need more clarification on the technology and what it does.
Additional concerns include the following:
- Inaccurate AI recommendations leading to incorrect network configurations, creating unnecessary complexity or causing outages or other issues. This can stem from incorrect prompts from users, or occur when a system hasn’t been trained correctly or with enough data.
- Tool sprawl: Enterprises are increasingly concerned with “technical debt” and the associated costs.
- Culture and buy-in: Network management personnel can be risk-averse and may not trust AI tools or unproven recommendations. Similarly, some workers may eschew the technology for fear of it replacing their jobs, or because they are content with the status quo. These factors can limit the value of AI investment and its potential benefits.
- Enterprises may lack sufficient, quality data to provide proper insights or resolve issues.
- Technical skills for areas such as prompt engineering may require additional time and resource investment. While the idea is to automate workflows so that human workers can focus their talents on more strategic, high-level tasks, new data science skills may emerge as systems evolve. For instance, users may need to become versed in prompt engineering, or equipped with skills to effectively analyze AI outputs.
- Inflated expectations: There’s a lot of hype around AI. And because the technology is so new, there is little standardization and vendors may overstate their capabilities. This can lead to oversetting expectations that exceed reality, and enterprises may not get the value they’d hoped for, with systems providing incremental or negligible benefits.
Recommendations moving forward with AI networking
Gartner predicts that by 2027, 90% of enterprises will use some AI to automate day 2 network operations. Similarly, the firm says that by 2026, genAI technology will account for 20% of initial network configuration, up from near zero in 2023.
Moving forward, AI networking will be offered via numerous methods. These include the following:
- AIOps platforms that take a horizontal approach at a broad infrastructure level.
- Network vendors that incorporate AI into existing tools including SD-WAN, access points and switches.
- Multivendor tools that provide AI networking across several tools.
- Providers of a managed service incorporating AI networking.
As with any new or evolving technology, enterprises should proceed with care and due diligence. Gartner and other experts make numerous suggestions as enterprises explore AI networking technologies, their use cases and benefits. These include the following:
- Starting small and performing proof of concept (PoC) and testing before adopting tools and rolling them out into production. When developing a strategy, enterprises should act on the AI’s recommendations and predictions, then incrementally rely on automation as the system proves itself (or doesn’t) and human trust goes up.
- Identifying what type of AI networking system — AIOps platforms, network vendors, multivendors or managed service providers — work best for their particular enterprise based on resources and needs.
- Selecting AI networking vendors after determining whether their operational model is do-it-yourself (DIY) or managed network services (MNS), and also whether the networking environment is single vendor or multivendor. This will help companies determine which type of vendor is the right option for them.
- Requiring vendors to provide specific details and offer complete breakdowns of their tools and what they can (and cannot) do and deliver. Organizations should ensure that these potential future partners provide timelines. This should be from implementation to one to two years out (or longer).
- Identifying how network operations will change and evolving roles from network management. IT teams should be armed with data consumption capabilities and be able to analyze and act on AI recommendations.
- Justifying adoption by calculating cost savings and benefits when it comes to resource efficiency, network availability, performance and improved experiences.
Key takeaways for AI networking
- AI networking combines AI with networking infrastructure to automate and optimize IT operations.
- AI networking has the capability to transform network management by automating maintenance, troubleshooting and incident management. Algorithms have the enhanced capability to make data-driven predictions that improve security, performance and operations.
- AI networking incorporates a number of different technologies, including predictive and trend analysis, pattern recognition, digital twins and genAI.
- The benefits of AI networking include operational management savings of up to 25% through reduced support calls, improved troubleshooting, increased network availability and optimized end-user experience, among other things.
- Risks include inflated expectations and hype, inaccurate outputs, insufficient data to power systems and cultural buy-in. New skills may also be required, including prompt engineering and analysis of AI output.
- While AI networking and AIOps are sure to be disruptive, enterprises must be deliberate in their approach and determine the best platform for their business needs and strategies.
Synonyms, acronyms, abbreviations
- Artificial intelligence networking
- AI networking
- AI network
- AI networks
- Intent-based networking
- Autonomous networks
- Self-driving networks
- Self-healing networks
AI networking overview
“AI networking offers great potential to disrupt long-standing traditional networking operations to create a massive productivity increase.” Gartner, Innovation Insight: AI Networking Has the Potential to Revolutionize Network Operations
Artificial intelligence (AI) networking is an evolution of AIOps (AI for IT operations) that focuses on ongoing “day 2” management, maintenance and optimization of a network. It combines AI with networking infrastructure to automate and optimize IT operations.
Where AIOps has a broader focus on the information and operations (I&O) infrastructure level, AI networking is specific to the networking domain (data center switching, wired, wireless, LAN, WAN, SD-WAN, multicloud).
The term was coined by Gartner in 2023, but the concept existed previously with different titles that essentially referred to the same function. Vendors have called it intent-based networking, autonomous networks, self-driving networks and self-healing networks.
[Related: Cisco explains why AI networking and Ethernet fabric are a perfect match]
Use cases for AI networking
IT infrastructure is critical to today’s enterprise, but it can be complex and difficult to manage, and IT teams often require specific, high-level skills to identify, troubleshoot and solve network problems. Additionally, network managers are bombarded with alerts from all angles that can be difficult to sift through and prioritize. All of this is complicated by the ongoing talent shortage of IT workers, which makes automation an urgent matter.
