In the modern enterprise, the promise of Artificial Intelligence (AI) at the edge—driving everything from autonomous robotics to real-time quality control—is immense. Yet, the path to realizing this promise is often fraught with unseen challenges, not within the AI models themselves, but within the subterranean layers of the enterprise wireless stack. The conventional wisdom that wireless is merely about coverage or throughput is a dangerous anachronism. Today, wireless is the central nervous system connecting diverse endpoints to instantaneous decisions. When this foundational layer falters, AI initiatives don’t collapse spectacularly; they simply atrophy, undermined by insidious latency, unpredictable behavior, and hidden safety risks.

Devices & Endpoints: The Genesis of AI Signals

The journey of intelligence at the edge begins with the devices and endpoints themselves. These are the physical manifestations of the digital world, constantly generating the raw data that feeds AI models and drives automated processes. Without capable and well-integrated devices, even the most robust wireless infrastructure remains inert.

What Lives Here

The diversity of endpoints in the modern enterprise is vast, encompassing a spectrum of form factors and functionalities:

Traditional Devices: Laptops, mobile phones, and tablets remain crucial for human-centric operations, providing connectivity for frontline workers and management interfaces.
IoT Sensors & Cameras: These are the eyes and ears of the AI system, collecting environmental data, operational parameters, and visual input. From temperature and pressure sensors to high-resolution AI-powered cameras, they form the bedrock of data acquisition.
Robots, AGVs, AMRs: Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) are rapidly transforming logistics and manufacturing. Their precise movements and collaborative actions are entirely dependent on continuous, low-latency wireless communication.
AR/VR Headsets & Wearables: For immersive training, remote assistance, and augmented reality overlays in complex environments, these devices demand extremely low latency and high bandwidth to deliver a seamless user experience.

Typical Tools & Platforms for Device Integration

Integrating and managing such a diverse array of devices requires specialized tools and platforms:

Industrial IoT Devices: Major industrial players like Siemens, Bosch, and Rockwell provide integrated ecosystems of sensors, controllers, and edge devices designed for rugged industrial environments. These devices often come with their own communication protocols and integration APIs.
Smart Cameras: Platforms built around NVIDIA Jetson-based systems or Intel OpenVINO devices empower smart cameras with on-device AI capabilities for real-time object detection, anomaly recognition, and quality inspection.
Robotics Platforms: Industry leaders such as ABB, FANUC, KUKA, and OTTO Motors offer comprehensive robotics platforms, where connectivity to the enterprise wireless stack is a critical enabler for coordinated movements, real-time analytics, and safety protocols.

The sheer volume and variety of data generated by these devices necessitate a wireless access layer that is not only ubiquitous but also intelligently managed to cater to the distinct QoS requirements of each endpoint.

Wireless Access Layer: The Digital Conduit

The Wireless Access Layer (Layer 1) is the immediate gateway for device-generated signals to enter the digital domain. This is where the physical connection is established, and the foundational protocols for wireless communication are put into play. It’s the first critical link determining the speed, reliability, and security of data transmission from the edge.

Wi-Fi 7: Paving the Way for Extreme Throughput

The evolution of Wi-Fi standards, particularly with the advent of Wi-Fi 7 (IEEE 802.11be), marks a significant leap in unbound connectivity. Wi-Fi 7, also known as Extremely High Throughput (EHT), builds upon the advancements of Wi-Fi 6 and Wi-Fi 6E to deliver unprecedented speeds and efficiency.

Key Wi-Fi 7 technologies relevant to the enterprise wireless stack include:

4096-QAM (Quadrature Amplitude Modulation): Increases data density, allowing more bits per symbol and thereby boosting data rates by 20% compared to Wi-Fi 6’s 1024-QAM. This directly translates to faster data transfer for bandwidth-intensive edge applications.
320 MHz Channel Width: Doubles the channel width compared to previous generations, significantly increasing potential throughput. While spectrum availability can be a constraint, this wider channel offers immense capacity for data-heavy AI workloads.
Multi-Link Operation (MLO): A revolutionary feature that allows devices to use multiple radio bands (2.4 GHz, 5 GHz, and 6 GHz) simultaneously for transmission and reception. MLO enhances speed, reduces latency, and improves reliability by distributing traffic across links or using redundant paths.
Multiple Resource Unit (MRU): Improves the efficiency of OFDMA (Orthogonal Frequency-Division Multiple Access) by allowing a single user to utilize multiple resource units within a transmission, minimizing wasted spectrum and increasing throughput.
Preamble Puncturing: Enables Wi-Fi 7 devices to selectively ignore portions of a channel that are experiencing interference, preserving the rest of the wide channel for data transmission. This is crucial for maintaining performance in congested 6 GHz environments.
Restricted Target Wake Time (R-TWT): An enhancement to TWT from Wi-Fi 6, R-TWT improves latency predictability and reduces jitter for latency-sensitive traffic by allowing access points to use improved channel access and resource reservations.

