GPT‑5 OpenAI is already changing the way businesses operate. Currently, more than 600,000 companies pay business users of ChatGPT Enterprise, and more than 92% of Fortune 500 firms use OpenAI products or APIs to at least some extent.
New generation Artificial Intelligence Tools quickly moves into production, supporting customer interactions, employee workflows, and internal decision making across departments.
President, CEO and founder of Alkira.
The connection between business and OpenAI tools is rapidly strengthening. In 2025, the number of daily API calls exceeded 2.2 billion. On average, companies now use more than five internal applications or workflows based on GPT models.
This growth is good for innovation, but it also puts new strain on the systems that keep everything running. And the biggest stress isn't computing or storing. This is a network.
Doubts about GPT-5
Some in the tech world have doubts about GPT-5, but that hasn't stopped large companies from quickly adopting it.
Developers and regular users report both real achievements and stubborn limitations, and this mixture of praise and criticism makes it clear that if you move from small tests to full production, you need IT infrastructure which can grow and bear load.
In particular, CIOs are moving very quickly to implement GPT-5 and integrate it into the business. But many do this without a clear understanding of how these systems move data. This kind of artificial intelligence thrives on real-time processing and seamless access to cloud models.
It constantly transmits video, audio, text prompts and business data back and forth. This is not the type of traffic that most enterprise networks are designed to handle.
Legacy networks weren't built for AI traffic
Many organizations still rely on networks developed many years ago: MPLS circuits, centralized networks. business VPNperhaps a consolidated SD-WAN solution. These settings are suitable for email and SaaS applications. But GPT-5 is a different matter. It generates an unpredictable high volume of traffic across cloud regions and business units.
The model can retrieve data from CRM platform in one region, process it using a cloud inference system somewhere else, and send the results to a user interface on the other side of the world.
If your network is not flexible and responsive, it will slow everything down. Latency kills the experience. Poor routing disrupts workflows. Limited visibility turns performance issues into guessing games. And when this happens, the AI ​​is blamed, when the real problem is the path the data needs to take.
Evolving network architecture for AI workloads
The problem is mainly architectural. Traditional networks, built on a device-by-device and channel-by-channel basis, often face scalability issues when supporting high-demand AI workloads.
Expanding to new sites or regions often requires careful project planning, and deploying new applications requires coordination among networking, security, and cloud teams—processes that can slow down the IT responsiveness needed to quickly implement AI.
Many organizations are exploring evolving network architectures that emphasize scalability, global reach, and on-demand resource provisioning to address these challenges.
New models aim to support dynamic provisioning of services rather than fixed connections, and reduce dependence on hardware-centric environments. This shift can allow IT teams to more flexibly and quickly provision network resources as business needs evolve.
The industry's adoption of cloud-based networking designs has demonstrated potential benefits, including simplified deployment of artificial intelligence tools, improved traffic routing based on application requirements, and improved workload segmentation to balance performance and security.
These approaches often aim to minimize manual reconfiguration efforts and better support rapid innovation cycles. In short, they provide a more adaptive foundation for the workloads of the modern enterprise.
Security must scale with AI
Safety must also keep up. GPT-5 interacts with sensitive data, often extracting it from live internal systems such as financial statements, product documentation, or customer histories. If a network cannot provide identity-based access, audit trails, and segmentation policies at scale, it poses a real threat.
You want a network where politics is seen as part of the project, not as an afterthought added on later. Ultimately, these controls are necessary to maintain business confidence and compliance.
The benefit is greater than smoother AI performance. When the network matches the pace of the business, innovation happens faster. Developers can launch new features without waiting for infrastructure development.
Business leaders can test ideas in production environments without weeks of prep work. Risk management teams gain better visibility and control. And CIOs stop being blockers and start being enablers.
The web is now an artificial intelligence tool
Most enterprises were not ready for GPT-4, and GPT-5 is already ahead of their infrastructure. The gap is widening, but it is not too late to get ahead of it.
The network is now the leading edge of your AI strategy, and if it doesn't evolve with the workloads it supports, it will hold you back.
GPT-5 is here. The question is whether your network is ready to keep up with the times.
Check out the best network monitoring tools.
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