AI networking seeks to transform traditional IT operations and make networks more intelligent, self-adaptive, efficient and reliable. The technology uses machine learning (ML), deep learning, natural language processing (NLP), generative AI (genAI) and other methods to monitor, troubleshoot and secure networks.
Core capabilities include:
Network automation
AI networking automates tasks including network configuration, monitoring and troubleshooting. This helps to improve performance, optimize resource allocation and reduce downtime. Tasks including configuration and incident management, software updating and others are also automated, as is recommendation and response.
Optimized ITSM
AI networking can optimize IT service management (ITSM) by handling the most basic level 1 and level 2 support issues (like password resets or hardware glitches). Leveraging NLP, chatbots and virtual agents can field the most common and simple service desk inquiries and help users troubleshoot. AI can also identify higher-level issues that go beyond step-by-step instructions and pass them along for human support.
AI networking can also help reduce trouble ticket false-positives by approving or rejecting tickets before they are acted on by the IT help desk. This can reduce the probability that human workers will chase tickets that either weren’t real problems in the first place, were mistakenly submitted or duplicated or were already resolved.
These AI handoffs can improve response times and reduce IT staff workloads, allowing them to focus on strategy and more advanced tasks. AI networking can also enhance operational efficiency and reduce human error caused by alert burnout.
Improved network management and performance
AI can analyze large amounts of network data and traffic and perform predictive network maintenance. Algorithms can identify patterns, anomalies and trends to anticipate potential issues before they degrade performance or cause unexpected network outages. IT teams can then act on these to prevent — or at least minimize — disruption.
AI networking systems can also identify bottlenecks, latency issues and congestion areas. Through ongoing analysis of workloads, resource utilization and demand forecasts, AI can allocate network resources, scale infrastructure, reroute traffic where needed and improve quality of service (QoS).
AI networking can also accomplish the following:
- Determine probability of hardware failure — such as a faulty CPU or flash drive — and resolve it when convenient.
- Correlate multiple datasets to determine source of latency or other issues.
- Respond to spikes in demand by requesting more bandwidth or rerouting to alternative channels based on noise, interference or congestion.
- Determine the cause of high server response times.
- Generate vendor-neutral troubleshooting or provisioning so that the IT team doesn’t have to be versed in every vendor’s specific platforms or jargon.
Incident management
Through correlation analysis, pattern recognition and other methods, AI algorithms can target incident causes and suggest remediation actions. This can reduce the IT team’s time and effort in identifying, diagnosing and resolving issues.
Intelligent security
AI can look at traffic, user behavior and system logs to pinpoint anomalies and flag potential security breaches or attacks. This can support proactive threat detection, response time, mitigation and network protection. AI can also respond to cybersecurity issues in real time.
Day 0 and day 1 functions
While it homes in on day 2 operations, AI networking also supports day 0 and day 1 functions, including network design, setup and recommendations to optimize network performance.
Main components of AI networking
AI networking incorporates numerous technologies, including the following:
Predictive analytics
Through data analysis and statistical models, AI can learn to understand a network and its policies. It can study and process predefined metrics, traffic flows, trends and patterns and compare them against established baselines.
Trend analysis and pattern recognition
Algorithms can analyze trends and use pattern recognition to make sense out of real-time and historical data. Through monitoring and observability, AI can process event and telemetry data to detect incidents as they occur.
The system does this by creating a baseline from historic data, then continually learning and refining — with or without human-in-the-loop — patterns of events based on data as well as human operator input, guidelines, reaction and interaction.
Event correlation
Using baseline models, time-series and topology information, AI can compress and correlate events across telemetry domains and group-related events, thus reducing the need for human intervention.
Closed-loop problem resolution
AI networking continuously learns and improves associations between events and human responses, whether through explicit actions, guidance or simple observation. This process might trigger a system to offer recommendations or take action itself based on its training and parameters.
GenAI
AI networking will increasingly use genAI and large language models (LLMs), which can offer suggestions or create specific, catered plans of action.
For instance, Gartner posits, an engineer could ask a ChatGPT-like interface to design a leaf-spine network (consisting of two switching layers) that could support 400 servers using Vendor A. Using data (both public and organization- and industry-specific) the platform could then generate the required configurations for this specific prompt.
Digital twins
Using a simulated nonproduction environment, enterprises can validate the impacts of network changes before they are deployed in the physical world. A combination of AI and digital twins can also work into a continuous integration/continuous delivery (CI/CD) pipeline that can allow for “what if” scenarios and to ensure that the network is operating as expected.
Gartner predicts that by 2026, 50% of networking vendors will offer a digital twin capability in their tools, up from 10% in 2023.
AI networking comparison to AIOps
AI networking and AIOps are closely intertwined as they both fuse AI and ML with networking. But there are important differences.
AIOps has been around longer as a term and concept. Coined by Gartner in 2017, the technology enhances decision-making across I&O by aggregating and contextualizing large amounts of operational data. Its stages include initial data collection, model training, automation, anomaly detection and continuous learning.
As opposed to AI networking, which starts at the more strategic day 2 maintaining, monitoring and optimizing of the network, AIOps begins with day 0 planning and design, including defining business strategies and outcomes and identifying customer needs.
The system then ingests historical and real-time streaming data, filters out “noisy” data and identifies patterns in data. As it evolves into day 1 automation and remediation, the platform’s functions grow increasingly sophisticated as the system collects more and more data and continues to learn.
Day 2 and 2-plus capabilities then address user experience — through such capabilities as infrastructure and device health metrics, application-based context and pre/post connection performance — and provide more comprehensive AI-driven support based on closed-loop automation and self-remediation.
