No, AI Won’t Fully Automate Wi-Fi Network Management for Enterprises. But Here’s What It Can Do

Enterprise Wi-Fi networking teams are experiencing big gains with Artificial Intelligence (AI), but full automation is unlikely. AI tools are not immune to hallucinations and make the occasional mistake, which is too risky for high-stakes decision-making. However, ABI Research still sees AI transforming the industry in profound ways.

The Wi-Fi ecosystem is currently championing the potential of new, advanced forms of Artificial Intelligence (AI), with Generative AI (Gen AI) and Agentic AI tools promoted as holding the keys to autonomous networking.

Complete automation of Wi-Fi networking is appealing from a strategic business perspective. Enterprises face skills shortages, sophisticated cyberthreats, and more robust data needs. AI simplifies much of Wi-Fi network management, reducing labor costs and boosting cybersecurity.

Yet, while AI will undoubtedly be a key resource for enterprise networking, ABI Research does not believe that it will fully automate Wi-Fi network management anytime soon. Many high-stakes decisions still require human intervention. Outsourcing the most important networking decisions to AI is risky in the eyes of enterprises.

That said, organizations still recognize the value of AI-enhanced networks. According to Cisco’s 2024 Global Networking Trends Report, 60% of Information Technology (IT) leaders and professionals plan to leverage AI for predictive network automation within 2 years.

In this blog, we will cover the top AI use cases for Wi-Fi, the common challenges associated with its implementation, and how AI will change the Wi-Fi industry.

Key Takeaways:

• AI is reshaping enterprise Wi-Fi, but won’t fully automate it. Humans remain essential for oversight, decision-making, and addressing complex challenges, despite AI’s ability to reduce costs, simplify network management, and reduce maintenance costs.
• Different forms of AI serve unique roles. From traditional AI for resource management to Gen AI for natural language tools and Agentic AI for workflows, enterprises are advancing step by step toward future autonomous networks.
• AI brings clear benefits to Wi-Fi networks. Use cases include optimizing user experiences, saving energy, automating maintenance, improving SLA tracking, and reinforcing cybersecurity with real-time anomaly detection.
• Challenges still limit widespread adoption. Multi-vendor interoperability, observability gaps, skill shortages, and concerns about predictable outcomes mean enterprises must carefully plan AI integration into Wi-Fi environments.
• Equipment vendors must shift their business models. The Wi-Fi market is shifting from a hardware-centric sales model to software-based and as-a-Service (aaS) pricing. Equipment vendors are responding by focusing on recurring revenue streams, rather than on-time purchases.

Types of AI for Enterprise Wi-Fi Networking

AI has progressed at lightning speed in recent years, and it shows no signs of stopping. The four phases of AI are outlined below:

• Traditional AI: Consumers and enterprises have been using this basic form of AI for over a decade now. Traditional AI is used in Wi-Fi networks to analyze user behavior patterns and enhance Radio Resource Management (RRM). However, this type of AI cannot contextualize data.
• Gen AI: ChatGPT’s launch in 2022 introduced the world to the next generation of AI, Gen AI. It excels at creating new content, such as text, images, and interactive tools. In terms of enterprise networking, the Large Language Models (LLMs) used in Gen AI tools have been useful for adding natural language conversation to chatbots.
• Agentic AI: Introduced in 2H 2024, Agentic AI is capable of performing entire workflows autonomously, from start to finish. AI Agents can work together across disparate platforms to solve complex networking challenges and make recommendations. Currently, humans still need to be “in the loop” for critical decision-making.
• Autonomous AI: Looking into the future, the next phase of AI evolution will hypothetically facilitate fully autonomous networks. AI agents would understand the intricacies of a Wi-Fi network and automatically deploy AI agents to optimize performance and build new applications. However, regulatory, ethical, and security challenges remain before we reach this stage.

AI Use Cases in Wi-Fi Networks

AI unlocks a wide range of enterprise networking use cases, including:

Improved User Experiences

AI excels at tracking traffic patterns, which network administrators can use to optimize user experiences. AI provides suggestions on where to allocate resources and at what time, based on anticipated demand. More advanced AI tools can automatically perform this action.

For example, Cisco’s gen AI-powered Deep Network Model dynamically optimizes network resources based on the specific task at hand.

Increased Energy Efficiency

Wi-Fi networks consume a tremendous amount of energy, with residential Customer Premises Equipment (CPE) consuming the same amount as 6.82 million homes in the European Union (EU). Enterprises workloads are even more demanding, making sustainability a key use case for AI.

AI reduces energy usage and costs by automatically powering down network infrastructure when not being used. It can also accurately predict traffic demand, ensuring network operators strike a balance between performance and energy consumption.

For instance, HPE Aruba Networking’s Wi-Fi 7-ready 730 Series APs integrate AI to provide dynamic power-saving features.

