Aryaka: How The Enterprise Network Can Unlock AI Success

An employee asking an AI assistant to summarize a document may look like a small moment of productivity. But the request can set off a much larger chain of activity: data moving between devices, cloud services, internal systems, security controls, and sometimes other AI tools.

That is changing the role of the workplace network. It is no longer background infrastructure that only becomes visible when a video call fails. Its performance and security increasingly shape whether AI tools feel useful, whether hybrid employees can work without friction, and whether sensitive information stays under control.

Aryaka takes the view that enterprises need to treat this as a foundation issue, bringing connectivity and security closer together as AI becomes part of everyday work.

This is not only an IT issue. It affects productivity, security, service delivery, and the pace at which an organization can introduce new AI-enabled ways of working. The starting point is understanding what has changed.

How Is AI Changing What Enterprise Networks Need to Support?

For many years, enterprise networks were designed around a fairly simple picture of work. Employees were mainly in offices. Important applications ran in corporate data centers. Most traffic followed predictable routes between fixed locations.

That picture has changed. Employees now use cloud software throughout the day, whether they are in an office, at home, or on the move. Many also use generative AI tools to research, write, summarize, analyze, and automate parts of their work. In some organizations, AI systems are also connecting to internal data, external services, and other software tools to complete more complex tasks.

These uses create different demands on the network.

AI services can generate sudden bursts of data traffic and can be more sensitive to delay, unstable connections, and lost data packets than older business applications. Aryaka notes in its guide to Secure SD-WAN and Unified SASE that generative AI, retrieval-augmented generation, agentic workflows, and cloud-based GPU services may all put pressure on networks that were not built for these patterns.

For a non-technical example, consider an employee using an internal AI assistant to review a contract. The assistant may need to securely access documents, connect to an approved AI service, apply the organization's policies, and return an answer quickly enough to be useful. If any part of that chain is slow, unreliable, or poorly secured, the employee experiences the AI tool as a problem, even if the underlying issue is the network.

The rise of hybrid work adds to the challenge. The network must now support employees, contractors, branch offices, cloud services, and company data across many different locations. The goal is not merely to keep everyone connected. It is to make sure the connection is reliable, secure, and manageable wherever work happens.

Why Is MPLS No Longer Enough for AI and Cloud Workloads?

Multiprotocol Label Switching, usually shortened to MPLS, is a private network service that many businesses have used for years to connect offices and data centers. It remains useful in some situations, especially where organizations need established and controlled connections between a small number of fixed locations.

However, MPLS was built around a workplace in which applications were centralized and traffic was relatively predictable. Today, important business tools may run across several cloud environments. Employees may be working from home, in branch offices, or on the road. AI applications may need information to move quickly between users, cloud services, and company systems.

There is also a practical issue of speed and flexibility. MPLS connections can take time to install or change, which can be difficult for organizations opening new sites, expanding into new regions, or responding to changing business needs. Its routing is typically more fixed, rather than continuously choosing the best available path based on live network conditions.

Software-defined wide-area networking, or SD-WAN, was developed to give organizations more flexibility. It can use a mix of connections, including broadband, cellular networks, and MPLS, then choose the most suitable route for an application. But SD-WAN alone is primarily about connectivity. Security can still be handled by separate products, separate vendors, and separate management tools.

This does not mean every enterprise needs to remove MPLS immediately. It does mean IT leaders should ask whether their existing mix of network connections, security tools, and remote-access methods can support the way their people will work with AI in the coming years.

What Does an AI-Ready Enterprise Network and SASE Strategy Need?

An AI-ready enterprise network is not defined by one product. It is an approach that helps employees reach applications reliably while giving IT and security teams a consistent way to protect access and data.

Secure SD-WAN is often a useful first step. It combines flexible SD-WAN connectivity with security controls built into the service. Instead of treating the network route and the security check as entirely separate jobs, it helps organizations connect people and applications while applying the same core protections across locations.

Unified Secure Access Service Edge, or Unified SASE, goes further. It brings the network together with cloud-delivered security services, including secure web access, firewalls, zero-trust access controls, cloud application protection, and data-loss prevention. In simple terms, it aims to give a business one joined-up way to manage how people connect to applications and how sensitive information is protected.

For enterprise leaders, this matters because a slow or insecure connection can undermine the value of an otherwise promising AI investment. The network needs to help people work productively, while still giving the organization confidence that access and data are being managed responsibly.