AIOps is touted for its impact on user, operations and DevOps/app experiences and location services.
Some AIOps capabilities include the following:
- At-scale automation
- Automated detection and resolution of anomalies before they have an impact
- Ongoing performance analysis and optimization
- Aggregation of operations data from multiple, disparate IT environments
- Reduction of false alarms
- Increased detection of malware traffic and vulnerabilities
- Causation determination through ML-driven root-cause analysis
- Improved operator efficiency and user experience due to conversational interfaces powered by NLP
Benefits of AI networking
Gartner asserts that AI networking can drive operational management savings by up to 25%. This is because it can reduce support calls, allow for improved troubleshooting, increase network availability and optimize end-user experience “that can’t reasonably be achieved by scaling manual resources,” the tech firm says.
Notably, AI networking simplifies network management, security and application infrastructures even as they become more complex due to disparate data center, multicloud, colocation and edge environments, as well as increasing abstraction layers (Kubernetes or containers).
[Related: Arista Networks predicts Ethernet will power AI networking]
AI networking can also achieve the following:
- Quickly respond to problems before humans discover them and before failure occurs
- Predict and prevent network problems
- Correlate data sources to centralize problem identification via typology and comprehension of contextual network correlations
- Determine if an appropriate resolution is available for a certain issue and generate the data flow required for additional investigation
- Optimize resource allocation and streamline ITSM processes
- Strengthen security
- Provide insights to humans and enable data-driven decision-making
Ultimately, Gartner says it has seen enterprises experience savings of more than 50% in areas including troubleshooting and install time.
Furthermore, because AI networking simplifies network management, workers don’t need to have deep network configuration and troubleshooting skills. Enterprises can automate via genAI and remove manual human setup.
And, with fewer workers needed to manage the network, organizations struggling with a skills/experience gap can manage networks in-house rather than outsourcing.
Challenges of AI networking
Still, ambiguity around AI networking — what it means and how it works — remains, as the definition is new and fuses different concepts that vendors have been promoting for some time now.
[Related: AI vendors create fear, uncertainty and doubt — especially among information-hungry IT executives]
This lack of clarity and mixing of terminology has hampered implementation: Gartner estimates that the AI networking adoption rate is less than 10%. This indicates that enterprises are interested, but need more clarification on the technology and what it does.
Additional concerns include the following:
- Inaccurate AI recommendations leading to incorrect network configurations, creating unnecessary complexity or causing outages or other issues. This can stem from incorrect prompts from users, or occur when a system hasn’t been trained correctly or with enough data.
- Tool sprawl: Enterprises are increasingly concerned with “technical debt” and the associated costs.
- Culture and buy-in: Network management personnel can be risk-averse and may not trust AI tools or unproven recommendations. Similarly, some workers may eschew the technology for fear of it replacing their jobs, or because they are content with the status quo. These factors can limit the value of AI investment and its potential benefits.
- Enterprises may lack sufficient, quality data to provide proper insights or resolve issues.
- Technical skills for areas such as prompt engineering may require additional time and resource investment. While the idea is to automate workflows so that human workers can focus their talents on more strategic, high-level tasks, new data science skills may emerge as systems evolve. For instance, users may need to become versed in prompt engineering, or equipped with skills to effectively analyze AI outputs.
- Inflated expectations: There’s a lot of hype around AI. And because the technology is so new, there is little standardization and vendors may overstate their capabilities. This can lead to oversetting expectations that exceed reality, and enterprises may not get the value they’d hoped for, with systems providing incremental or negligible benefits.
Recommendations moving forward with AI networking
Gartner predicts that by 2027, 90% of enterprises will use some AI to automate day 2 network operations. Similarly, the firm says that by 2026, genAI technology will account for 20% of initial network configuration, up from near zero in 2023.
Moving forward, AI networking will be offered via numerous methods. These include the following:
- AIOps platforms that take a horizontal approach at a broad infrastructure level.
- Network vendors that incorporate AI into existing tools including SD-WAN, access points and switches.
- Multivendor tools that provide AI networking across several tools.
- Providers of a managed service incorporating AI networking.
As with any new or evolving technology, enterprises should proceed with care and due diligence. Gartner and other experts make numerous suggestions as enterprises explore AI networking technologies, their use cases and benefits. These include the following:
- Starting small and performing proof of concept (PoC) and testing before adopting tools and rolling them out into production. When developing a strategy, enterprises should act on the AI’s recommendations and predictions, then incrementally rely on automation as the system proves itself (or doesn’t) and human trust goes up.
- Identifying what type of AI networking system — AIOps platforms, network vendors, multivendors or managed service providers — work best for their particular enterprise based on resources and needs.
- Selecting AI networking vendors after determining whether their operational model is do-it-yourself (DIY) or managed network services (MNS), and also whether the networking environment is single vendor or multivendor. This will help companies determine which type of vendor is the right option for them.
- Requiring vendors to provide specific details and offer complete breakdowns of their tools and what they can (and cannot) do and deliver. Organizations should ensure that these potential future partners provide timelines. This should be from implementation to one to two years out (or longer).
- Identifying how network operations will change and evolving roles from network management. IT teams should be armed with data consumption capabilities and be able to analyze and act on AI recommendations.
- Justifying adoption by calculating cost savings and benefits when it comes to resource efficiency, network availability, performance and improved experiences.
Key takeaways for AI networking
- AI networking combines AI with networking infrastructure to automate and optimize IT operations.