Streamlined Network Maintenance

Network engineers leverage AI to accelerate the troubleshooting process for hardware and configuration issues, both off-site and on-site. Beyond that, AI is being used to track Wi-Fi equipment, which simplifies inventory counts and verifying physical and licensing statuses.

For example, Huawei’s AssurSpirit is a tool built to troubleshoot complex networks in data centers. It can manage more than 100 types of alarms, each with its own process. The tool also uses an LLM that draws on the customer’s expert knowledge to provide suggestions.

Tracking SLAs

Many organizations outsource the design, installation, and management processes of Wi-Fi networking to Managed Service Providers (MSPs). These organizations then rely on Service-Level Agreements (SLAs) to decipher whether or not the MSP is living up to its end of the deal. AI simplifies this task by collecting the necessary data across the network to verify Quality of Service (QoS) metrics. This is particularly valuable for large, complex, and sprawling networks. It’s also useful for multi-tenant networks.

Ruckus AI leverages cloud-based network assurance to help track and fulfill SLAs, including through the use of advanced AI algorithms for network health monitoring. AI is also employed to rapidly identify and prioritize network incidents in real time.

Reinforced Security

AI has proven to be beneficial for cybersecurity, with Darktrace’s 2025 State of AI Cybersecurity report indicating that 78% of Chief Information Security Officers (CISOs) have experienced positive impacts. In terms of enterprise Wi-Fi networks, AI scans the network to detect anomalies and prevent intrusions. Furthermore, AI supports zero-trust architecture by enabling continuous checks and flexible access control.

For example, across late 2024 and early 2025, Juniper Networks rolled out its Secure AI-Native Edge solution. It combines security and networking functions into Mist AI. This rollout also introduced Mist Security Assurance, giving operators a single platform to manage networks across data centers, campuses, and branch sites with greater visibility and control.

What Challenges Come with AI-Enabled Wi-Fi Networks?

Some of the biggest challenges associated with integrating AI into Wi-Fi networks are:

• Deterministic Outcomes: AI automation tools hold immense potential in Wi-Fi networking, but desired outcomes are no guarantee. In mission- and safety-critical sectors like healthcare and industrial manufacturing, enterprises still prefer the predictability of manual processes.
• Identifying Issues to be Solved with AI: Many organizations lack clarity on the specific networking problems they want AI to solve. Labeling can help here, but companies should be sure labels keep pace with evolving cyberthreats.
• Multi-Vendor Environments: Most large enterprise networks use equipment from multiple vendors. At the same time, vendors typically only support AI capabilities on their own products, creating interoperability challenges.
• Network Observability: Companies that lack end-to-end network visibility will fail to use AI effectively. Broad observability is required to monitor key metrics and verify if AI is producing the desired results.
• Staff Competencies: Managing an AI-powered network requires considerable skill sets, of which there is a shortage. Furthermore, network automation continues to rely on human staff to set parameters and business goals. At least in the short term, AI’s transformative potential is limited to an organization’s technical competencies.

Four Ways AI Will Change Enterprise Network Management

The innovative capabilities of AI have a profound positive impact on enterprises that need to simplify Wi-Fi network management. The way IT teams interact with Wi-Fi networks, what businesses can accomplish, and how the market perceives networking solutions is evolving. Below are four ways ABI Research forecasts that AI will change enterprise network management:

• AI will cut headcounts and reshape the workforce. AI-enhanced networks will reduce staffing needs and enable organizations to achieve more with the same resources. To realize the full value of AI in Wi-Fi networks, enterprises need to focus on upskilling current employees and/or hiring new talent with proficiency in AI tooling.
• AI will transform SLA tracking and KPI management. AI will alter the way organizations track SLAs, as it automatically monitors the network and reads data to determine if Key Performance Indicators (KPIs) are being met. AI enables organizations to customize network resources for specific business objectives. Moreover, solutions like CommScope’s RUCKUS provide MSPs with an AI-powered analytics dashboard where they can more easily manage different clients' projects.
• AI will empower budget-constrained firms to scale. AI means that organizations with limited IT budgets can accomplish things they previously couldn’t. AI-powered tools will enable even smaller firms to gain network-wide visibility without expansive and well-experienced teams.
• AI will drive a shift to OPEX consumption models. Finally, ABI Research forecasts that AI will drive demand for more Operating Expenditure (OPEX)-based consumption models, such as Network-as-a-Service (NaaS). This is primarily due to AI’s ability to dynamically optimize network resources.

On the equipment vendor side of things, AI is accelerating demand for as-a-Service business and service models. Wi-Fi vendors have been using the following business models to monetize AI.

Table 1: Wi-Fi AP Vendors’ Business Models in the AI Era

(Source: ABI Research)

Get an in-depth study of AI-enabled networking capabilities across all the leading Wi-Fi AP vendors by downloading ABI Research’s AI's Revolutionary Potential in Enterprise Wi-Fi Network Management presentation.