Leading Wi-Fi 7 infrastructure providers include Cisco Catalyst/Meraki, Aruba Networks, Juniper Mist, and Extreme Networks, each offering robust solutions for high-density and performance-critical deployments.

Private 5G: Dedicated Connectivity for Mission-Critical AI

While Wi-Fi 7 excels in many high-bandwidth, low-latency scenarios, certain mission-critical AI applications at the edge demand even higher levels of reliability, security, and deterministic performance. This is where Private 5G (Fifth Generation) networks become indispensable. Private 5G offers a dedicated, localized wireless network, often deployed on-site, providing unparalleled control and performance.

Advantages of Private 5G for industrial AI and automation include:

Enhanced Reliability and Uptime: Private 5G is engineered for high uptime and deterministic responsiveness, far exceeding the typical “three-nines uptime” (99.9%) of Wi-Fi, which translates to hours of annual downtime. For AI-driven systems where even momentary disruptions can be costly, this level of reliability is paramount.
Ultra-Low Latency and Jitter: Private 5G delivers consistently lower latency and reduced jitter compared to public cellular or even advanced Wi-Fi. This is critical for real-time AI inferencing at the edge, where decisions must be made in milliseconds for applications like autonomous robotics or remote control.
Massive Device Connectivity: Designed to concurrently support a vast number of devices across expansive facilities, Private 5G can scale to meet the demands of increasingly device-dense industrial environments, such as smart factories with thousands of sensors and robots.
On-Site Data Control and Security: By keeping operational data entirely within the enterprise’s private network, Private 5G significantly enhances security, privacy, and compliance, mitigating risks associated with data traversing public networks.
Seamless Mobility and Handoffs: For applications involving moving assets like AGVs or autonomous vehicles, Private 5G offers seamless, make-before-break handoffs between access points, ensuring uninterrupted connectivity and preventing costly stalls.

Prominent providers in the Private 5G space include Nokia Digital Automation Cloud, Ericsson Private 5G, Samsung Private Networks, Athonet, Celona, and Airspan. These solutions leverage 5G’s core capabilities, including network slicing and ultra-reliable low-latency communication (URLLC), to tailor networks for specific industrial use cases. For instance, Cisco’s Ultra-Reliable Wireless Backhaul (URWB) technology, built upon 802.11, provides similar benefits for mission-critical applications by ensuring ultra-low latency and seamless handoffs for moving assets, often complementing Wi-Fi deployments.

Spectrum & RF Intelligence: The Invisible Architect

Above the foundational access layer, the Spectrum & RF Intelligence (Layer 2) acts as the invisible architect of the wireless environment. This layer is responsible for intelligently managing the radio frequency spectrum to optimize performance, minimize interference, and adapt to dynamic conditions. Its effective operation is crucial for guaranteeing the predictable and reliable delivery of data to and from edge AI systems.

Core Functions

This layer orchestrates several critical functions:

Channel Selection: Dynamically choosing the least congested and most performant frequency channels to ensure optimal data flow. This is especially vital in dense industrial environments where multiple wireless systems might coexist.
Power Control: Adjusting the transmit power of access points and devices to achieve the right balance between coverage and interference. Too much power can cause co-channel interference, while too little leads to coverage gaps.
Interference Mitigation: Actively identifying and neutralizing sources of interference, whether from neighboring wireless networks, industrial machinery, or other electromagnetic sources. Techniques like preamble puncturing in Wi-Fi 7 aim to make this process more efficient.
Multi-Link Operation (Wi-Fi 7): As discussed, MLO strategically utilizes multiple frequency bands simultaneously, allowing for load balancing and increased resilience against specific band interference.
Real-time Adaptation: Continuously monitoring the RF environment and adjusting parameters to maintain optimal performance in dynamic conditions.