- AI networking has the capability to transform network management by automating maintenance, troubleshooting and incident management. Algorithms have the enhanced capability to make data-driven predictions that improve security, performance and operations.
- AI networking incorporates a number of different technologies, including predictive and trend analysis, pattern recognition, digital twins and genAI.
- The benefits of AI networking include operational management savings of up to 25% through reduced support calls, improved troubleshooting, increased network availability and optimized end-user experience, among other things.
- Risks include inflated expectations and hype, inaccurate outputs, insufficient data to power systems and cultural buy-in. New skills may also be required, including prompt engineering and analysis of AI output.
- While AI networking and AIOps are sure to be disruptive, enterprises must be deliberate in their approach and determine the best platform for their business needs and strategies.
Synonyms, acronyms, abbreviations
- Artificial intelligence networking
- AI networking
- AI network
- AI networks
- Intent-based networking
- Autonomous networks
- Self-driving networks
- Self-healing networks
泛地關注資訊和營運 (I&O) 基礎設施級別，而 AI 網路則特定於網路領域（資料中心交換、有線、無線、LAN、WAN、SD-WAN、多雲）。
這個術語由 Gartner 於 2023 年創造，但該概念之前曾以不同的標題存在，但本質上指的是相同的功能。供應商將其稱為基於意圖的網路、自主網路、自動駕駛網路和自我修復網路。
[相關： 思科解釋了為什麼人工智慧網路和乙太網路結構是完美的搭配]
AI 網路的用例
IT 基礎架構對於當今的企業至關重要，但它可能非常複雜且難以管理，IT 團隊通常需要特定的高階技能來識別、排除和解決網路問題。此外，網路管理員還受到來自各個角度的警報的轟炸，這些警報可能難以篩選並確定優先順序。IT 人員持續的人才短缺使這一切變得更加複雜，這使得自動化成為一個緊迫的問題。
AI網路旨在改變傳統IT運作方式，讓網路更加智慧、適應性、有效率、可靠。該技術使用機器學習（ML）、深度學習、自然語言處理（NLP）、生成式人工智慧（genAI）和其他方法來監控、排除故障和保護網路。
核心能力包括：
網路自動化
人工智慧網路可自動執行網路配置、監控和故障排除等任務。這有助於提高效能、優化資源分配並減少停機時間。配置和事件管理、軟體更新等任務以及建議和回應也是自動化的。
優化的ITSM
AI 網路可以透過處理最基本的 1 級和 2 級支援問題（例如密碼重設或硬體故障）來優化 IT 服務管理 (ITSM)。利用 NLP、聊天機器人和虛擬代理可以處理最常見、最簡單的服務台查詢並幫助用戶排除故障。人工智慧還可以識別超出逐步指令的更高層級問題，並將其傳遞給人類支援。
人工智慧網路還可以在 IT 服務台採取行動之前批准或拒絕故障單，從而幫助減少故障單誤報。這可以降低人類工作人員追蹤本來就不是真正問題、錯誤提交或重複或已經解決的票證的可能性。
這些人工智慧交接可以縮短回應時間並減少 IT 員工的工作量，使他們能夠專注於策略和更高階的任務。人工智慧網路還可以提高營運效率並減少因警報倦怠而導致的人為錯誤。
改進的網路管理和效能
人工智慧可以分析大量的網路數據和流量並執行預測性網路維護。演算法可以識別模式、異常和趨勢，以便在潛在問題降低效能或導致意外網路中斷之前對其進行預測。然後，IT 團隊可以針對這些問題採取行動，以防止（或至少最大限度地減少）中斷。
人工智慧網路系統還可以識別瓶頸、延遲問題和擁塞區域。透過對工作負載、資源利用率和需求預測的持續分析，人工智慧可以分配網路資源、擴展基礎設施、根據需要重新路由流量並提高服務品質 ( QoS )。
人工智慧網路還可以完成以下任務：
- 確定硬體故障的可能性（例如 CPU 或快閃磁碟機故障）並在方便時解決它。
- 關聯多個資料集以確定延遲或其他問題的根源。
- 透過請求更多頻寬或根據雜訊、幹擾或擁塞重新路由到替代頻道來回應需求高峰。
- 確定伺服器回應時間較長的原因。
- 產生供應商中立的故障排除或配置，以便 IT 團隊不必精通每個供應商的特定平台或術語。
事件管理
透過關聯分析、模式識別等方法，人工智慧演算法可以針對事件原因並提出補救措施建議。這可以減少 IT 團隊識別、診斷和解決問題的時間和精力。
智慧安防
人工智慧可以查看流量、使用者行為和系統日誌，以找出異常情況並標記潛在的安全漏洞或攻擊。這可以支援主動威脅偵測、回應時間、緩解和網路保護。人工智慧還可以即時響應網路安全問題。
第 0 天和第 1 天函數
雖然人工智慧網路專注於第 2 天的操作，但它還支援第 0 天和第 1 天的功能，包括網路設計、設定和優化網路效能的建議。
AI網路的主要組成部分
人工智慧網路融合了多種技術，包括：
預測分析
透過數據分析和統計模型，人工智慧可以學習理解網路及其策略。它可以研究和處理預先定義的指標、流量、趨勢和模式，並將它們與既定的基準進行比較。
趨勢分析和模式識別
演算法可以分析趨勢並使用模式識別來理解即時和歷史數據。透過監控和可觀察性，人工智慧可以處理事件和遙測數據，以便在事件發生時檢測到它們。
該系統透過根據歷史數據創建基線，然後根據數據以及人類操作員輸入、指南、反應和交互不斷學習和完善（無論是否有人參與）事件模式來實現這一點。
事件關聯
使用基線模型、時間序列和拓撲訊息，人工智慧可以壓縮和關聯跨遙測域的事件和群組相關事件，從而減少人工幹預的需要。
閉環問題解決
人工智慧網路不斷學習並改善事件與人類反應之間的關聯，無論是透過明確的行動、指導或簡單的觀察。此過程可能會觸發系統根據其訓練和參數提供建議或自行採取行動。
