OpenAI’s GPT-5 is already changing the way businesses work. More than 600,000 companies now pay for ChatGPT Enterprise business users, and more than 92% of Fortune 500 companies use OpenAI products or APIs, at least to some extent.
A new generation of AI tools is rapidly entering production, supporting customer interactions, employee workflows, and internal decision-making across departments.
The connection between companies and OpenAI tools is getting closer. By 2025, the number of daily API calls will increase to over 2.2 billion. On average, companies now run more than five internal applications or workflows based on GPT models.
This kind of growth is good for innovation, but it also puts new pressure on the systems that keep everything running. And the biggest stress point isn’t processing power or storage. It’s the network.
Doubts about GPT-5
Some in the tech world are skeptical of GPT-5, but that hasn’t stopped large companies from quickly adopting it.
Developers and regular users have highlighted both the real benefits and persistent limitations, and this mix of praise and criticism makes it clear that you need an IT infrastructure that can grow and withstand the pressure as you move from small tests to full production.
Especially IT departments are introducing GPT-5 very quickly and integrating it into the company. But many do this without fully understanding how these systems transfer data. This AI thrives on real-time processing and continuous access to models in the cloud.
Continuously transmit professional video, audio, large voice commands and data in both directions. This is not the type of traffic that most enterprise networks are designed for.
Legacy networks were not designed for AI traffic
Many companies still rely on networks that were developed years ago: MPLS connections, centralized corporate VPNs, and perhaps an integrated SD-WAN solution. These configurations are suitable for messaging and SaaS applications. But GPT-5 is different. Generates large and unpredictable traffic between cloud regions and business units.
The model can take data from a CRM platform in one region, process it through a cloud-based inference engine elsewhere, and send the results to a user interface on the other side of the world.
If your network is not flexible and responsive, everything will slow down. Latency kills the experience. Incorrect routing disrupts workflow. Limited visibility turns performance issues into guessing games. And when this happens, the AI is to blame, while the real problem is the path the data had to take.
Scalable Network Architecture for AI Workloads
The challenge is largely architectural. Traditional networks built device-by-device and link-by-link often face scalability issues when supporting demanding AI workloads.
Expanding into new locations or regions often requires extensive project planning, while implementing new applications requires coordination between network, security and cloud teams; processes that can slow down the IT relevance needed for rapid AI adoption.
Many companies are exploring scalable network architectures that emphasize scalability, global reach, and on-demand delivery to meet these challenges.
The new models are intended to support dynamic service delivery instead of fixed connections and reduce reliance on hardware-centric environments. This change can enable IT teams to provision network resources more flexibly and faster as business needs evolve.
The industry’s adoption of cloud-inspired network designs has demonstrated potential benefits, including optimized deployment of artificial intelligence, better traffic routing based on application requirements, and better 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 adaptable foundation for modern enterprise workloads.
Security must be developed with artificial intelligence
Security must also keep pace. GPT-5 interacts with and often retrieves sensitive data from active internal systems, such as financial data, product documentation or customer history. If the network cannot enforce identity-based access, audit trails and segmentation policies at scale, a real trade-off arises.
What is needed is a network that sees politics as part of the project and not as an afterthought. Ultimately, these controls are essential to maintaining trust and compliance within the organization.
The gain outweighs the AI’s smoother performance. As the network adapts to the pace of business, innovation accelerates. Developers can introduce new features without waiting for infrastructure.
Business leaders can test their ideas in production environments without having to do weeks of preparatory work. Risk management teams benefit from greater visibility and control. And CIOs are no longer blockers, but become enablers.
The network is now an enabler of artificial intelligence.
Most organizations were not prepared for GPT-4, and GPT-5 is already well ahead of their infrastructure needs. The gap is widening, but it is not too late to close it.
Networking is now central to your AI strategy, and if it doesn’t scale 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.
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