Essential Tools

Sophisticated tools are employed at this layer to provide RF intelligence:

Juniper Mist AI (RF Optimization): Leverages AI to provide proactive RF management, anomaly detection, and self-healing capabilities, ensuring optimal wireless performance.
Aruba AirMatch: An AI-powered solution for automated RF optimization, intelligently adjusting channel and power settings across the wireless network.
Cisco RRM (Radio Resource Management): Cisco’s intelligent RRM capabilities automatically adjust transmit power and channel assignments to optimize coverage and capacity.
Celona MicroSlicing: For Private 5G deployments, micro-slicing allows for fine-grained allocation of wireless resources to specific applications, enabling dedicated performance levels for critical AI workloads.
Nokia RAN Intelligent Controller (RIC): In Open RAN architectures, the RIC plays a crucial role in enabling real-time control and optimization of radio resources, facilitating advanced RF intelligence and service delivery.

The intelligence embedded in this layer ensures that the wireless signals, which are the lifeblood of edge AI, navigate the complex RF landscape efficiently and reliably, minimizing the “silent failures” caused by unpredictable wireless behavior.

Edge Network & Transport: Deterministic Paths to Intelligence

Building upon the robust RF intelligence, the Edge Network & Transport (Layer 3 & 4) establishes the deterministic paths that data travels between devices, edge compute, and the broader enterprise network. This layer is paramount for ensuring that AI-driven insights reach their destination with the necessary speed and integrity, particularly where latency and reliability are non-negotiable.

Moving Traffic with Deterministic Paths

The Edge Network and Transport layer ensures that the flow of data is predictable and consistently meets the QoS requirements of critical applications. This demands more than just basic routing; it requires intelligent traffic management and robust network infrastructure designed for the demands of industrial AI.

Tools for Edge Network & Transport include:

Network Infrastructure:
- Cisco Nexus / Catalyst Switching: High-performance switching solutions providing the backbone for wired connectivity at the edge, integrating seamlessly with wireless access.
- Arista EOS: Cloud-grade routing and switching platforms known for their open architecture and automation capabilities, crucial for dynamic edge environments.
- Juniper Fabric: Comprehensive networking solutions that enable automated, secure, and scalable network fabrics, extending from the data center to the enterprise edge.
SD-WAN (Software-Defined Wide Area Network):
- Cisco Viptela, VMware SD-WAN, Fortinet: SD-WAN solutions intelligently route traffic over various transport links (e.g., broadband, MPLS, 5G), prioritizing critical AI data, optimizing performance, and ensuring resilience even across geographically dispersed edge locations. They contribute to consistent network behavior, reducing unpredictable latency.
5G UPF at Edge (User Plane Function):
- Nokia, Ericsson, Open5GS: In Private 5G deployments, moving the 5G User Plane Function (UPF) to the edge significantly reduces latency by keeping data traffic localized at the enterprise site. Instead of routing all data back to a centralized core, the edge UPF handles local traffic efficiently, which is critical for real-time industrial applications. This local breakout capability is a cornerstone of ultra-low latency applications that power edge AI.

Cisco Ultra-Reliable Wireless Backhaul (URWB) for Extreme Edge Cases

For extremely challenging industrial environments and fast-moving assets, where traditional Wi-Fi or even basic 5G deployments might struggle with “make-before-break” handoffs and ultra-low latency, solutions like Cisco’s Ultra-Reliable Wireless Backhaul (URWB) offer significant benefits.

Ultra-Low Latency: URWB is specifically designed to minimize latency for mission-critical applications.
“Make-Before-Break” Handovers: This crucial feature ensures that moving vehicles (e.g., AGVs, trains) establish a reliable connection with the next access point before losing connectivity with the current one, eliminating packet loss and service interruptions during roaming.
Multipath Operations (MPO): URWB utilizes MPO to send high-priority packets via redundant paths over uncorrelated frequencies to multiple access points simultaneously. This advanced technique can duplicate protected traffic up to eight times, exploiting time, spatial, and frequency diversity to further reduce latency and improve reliability, effectively addressing interference and hardware failures.
Ideal Complement to Wi-Fi: URWB extends network infrastructure to fixed or moving assets where wired options are impractical or too expensive, making it an ideal companion to Wi-Fi for supporting a broader range of latency-sensitive applications in unlicensed spectrum.