基因人工智慧
人工智慧網路將越來越多地使用 genAI 和大型語言模型 (LLM)，它們可以提供建議或創建具體的、有針對性的行動計劃。
例如，Gartner 認為，工程師可以要求類似 ChatGPT 的介面來設計葉脊網路（由兩個交換層組成），該網路可以使用供應商 A 支援 400 台伺服器。 ）然後平台可以產生此特定提示所需的配置。
數位孿生
使用模擬的非生產環境，企業可以在將網路變更部署到實體世界之前驗證網路變更的影響。人工智慧和數位孿生的組合還可以用於持續整合/持續交付（CI/CD）管道，該管道可以允許「假設」場景並確保網路按預期運作。
Gartner 預測，到 2026 年，50% 的網路供應商將在其工具中提供數位孿生功能，高於 2023 年的 10%。
AI 網路與 AIOps 的比較
AI 網路和 AIOps 緊密相連，因為它們都將 AI 和 ML 與網路融合在一起。但也存在著重要的差異。
AIOps 作為一個術語和概念已經存在了很長時間。該技術由 Gartner 於 2017 年提出，透過聚合和關聯大量營運數據來增強整個 I&O 的決策。其階段包括初始資料收集、模型訓練、自動化、異常檢測和持續學習。
AI 網路從更具策略性的第 2 天維護、監控和優化網路開始，而 AIOps 則從第 0 天的規劃和設計開始，包括定義業務策略和結果以及識別客戶需求。
然後，系統攝取歷史和即時串流數據，過濾掉「噪音」數據並識別數據模式。隨著它發展到第一天的自動化和修復，隨著系統收集越來越多的數據並不斷學習，平台的功能變得越來越複雜。
然後，第2 天和第2 天以上的功能透過基礎設施和裝置運行狀況指標、基於應用程式的上下文以及連接前/連接後效能等功能來解決用戶體驗問題，並提供基於閉環自動化和自我管理的更全面的人工智慧驅動支援。補救措施。
AIOps 因其對用戶、營運、DevOps/應用程式體驗和位置服務的影響而備受推崇。
一些 AIOps 功能包括以下內容：
- 大規模自動化
- 在異常產生影響之前自動檢測和解決異常
- 持續的效能分析和優化
- 聚合來自多個不同 IT 環境的營運數據
- 減少誤報
- 增加對惡意軟體流量和漏洞的偵測
- 透過機器學習驅動的根本原因分析來確定因果關係
- 由於 NLP 支援的對話式介面提高了操作員效率和使用者體驗
人工智慧網路的好處
Gartner 聲稱 AI 網路可以將營運管理成本節省高達 25%。這是因為它可以減少支援電話、改進故障排除、提高網路可用性並優化最終用戶體驗，“這是透過擴展手動資源無法合理實現的”，該科技公司表示。
值得注意的是，人工智慧網路簡化了網路管理、安全性和應用基礎設施，即使它們因不同的資料中心、多雲、託管和邊緣環境以及不斷增加的抽象層（Kubernetes或容器）而變得更加複雜。
[相關：Arista Networks 預測乙太網路將為人工智慧網路提供動力]
AI網路還可以實現以下目標：
- 在人們發現問題之前和發生故障之前快速回應問題
- 預測和預防網路問題
- 透過類型學和上下文網路相關性的理解關聯資料來源以集中問題識別
- 確定某個問題是否有適當的解決方案，並產生額外調查所需的資料流
- 優化資源分配並簡化 ITSM 流程
- 加強安全保障
- 為人類提供見解並實現數據驅動的決策
最終，Gartner 表示，它發現企業在故障排除和安裝時間等方面節省了 50% 以上。
此外，由於人工智慧網路簡化了網路管理，工作人員不需要具備深厚的網路配置和故障排除技能。企業可以透過 genAI 自動化並消除手動設定。
而且，由於管理網絡所需的員工減少，因技能/經驗差距而苦苦掙扎的組織可以在內部管理網絡，而不是外包。
人工智慧網路的挑戰
儘管如此，人工智慧網路的模糊性——它的含義和工作原理——仍然存在，因為它的定義是新的，並且融合了供應商一段時間以來一直在推廣的不同概念。
[相關：人工智慧供應商製造恐懼、不確定性和懷疑——尤其是在資訊匱乏的 IT 主管中]
術語的不明確性和混雜性阻礙了實施：Gartner 估計 AI 網路的採用率不到 10%。這表明企業對此感興趣，但需要對該技術及其用途進行更多澄清。
其他問題包括：
- 不準確的人工智慧建議會導致不正確的網路配置，造成不必要的複雜性或導致中斷或其他問題。這可能源自於使用者的錯誤提示，或在系統未經過正確訓練或沒有足夠的資料時發生。
- 工具蔓延：企業越來越關注「技術債」和相關成本。
- 文化與認同：網路管理人員可能規避風險，可能不信任人工智慧工具或未經證實的建議。同樣，一些工人可能會避開這項技術，因為擔心它會取代他們的工作，或者因為他們滿足於現狀。這些因素可能會限制人工智慧投資的價值及其潛在收益。
- 企業可能缺乏足夠的高品質數據來提供正確的見解或解決問題。
- 快速工程等領域的技術技能可能需要額外的時間和資源投資。雖然我們的想法是實現工作流程自動化，以便人類員工可以將他們的才能集中在更具策略性的高階任務上，但隨著系統的發展，新的資料科學技能可能會出現。例如，使用者可能需要精通即時工程，或具備有效分析人工智慧輸出的技能。
- 過高的期望：圍繞人工智慧有很多炒作。而且由於該技術非常新，因此幾乎沒有標準化，供應商可能會誇大其能力。這可能會導致超越現實的過高期望，而且企業可能無法獲得他們所希望的價值，而係統提供的收益是增量的或可以忽略不計的。
推動人工智慧網路的建議
Gartner 預測，到 2027 年，90% 的企業將使用一些人工智慧來實現第二天網路營運的自動化。同樣，該公司表示，到 2026 年，genAI 技術將佔初始網路配置的 20%，而 2023 年比例接近零。
展望未來，人工智慧網路將透過多種方式提供。其中包括以下內容：
- AIOps 平台在廣泛的基礎設施層面採用橫向方法。
- 將 AI 融入現有工具（包括 SD-WAN、存取點和交換器）的網路供應商。
- 跨多種工具提供 AI 網路的多供應商工具。
- 結合人工智慧網路的託管服務提供者。
與任何新技術或不斷發展的技術一樣，企業應謹慎行事並盡職調查。當企業探索人工智慧網路技術、其用例和優勢時，Gartner 和其他專家提出了大量建議。其中包括以下內容：
- 在採用工具並將其投入生產之前從小規模開始進行概念驗證 (PoC) 和測試。在製定策略時，企業應該根據人工智慧的建議和預測採取行動，然後隨著系統證明自己（或沒有證明）以及人類信任度的上升而逐漸依賴自動化。
- 根據資源和需求，確定哪種類型的人工智慧網路系統（AIOps 平台、網路供應商、多供應商或主機服務供應商）最適合其特定企業。
- 在確定人工智慧網路供應商的營運模式是DIY（DIY）還是託管網路服務（MNS）以及網路環境是單一供應商還是多供應商之後選擇AI網路供應商。這將幫助公司確定哪種類型的供應商是他們的正確選擇。
- 要求供應商提供具體的細節，並提供其工具的完整細分以及他們可以（和不能）做什麼和交付的內容。組織應確保這些潛在的未來合作夥伴提供時間表。這應該是從實施到一到兩年（或更長）的時間。
- 確定網路營運將如何改變以及網路管理角色的演變。IT團隊應該具備數據消費能力，能夠分析人工智慧建議並採取行動。
- 透過計算資源效率、網路可用性、效能和改進體驗方面的成本節省和效益來證明採用的合理性。
人工智慧網路的關鍵要點
