AI has rapidly transitioned from theory to practice in the UK, reshaping how employees work and contributing significantly to economic growth. However, unlocking AI’s full potential depends on robust network infrastructure as much as high-performing AI models.

AI workloads are highly data intensive, requiring seamless information flow between systems and users, frequently between multiple cloud environments. Despite this, more than half of global organisations are trying to run advanced AI on legacy networks which cause bottlenecks, increase expenses, and limit the effectiveness of AI overall.

In fact, a survey by enterprise connectivity company Expereo found that 94% of enterprises report networks limiting their ability to run AI projects in some aspect. Businesses need to identify network shortcomings and invest in the right upgrades to truly avail of the benefits of AI.

What makes a network AI-ready?

Underneath every AI application is the infrastructure that keeps data moving, often involving 870,000 miles (1.4 million km) of subsea internet cables. To meet the rising AI demands, companies that own and operate these cables must adapt and grow.

Let’s think about how AI is being leveraged in a business. A service agent asks a question and requires a near instant answer. A sensor streams images to be analysed and needs a decision back within milliseconds. A product team pushes a model update and wants it to land in the right place without disruption.

Each of these tasks relies on networks; some controlled by the organisation, and others outsourced. That is why the qualities of the network often determine whether AI delivers real value. So, designing networks that are AI-ready is crucial.

The attributes for an AI-ready network can be broken down into three areas. The first is bandwidth. AI uses large amounts of data and for it to be moved effectively, the network must have enough capacity to avoid queues.

We also need AI to be fast, giving quick, accurate responses. This requires low latency across the network. And the network needs to be resilient, able to withstand stress and recover quickly when issues occur. Across all three, the goal is the same: a steady, predictable experience your teams can trust.

Building AI‑ready infrastructure

Becoming AI‑ready doesn’t mean starting over. Businesses can build on what they already have. Start by focusing on high ROI-use cases, such as, employee assistants, or fraud detection. Trace the journey between the user or device to the application, and measure how long that round trip takes, how often it spikes and where it slows down.

Quick wins include direct cloud connections and treating cloud‑to‑cloud traffic as a normal, engineered route, rather than an exception.

For locations requiring real-time responses, pair edge computing with strong local wireless networks such as private 5G or Wi-Fi. This ensures sensitive processing stays close to the action—ideal for scenarios like automation or healthcare.

Some workloads require greater control over where data resides and who can access it. Sovereign AI options support this by running models on region-bound platforms and connecting over private links. This keeps processing and logs within defined locations while reducing variability from public internet routes.

Security, as always, must be a priority. AI increases the amount of sensitive data moving across systems, and regulatory frameworks such as GDPR require that it is securely handled. To ensure data security, businesses must encrypt data by default when it is moved and stored.

Organisations must also manage data access carefully. This can be achieved by only granting permissions to people and systems that need it and keeping records of who accesses the information.

In addition, maintaining end‑to‑end visibility allows teams to check the causes of slow AI responses, by being able to see where the issue sits—whether it is with the application, the data source or the network path in between.

What can businesses do now to get ahead?

Start with a simple test of AI readiness: Do your priority AI use cases deliver fast responses during peak times? If not, identify the source of delay—is it in the last mile, the network backbone, or between clouds?

Prioritise upgrades where they matter most, whether that’s fibre to key locations, direct connectivity into your primary clouds, or pairing strong local wireless with nearby computing. Choose partners offering scalable connectivity, sovereign options, and the flexibility to grow with your business.

In today’s competitive landscape, businesses must innovate at scale with cloud and AI. This is only possible with the right networks, allowing organisations to roll out AI, fully avail of its benefits and drive growth.