The robustness of this layer directly impacts the responsiveness of AI systems. If data is bogged down by inconsistent routing or network bottlenecks, the millisecond-level decisions promised by AI will remain elusive.

Edge Compute & AI Runtime: Where Intelligence Runs

With data reliably delivered by the transport layer, the Edge Compute & AI Runtime (Layer 5 & 6) becomes the crucial stage where raw information transforms into actionable intelligence. This is the localized processing hub, bringing computational power close to the data source to minimize latency and enable real-time decision-making for AI algorithms.

Where Intelligence Runs

This layer comprises the hardware platforms and software environments necessary to host and execute AI models at the enterprise edge.

Compute Platforms for Edge AI:

NVIDIA Jetson / EGX: NVIDIA’s Jetson and EGX platforms are specifically designed for AI at the edge, offering powerful GPUs for inference and machine learning workloads. They are widely used in smart cameras, robotics, and autonomous systems where real-time visual processing is critical.
Intel Edge Insights: Intel provides a suite of hardware and software solutions optimized for edge computing, including processors like Intel Atom and Xeon D, alongside toolkits that accelerate AI deployment.
AMD EPYC Edge: AMD’s EPYC processors offer high core counts and strong performance suitable for more demanding edge server roles, capable of running multiple AI inference tasks concurrently.

Edge Platforms (Software Stacks):

Red Hat OpenShift Edge: Provides a consistent Kubernetes platform that extends from the data center to the edge, enabling containerized application deployment and management for AI workloads.
Edge / VMware Edge: VMware offers solutions for virtualizing and managing edge environments, providing a consistent operational model for running applications and AI instances on diverse hardware.
Compute Stack: Generic term for the underlying operating systems, virtualization layers (e.g., KVM, Docker), and runtime environments that enable AI applications to function.

Cloud-Extended Edge Platforms:

AWS Outposts: Brings AWS services, infrastructure, and operating models to virtually any on-premises facility, including edge locations, enhancing local processing while maintaining seamless integration with the cloud.
Azure Stack Edge: A portfolio of devices that bring Azure’s compute, storage, and networking capabilities to the edge, enabling rapid deployment of AI, machine learning, and containerized workloads.

The strategic placement and capabilities of this layer are vital. By processing data locally, edge compute reduces the reliance on constant cloud connectivity, enhances data sovereignty, and—most importantly—slashes the round-trip time for AI inference, allowing for sub-millisecond reactions in critical industrial processes.

AI Control Plane: The Brain of the Network

The AI Control Plane (Layer 7) functions as the “brain” of the entire enterprise wireless stack, leveraging AI and machine learning to proactively manage, optimize, and secure the network. It’s not just reactive; it anticipates issues, optimizes performance, and ensures the network precisely aligns with business intent.

Capabilities

This layer employs advanced AI capabilities to oversee the complex interplay of devices, spectrum, network transport, and edge compute:

QoE (Quality of Experience) Scoring: Continuously assesses the end-user or application experience, moving beyond basic network metrics to understand the actual performance perceived at the edge. This provides a holistic view of wireless health.
Predictive Assurance: Uses AI to analyze historical data and current network conditions to predict potential issues before they impact operations. For instance, it can foresee potential interference patterns or capacity bottlenecks.
Anomaly Detection: Identifies unusual network behavior that might indicate security breaches, hardware malfunctions, or application performance degradation, often much faster than human operators could.
Intent-Based Optimization: Translates high-level business objectives (e.g., “ensure ultra-low latency for robot navigation”) into specific network configurations and resource allocations, dynamically optimizing the wireless stack to meet these goals.

Tools for the AI Control Plane

Leading vendors are integrating AI deeply into their network management solutions:

Juniper Mist AI: A pioneer in AI-driven wireless, Mist AI provides proactive insights, automated troubleshooting, and self-optimizing network capabilities.
Cisco AI Network Analytics: Part of Cisco’s broader intent-based networking strategy, this solution uses AI to analyze network data, predict issues, and provide actionable recommendations.
Aruba Central AI: Offers AI-powered insights, anomaly detection, and optimization capabilities for Aruba’s networking portfolio.
Nokia AVA: Nokia’s AI-powered operations platform for telecom networks, providing a range of AI services for network optimization and automation.
Ericsson Cognitive Network Solutions: Ericsson’s suite of AI and automation solutions designed for optimizing 5G networks, ensuring efficiency and performance.