- AI 網路將 AI 與網路基礎架構結合，實現 IT 營運的自動化和最佳化。
- 人工智慧網路能夠透過自動化維護、故障排除和事件管理來改變網路管理。演算法具有增強的能力來進行資料驅動的預測，從而提高安全性、效能和操作。
AI networking overview
“AI networking offers great potential to disrupt long-standing traditional networking operations to create a massive productivity increase.” Gartner, Innovation Insight: AI Networking Has the Potential to Revolutionize Network Operations
Artificial intelligence (AI) networking is an evolution of AIOps (AI for IT operations) that focuses on ongoing “day 2” management, maintenance and optimization of a network. It combines AI with networking infrastructure to automate and optimize IT operations.
Where AIOps has a broader focus on the information and operations (I&O) infrastructure level, AI networking is specific to the networking domain (data center switching, wired, wireless, LAN, WAN, SD-WAN, multicloud).
The term was coined by Gartner in 2023, but the concept existed previously with different titles that essentially referred to the same function. Vendors have called it intent-based networking, autonomous networks, self-driving networks and self-healing networks.
[Related: Cisco explains why AI networking and Ethernet fabric are a perfect match]
Use cases for AI networking
IT infrastructure is critical to today’s enterprise, but it can be complex and difficult to manage, and IT teams often require specific, high-level skills to identify, troubleshoot and solve network problems. Additionally, network managers are bombarded with alerts from all angles that can be difficult to sift through and prioritize. All of this is complicated by the ongoing talent shortage of IT workers, which makes automation an urgent matter.
AI networking seeks to transform traditional IT operations and make networks more intelligent, self-adaptive, efficient and reliable. The technology uses machine learning (ML), deep learning, natural language processing (NLP), generative AI (genAI) and other methods to monitor, troubleshoot and secure networks.
Core capabilities include:
Network automation
AI networking automates tasks including network configuration, monitoring and troubleshooting. This helps to improve performance, optimize resource allocation and reduce downtime. Tasks including configuration and incident management, software updating and others are also automated, as is recommendation and response.
Optimized ITSM
AI networking can optimize IT service management (ITSM) by handling the most basic level 1 and level 2 support issues (like password resets or hardware glitches). Leveraging NLP, chatbots and virtual agents can field the most common and simple service desk inquiries and help users troubleshoot. AI can also identify higher-level issues that go beyond step-by-step instructions and pass them along for human support.
AI networking can also help reduce trouble ticket false-positives by approving or rejecting tickets before they are acted on by the IT help desk. This can reduce the probability that human workers will chase tickets that either weren’t real problems in the first place, were mistakenly submitted or duplicated or were already resolved.
