WBA pushes common AI frameworks to modernise Wi-Fi

The Wireless Broadband Alliance has published a report on applying artificial intelligence and machine learning across Wi-Fi networks, focusing on common frameworks to reduce fragmentation across vendors and deployments.

Titled AI/ML for Wi-Fi: Enabling Scalable, Intelligent Wi-Fi Ecosystems, the report argues that Wi-Fi operations are reaching a point where traditional, rule-based management no longer keeps pace. It links the shift to denser deployments, a wider mix of devices, and the expanding use of Wi-Fi in environments that require consistent performance.

Operational shift

The report describes a move from reactive troubleshooting to predictive monitoring and automated optimisation. It also outlines expected business outcomes, including lower operating costs, improved reliability and security, and a better end-user experience.

Wi-Fi networks now support workloads such as enterprise collaboration tools, industrial automation, immersive media and AI-related compute activity. These use cases add complexity that makes manual and rules-based approaches less effective at scale.

The guidance is aimed at device makers, network operators, enterprise IT teams and policymakers. It covers how AI and machine learning can be integrated across clients, access points, edge infrastructure and cloud systems.

Fragmentation risk

A central theme is the risk of industry fragmentation. The report points to proprietary implementations, inconsistent data quality and closed interfaces as factors that slow innovation and raise integration costs in multi-vendor environments.

Rather than standardising algorithms, it recommends interoperable frameworks. Key priorities include common approaches to data models, telemetry, APIs and model lifecycle management.

Data is also highlighted as a constraint. The report calls for shared datasets, federated learning and stronger governance to scale AI and machine learning across Wi-Fi, and for consistent inputs across devices, vendors and operating contexts.

Hybrid architecture

The report expects hybrid AI architectures to dominate, with intelligence distributed across clients, access points, edge systems and cloud platforms. It presents this as a way to meet performance requirements while reflecting the operational realities of large networks.

It also identifies "AI/ML-native Wi-Fi" as a longer-term direction. In that context, it references Wi-Fi 8 (IEEE 802.11bn), citing features such as DBE and MAPC that it says would work best when driven by an AI and machine learning engine.

The report was produced by the WBA AI/ML for Wi-Fi Project Group. Intel led the work, with Airties, Cisco and HPE as co-leads.

The alliance plans to share the findings with industry stakeholders and standards bodies, including the Wi-Fi Alliance and IEEE 802.11 meetings, to inform discussion on interoperability, data practices and architectures for intelligent Wi-Fi.

Tiago Rodrigues, president and CEO of the Wireless Broadband Alliance, said the industry needed common approaches as Wi-Fi takes on a more critical role.

"Wi-Fi is now expected to perform like critical infrastructure across homes, enterprises and cities, yet operational complexity is rising fast. AI and machine learning are becoming essential to keep networks reliable, secure and efficient at scale. The industry must align on common data, interfaces and governance, so that intelligent Wi-Fi can work across real-world multi-vendor environments and deliver value for all who use it," Rodrigues said.

Eric McLaughlin said Intel sees the work as part of a broader transition in Wi-Fi operations and planning.

"Intel is proud to lead the team that delivered this comprehensive report. AI/ML is transforming the future of Wi-Fi, and it has become a strategic imperative. We are excited to collaborate with our WBA partners and the broader ecosystem to accelerate its advancement to enable self-organizing, proactive, and more reliable networks with improved QoE across the industry," he said.

Metin Taskin said service providers face pressure to manage user experience at scale while limiting operating costs and site visits.

"The effective use of AI/ML in Wi-Fi environments will help ISPs proactively improve performance quality, innovate faster, and most critically, combat churn. Airties is proud to co-lead this WBA initiative and to share our insights and AI-driven software expertise as part of our commitment to empower operators to deliver smooth, smart, secure connectivity," he said.

Matthew MacPherson said enterprise Wi-Fi is increasingly used for business-critical applications, making traditional management approaches harder to sustain.

"As Wi-Fi becomes the primary connectivity technology for mission-critical enterprise applications, the complexity of managing these environments has outpaced traditional manual methods. This report provides a vital framework for the industry to transition from reactive troubleshooting to a proactive, self-optimizing architecture. By leveraging AI and machine learning through interoperable standards, we are enabling organizations to reduce operational overhead and deliver a more resilient, high-quality experience for every user and device," he said.