The AI Control Plane is critical for moving beyond manual, reactive network management to a predictive, proactive, and self-optimizing system. This automation is essential for maintaining the stability and predictability that AI at the edge demands, preventing silent failures from undermining critical operations.

Automation & Orchestration: Turning Intent into Action

The Automation & Orchestration layer (Layer 8) bridges the intelligence of the AI Control Plane with the physical and virtual infrastructure, turning abstract intent into concrete actions. This layer is responsible for automating complex network and compute tasks, ensuring that the wireless stack adapts dynamically to changing demands and optimizes resources efficiently.

Turns Intent into Action

This layer leverages automation tools and orchestration frameworks to streamline operations:

Cisco DNA Center: A centralized controller for Cisco’s enterprise networks, enabling intent-based networking, policy automation, and network assurance across wired and wireless infrastructure.
Aruba Central: A cloud-native platform for managing Aruba’s network infrastructure, offering unified operations, AI insights, and automation capabilities across access points, switches, and SD-WAN.
Juniper Apstra: Provides intent-based networking and automated operations for data center and campus networks, ensuring consistent deployment and management of network services.
Kubernetes (for edge workloads): The de facto standard for container orchestration, Kubernetes enables automated deployment, scaling, and management of containerized AI applications and edge services, ensuring high availability and efficient resource utilization.
Ansible / Terraform (infra automation): Infrastructure as Code (IaC) tools like Ansible and Terraform automate the provisioning and configuration of network devices, servers, and cloud resources, ensuring consistency, reducing manual errors, and accelerating deployment cycles.

This layer is essential for the agility and scalability required by modern AI deployments. It moves networking from a manual, configuration-intensive process to an automated, policy-driven paradigm, allowing the wireless stack to evolve and adapt at the speed of business.

Security & Trust Fabric: Adaptive, Wireless-Aware Security

In an increasingly interconnected world, where AI systems depend on vast amounts of data, the Security & Trust Fabric (Layer 9) is paramount. This layer provides adaptive, wireless-aware security that protects the entire enterprise wireless stack from devices to applications, ensuring the integrity, confidentiality, and availability of data and operations.

Adaptive, Wireless-Aware Security

This layer focuses on proactive and dynamic security measures tailored for the wireless domain:

Zero Trust:
- Zscaler, Palo Alto Prisma, Cisco Zero Trust: Implementations of Zero Trust principles ensure that no user or device, whether inside or outside the network perimeter, is inherently trusted. Every connection is authenticated, authorized, and continuously validated, minimizing the attack surface.
- NAC (Network Access Control): Cisco ISE, Aruba ClearPass: These solutions provide granular control over device access to the network, enforcing policies based on identity, device posture, and role, ensuring that only compliant devices can connect.
Wireless Threat Detection (AI-based WIDS/WIPS): AI-powered Wireless Intrusion Detection Systems (WIDS) and Wireless Intrusion Prevention Systems (WIPS) continuously monitor the wireless spectrum for rogue access points, unauthorized devices, and other Wi-Fi threats, automatically detecting and mitigating attacks.
SIM-based security (Private 5G): For Private 5G networks, security is deeply embedded at the SIM card level, providing strong authentication, encryption, and secure key management, offering a highly robust security foundation for mission-critical industrial applications.

The Security & Trust Fabric is not an afterthought; it’s an integrated component that ensures the continuous trust and protection of the data flowing through the wireless stack. Without a comprehensive and adaptive security posture, the risks associated with AI at the edge—from data breaches to system manipulation—become untenable.

Applications & Business Logic: What the Business Actually Uses

At the pinnacle of the Enterprise Wireless Stack, the Applications & Business Logic layer (Layer 9) represents the ultimate realization of all the underlying technological efforts. This is where the raw data, processed intelligence, and automated actions converge to deliver tangible business outcomes and value.