These AI handoffs can improve response times and reduce IT staff workloads, allowing them to focus on strategy and more advanced tasks. AI networking can also enhance operational efficiency and reduce human error caused by alert burnout.
Improved network management and performance
AI can analyze large amounts of network data and traffic and perform predictive network maintenance. Algorithms can identify patterns, anomalies and trends to anticipate potential issues before they degrade performance or cause unexpected network outages. IT teams can then act on these to prevent — or at least minimize — disruption.
AI networking systems can also identify bottlenecks, latency issues and congestion areas. Through ongoing analysis of workloads, resource utilization and demand forecasts, AI can allocate network resources, scale infrastructure, reroute traffic where needed and improve quality of service (QoS).
AI networking can also accomplish the following:
- Determine probability of hardware failure — such as a faulty CPU or flash drive — and resolve it when convenient.
- Correlate multiple datasets to determine source of latency or other issues.
- Respond to spikes in demand by requesting more bandwidth or rerouting to alternative channels based on noise, interference or congestion.
- Determine the cause of high server response times.
- Generate vendor-neutral troubleshooting or provisioning so that the IT team doesn’t have to be versed in every vendor’s specific platforms or jargon.
Incident management
Through correlation analysis, pattern recognition and other methods, AI algorithms can target incident causes and suggest remediation actions. This can reduce the IT team’s time and effort in identifying, diagnosing and resolving issues.
Intelligent security
AI can look at traffic, user behavior and system logs to pinpoint anomalies and flag potential security breaches or attacks. This can support proactive threat detection, response time, mitigation and network protection. AI can also respond to cybersecurity issues in real time.
Day 0 and day 1 functions
While it homes in on day 2 operations, AI networking also supports day 0 and day 1 functions, including network design, setup and recommendations to optimize network performance.
Main components of AI networking
AI networking incorporates numerous technologies, including the following:
Predictive analytics
Through data analysis and statistical models, AI can learn to understand a network and its policies. It can study and process predefined metrics, traffic flows, trends and patterns and compare them against established baselines.
Trend analysis and pattern recognition
Algorithms can analyze trends and use pattern recognition to make sense out of real-time and historical data. Through monitoring and observability, AI can process event and telemetry data to detect incidents as they occur.
The system does this by creating a baseline from historic data, then continually learning and refining — with or without human-in-the-loop — patterns of events based on data as well as human operator input, guidelines, reaction and interaction.
Event correlation
Using baseline models, time-series and topology information, AI can compress and correlate events across telemetry domains and group-related events, thus reducing the need for human intervention.
Closed-loop problem resolution
AI networking continuously learns and improves associations between events and human responses, whether through explicit actions, guidance or simple observation. This process might trigger a system to offer recommendations or take action itself based on its training and parameters.
GenAI
AI networking will increasingly use genAI and large language models (LLMs), which can offer suggestions or create specific, catered plans of action.
For instance, Gartner posits, an engineer could ask a ChatGPT-like interface to design a leaf-spine network (consisting of two switching layers) that could support 400 servers using Vendor A. Using data (both public and organization- and industry-specific) the platform could then generate the required configurations for this specific prompt.
Digital twins
Using a simulated nonproduction environment, enterprises can validate the impacts of network changes before they are deployed in the physical world. A combination of AI and digital twins can also work into a continuous integration/continuous delivery (CI/CD) pipeline that can allow for “what if” scenarios and to ensure that the network is operating as expected.
Gartner predicts that by 2026, 50% of networking vendors will offer a digital twin capability in their tools, up from 10% in 2023.
AI networking comparison to AIOps
AI networking and AIOps are closely intertwined as they both fuse AI and ML with networking. But there are important differences.
AIOps has been around longer as a term and concept. Coined by Gartner in 2017, the technology enhances decision-making across I&O by aggregating and contextualizing large amounts of operational data. Its stages include initial data collection, model training, automation, anomaly detection and continuous learning.
As opposed to AI networking, which starts at the more strategic day 2 maintaining, monitoring and optimizing of the network, AIOps begins with day 0 planning and design, including defining business strategies and outcomes and identifying customer needs.
The system then ingests historical and real-time streaming data, filters out “noisy” data and identifies patterns in data. As it evolves into day 1 automation and remediation, the platform’s functions grow increasingly sophisticated as the system collects more and more data and continues to learn.
Day 2 and 2-plus capabilities then address user experience — through such capabilities as infrastructure and device health metrics, application-based context and pre/post connection performance — and provide more comprehensive AI-driven support based on closed-loop automation and self-remediation.
AIOps is touted for its impact on user, operations and DevOps/app experiences and location services.
Some AIOps capabilities include the following:
- At-scale automation
- Automated detection and resolution of anomalies before they have an impact
- Ongoing performance analysis and optimization
- Aggregation of operations data from multiple, disparate IT environments
- Reduction of false alarms
- Increased detection of malware traffic and vulnerabilities
- Causation determination through ML-driven root-cause analysis
- Improved operator efficiency and user experience due to conversational interfaces powered by NLP
Benefits of AI networking
Gartner asserts that AI networking can drive operational management savings by up to 25%. This is because it can reduce support calls, allow for improved troubleshooting, increase network availability and optimize end-user experience “that can’t reasonably be achieved by scaling manual resources,” the tech firm says.
Notably, AI networking simplifies network management, security and application infrastructures even as they become more complex due to disparate data center, multicloud, colocation and edge environments, as well as increasing abstraction layers (Kubernetes or containers).
