When OpenAI publishes a report grounded in actual enterprise utilization, it’s price paying consideration. The information doesn’t simply predict the longer term; it paperwork how at the moment’s enterprise networks are already being reshaped.
In The State of Enterprise AI (2025), OpenAI analyzes utilization throughout multiple million enterprise clients. The findings present a transparent inflection level: enterprise AI utilization has grown 8x 12 months over 12 months, whereas using superior reasoning fashions has elevated greater than 300x. This indicators a basic shift from easy prompts to complicated, multi-step, workflow-driven AI.
AI is now not confined to pilots or innovation groups. It’s being embedded immediately into on a regular basis workflows, buyer interactions, and operational techniques. The report’s important perception is about how AI is converging round particular, high-impact use instances which might be reshaping community necessities and elevating the bar for what enterprise networks—and IT groups—are anticipated to ship. Let’s study this sample and what it reveals.
How enterprise AI use instances are reshaping the community
As enterprises undertake AI throughout departments and workflows, the rising use instances are basically remodeling community calls for, architectures, and the important enterprise function that networks play.
AI-powered buyer help turns the community into an expertise layer
AI-driven help is likely one of the fastest-scaling enterprise use instances. Organizations are deploying AI brokers throughout chat, e mail, and real-time voice to resolve a rising share of interactions finish to finish.
Voice-based AI introduces steady, latency-sensitive site visitors, whereas backend integrations with buyer relationship administration (CRM), billing, and order techniques generate persistent software programming interface (API)-driven flows. As AI utilization scales, these interactions transfer from edge instances to core buyer journeys.
The community turns into a part of the client expertise. Inconsistent WAN efficiency or unstable cloud paths can degrade buyer satisfaction and enhance strain on IT groups to diagnose points throughout voice, cloud inference, and backend techniques.
AI-assisted software program improvement drives explosive east–west site visitors
AI is now embedded throughout the software program lifecycle—producing code, refactoring purposes, testing, and debugging. This exercise is increasing effectively past conventional engineering groups, producing dense, steady east–west site visitors between builders, repositories, steady integration/steady deployment (CI/CD) pipelines, testing environments, and cloud inference providers. As reasoning-driven AI utilization grows, inner dependency chains change into deeper and extra tightly coupled.
Networks optimized primarily for north–south site visitors battle right here. AI-assisted improvement will increase inner site visitors quantity, cross-domain dependencies, and troubleshooting complexity—usually requiring IT groups to cause throughout community materials, cloud connectivity, and software pipelines concurrently.
AI-driven evaluation and analysis create bursty, cloud-heavy demand
Groups in finance, operations, and analysis and improvement (R&D) are utilizing AI to investigate datasets, synthesize analysis, and extract insights—compressing work that when took weeks into hours.
These workloads are bursty and cloud-heavy, triggering massive knowledge transfers and inference requests in brief home windows reasonably than predictable patterns.
Networks should soak up sudden spikes with out degradation. Congestion or throttling delays important enterprise choices and will increase the burden on groups already working at capability.
Agentic AI workflows make the community a part of the execution path
One of the vital vital shifts recognized in OpenAI’s report is the rise of agentic workflows—multi-step AI techniques that retrieve knowledge, apply logic, take motion throughout techniques, and confirm outcomes. These workflows span identification providers, APIs, software-as-a-service (SaaS) platforms, and cloud inference endpoints—making the community a part of the execution path.
Agentic workflows introduce steady cross-system dependencies, develop the safety assault floor by way of machine identities, and require IT groups to troubleshoot failures spanning identification, cloud, safety, and community domains. Any instability—latency spikes, dropped connections, or misrouted site visitors—can break the workflow chain.
AI-driven personalization places the community on the income path
Clever personalization engines form how enterprises interact clients—tailoring provides, suggestions, and experiences in actual time. The community is now not simply supporting revenue-generating purposes—it’s immediately a part of the income path.
Efficiency degradation interprets into missed alternatives, whereas safety gaps enhance enterprise threat. IT leaders at the moment are anticipated to ship pace and safety concurrently.
Worker AI assistants create always-on, in every single place demand
AI assistants have gotten the entrance door to institutional information—supporting onboarding, troubleshooting, and every day productiveness throughout campuses, branches, and distant places.
Sustained, always-on AI site visitors compounds present collaboration and software masses. Excessive-density wi-fi, dependable WAN connectivity, and constant safety enforcement are pushed more durable than ever—usually and not using a corresponding enhance in IT employees.
Embedded AI turns the community into an integration cloth
As AI is embedded immediately into digital merchandise—search, diagnostics, automation—the community turns into the mixing cloth, connecting customers, purposes, knowledge, and inference.
Site visitors patterns change into steady and unpredictable, making it more durable to keep up efficiency, implement segmentation, and maintain visibility throughout domains. The community should perform as a unified integration layer connecting AI elements throughout each area—customers, purposes, knowledge sources, and inference endpoints.
Enterprise networks—and IT groups—are struggling to scale AI
These use instances expose a rising hole. Many enterprise networks had been designed for human-driven interactions, predictable site visitors patterns, and handbook operations. AI-driven environments introduce steady machine-to-machine site visitors, real-time efficiency expectations, and deeply interconnected techniques.
This hole isn’t simply architectural—it’s operational. AI will increase operational complexity, expands the safety assault floor by way of new identities and integrations, and calls for expertise which might be more and more troublesome to rent and retain. AI works in pilots, however struggles at scale.
In lots of organizations, the know-how is shifting sooner than the working mannequin required to run AI reliably at scale.
Cisco helps shut the readiness hole
The structure behind the community issues greater than ever. That is the hole Cisco is filling with AI-Prepared Safe Community Structure—constructed to deal with the community as an execution platform for AI, connecting customers, purposes, knowledge, inference, and automation with the efficiency, safety, and visibility AI calls for.
By design, it delivers:
- Infrastructure constructed for real-time, high-concurrency AI workloads
- Safety enforced throughout the community cloth, not bolted on
- Deep telemetry and cross-domain intelligence (AgenticOps—autonomous operations at machine pace) that reduces operational complexity and limits the safety blast radius so smaller IT groups can function AI-scale environments reliably
The purpose isn’t extra complexity. It’s less complicated operations with larger functionality.
What IT leaders ought to do subsequent
OpenAI’s enterprise knowledge confirms AI is turning into foundational to enterprise operations. For IT leaders, this implies reassessing not simply purposes and knowledge, however the community and working mannequin that underpins them.
As AI embeds itself into workflows, merchandise, and operations, the community turns into inseparable from AI success. Organizations that modernize for real-time efficiency, embedded safety, and autonomous operations will scale AI with confidence. Those who don’t will battle to maneuver past experimentation.
Within the AI period, the enterprise community doesn’t simply help the enterprise—it allows it.
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