What the Business Actually Uses

This layer embodies the real-world applications and intelligent systems that leverage the robust wireless foundation:

Smart Manufacturing Systems: Automate and optimize production lines, track goods, manage inventory, and enforce quality control using real-time data from sensors and robots.
Digital Twins: Create virtual replicas of physical assets, processes, or entire factories, enabling real-time monitoring, simulation, predictive maintenance, and optimization. This requires continuous data synchronization between the physical and digital realms.
Real-time Quality Inspection: Utilize AI-powered cameras and sensors for instantaneous defect detection, ensuring product quality and minimizing waste.
Autonomous Logistics: Optimize supply chains, manage fleets of AGVs and AMRs, and automate material handling, relying on precise, low-latency communication.
AI Copilots & Dashboards: Provide human operators with AI-assisted insights, real-time performance metrics, and intuitive dashboards for enhanced decision-making and operational oversight.

Tools for Applications & Business Logic

Diverse tools and platforms are used to develop and deploy these enterprise applications:

Siemens Industrial Edge: Siemens’ platform for running edge applications, integrating operational technology (OT) with information technology (IT) at the factory floor.
PTC ThingWorx: An industrial IoT platform that enables the creation and deployment of connected applications, digital twins, and augmented reality experiences.
Azure IoT / AWS IoT: Comprehensive cloud-based IoT platforms that provide services for device management, data ingestion, analytics, and application development at scale.
Custom AI Pipelines (PyTorch, TensorRT): Companies often develop bespoke AI models and deploy them via custom pipelines using frameworks like PyTorch for training and NVIDIA TensorRT for high-performance inference at the edge.

The effectiveness of this layer is a direct reflection of the reliability, performance, and security provided by the entire wireless stack. If the foundation is weak, the applications built upon it will struggle to deliver consistent, predictable, and safe outcomes, ultimately hindering the enterprise’s AI ambitions.

Conclusion: Building a Future-Proof Digital Backbone for AI

The journey to superior business continuity in the era of industrial interconnectedness is inextricably linked to the strategic implementation and ongoing optimization of IoT platforms. As discussed, traditional approaches to disaster recovery are no longer sufficient to navigate the complexities and volatilities of modern operational environments. Instead, the focus has shifted towards proactive detection, real-time response, and integrated operational stability—capabilities that IoT platforms are uniquely positioned to deliver.

IoT platforms serve as the unifying force, consolidating disparate data streams from across assets, plants, and processes into a singular, real-time operational view. This aggregation is not merely about data collection; it’s about transforming a “data deluge” into actionable intelligence, enabling early identification of potential disruptions, fostering informed decision-making, and facilitating containment before minor glitches escalate into major crises.

The inherent advantages of standardization and repeatability embedded within unified IoT platforms are profound. By enforcing consistent protocols for connectivity, security, and workflows across the entire enterprise, these platforms mitigate individual dependencies, simplify the intricacies of incident response, and ensure that proven recovery strategies can be executed uniformly and reliably across geographically dispersed sites. This paradigm shift means that business continuity transcends a theoretical concept, becoming an ingrained aspect of daily operational rhythm.

In an environment increasingly characterized by uncertainty and rapid change, the ability to ensure uninterrupted operations emerges as a powerful competitive differentiator. Organizations that wisely invest in robust IoT platforms are not just fortifying themselves against potential disruptions; they are actively constructing agile, intelligent operations that remain stable, predictable, and controllable, even when faced with unforeseen circumstances. This strategic foresight and investment are precisely what delineate a truly resilient enterprise from one that remains vulnerable and fragile. The continuous evolution of IoT technologies, driven by advancements in AI, edge computing, digital twins, and robust cybersecurity, promises an even more capable future for business continuity, empowering organizations to not only weather any storm but to emerge stronger and more adaptable.

Is your organization ready to transform its operational resilience and secure a competitive edge through advanced IoT strategies?

Unlocking the full potential of IoT for robust business continuity requires specialized expertise and a tailored approach. At IoT Worlds, our team of seasoned consultants is dedicated to helping businesses like yours design, implement, and optimize IoT platforms that ensure continuous stability and operational excellence. From architectural design to protocol selection, security implementation, and strategic road mapping, we provide comprehensive guidance to future-proof your digital backbone.

Don’t wait for the next disruption to react. Proactively build an intelligent, resilient future for your enterprise.

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The Enterprise Wireless Stack: Unlocking AI at the Edge