[Related: Arista Networks predicts Ethernet will power AI networking]
AI networking can also achieve the following:
- Quickly respond to problems before humans discover them and before failure occurs
- Predict and prevent network problems
- Correlate data sources to centralize problem identification via typology and comprehension of contextual network correlations
- Determine if an appropriate resolution is available for a certain issue and generate the data flow required for additional investigation
- Optimize resource allocation and streamline ITSM processes
- Strengthen security
- Provide insights to humans and enable data-driven decision-making
Ultimately, Gartner says it has seen enterprises experience savings of more than 50% in areas including troubleshooting and install time.
Furthermore, because AI networking simplifies network management, workers don’t need to have deep network configuration and troubleshooting skills. Enterprises can automate via genAI and remove manual human setup.
And, with fewer workers needed to manage the network, organizations struggling with a skills/experience gap can manage networks in-house rather than outsourcing.
Challenges of AI networking
Still, ambiguity around AI networking — what it means and how it works — remains, as the definition is new and fuses different concepts that vendors have been promoting for some time now.
[Related: AI vendors create fear, uncertainty and doubt — especially among information-hungry IT executives]
This lack of clarity and mixing of terminology has hampered implementation: Gartner estimates that the AI networking adoption rate is less than 10%. This indicates that enterprises are interested, but need more clarification on the technology and what it does.
Additional concerns include the following:
- Inaccurate AI recommendations leading to incorrect network configurations, creating unnecessary complexity or causing outages or other issues. This can stem from incorrect prompts from users, or occur when a system hasn’t been trained correctly or with enough data.
- Tool sprawl: Enterprises are increasingly concerned with “technical debt” and the associated costs.
- Culture and buy-in: Network management personnel can be risk-averse and may not trust AI tools or unproven recommendations. Similarly, some workers may eschew the technology for fear of it replacing their jobs, or because they are content with the status quo. These factors can limit the value of AI investment and its potential benefits.
- Enterprises may lack sufficient, quality data to provide proper insights or resolve issues.
- Technical skills for areas such as prompt engineering may require additional time and resource investment. While the idea is to automate workflows so that human workers can focus their talents on more strategic, high-level tasks, new data science skills may emerge as systems evolve. For instance, users may need to become versed in prompt engineering, or equipped with skills to effectively analyze AI outputs.
- Inflated expectations: There’s a lot of hype around AI. And because the technology is so new, there is little standardization and vendors may overstate their capabilities. This can lead to oversetting expectations that exceed reality, and enterprises may not get the value they’d hoped for, with systems providing incremental or negligible benefits.
Recommendations moving forward with AI networking
Gartner predicts that by 2027, 90% of enterprises will use some AI to automate day 2 network operations. Similarly, the firm says that by 2026, genAI technology will account for 20% of initial network configuration, up from near zero in 2023.
Moving forward, AI networking will be offered via numerous methods. These include the following:
- AIOps platforms that take a horizontal approach at a broad infrastructure level.
- Network vendors that incorporate AI into existing tools including SD-WAN, access points and switches.
- Multivendor tools that provide AI networking across several tools.
- Providers of a managed service incorporating AI networking.
As with any new or evolving technology, enterprises should proceed with care and due diligence. Gartner and other experts make numerous suggestions as enterprises explore AI networking technologies, their use cases and benefits. These include the following:
- Starting small and performing proof of concept (PoC) and testing before adopting tools and rolling them out into production. When developing a strategy, enterprises should act on the AI’s recommendations and predictions, then incrementally rely on automation as the system proves itself (or doesn’t) and human trust goes up.
- Identifying what type of AI networking system — AIOps platforms, network vendors, multivendors or managed service providers — work best for their particular enterprise based on resources and needs.
- Selecting AI networking vendors after determining whether their operational model is do-it-yourself (DIY) or managed network services (MNS), and also whether the networking environment is single vendor or multivendor. This will help companies determine which type of vendor is the right option for them.
- Requiring vendors to provide specific details and offer complete breakdowns of their tools and what they can (and cannot) do and deliver. Organizations should ensure that these potential future partners provide timelines. This should be from implementation to one to two years out (or longer).
- Identifying how network operations will change and evolving roles from network management. IT teams should be armed with data consumption capabilities and be able to analyze and act on AI recommendations.
- Justifying adoption by calculating cost savings and benefits when it comes to resource efficiency, network availability, performance and improved experiences.
Key takeaways for AI networking
- AI networking combines AI with networking infrastructure to automate and optimize IT operations.
- AI networking has the capability to transform network management by automating maintenance, troubleshooting and incident management. Algorithms have the enhanced capability to make data-driven predictions that improve security, performance and operations.
- AI networking incorporates a number of different technologies, including predictive and trend analysis, pattern recognition, digital twins and genAI.
- The benefits of AI networking include operational management savings of up to 25% through reduced support calls, improved troubleshooting, increased network availability and optimized end-user experience, among other things.
- Risks include inflated expectations and hype, inaccurate outputs, insufficient data to power systems and cultural buy-in. New skills may also be required, including prompt engineering and analysis of AI output.
- While AI networking and AIOps are sure to be disruptive, enterprises must be deliberate in their approach and determine the best platform for their business needs and strategies.
Synonyms, acronyms, abbreviations
- Artificial intelligence networking
- AI networking
- AI network
- AI networks
- Intent-based networking
- Autonomous networks
- Self-driving networks
- Self-healing networks