Enterprises are hugely ambitious when it comes to new investments in AI. Yet there is often a gap between what organisations expect AI to achieve and what their technology stack can actually deliver.
Nearly one-third of IT decision-makers surveyed in Foundry’s 2025 AI Priorities Study flagged high costs associated with implementing AI in their existing tech stack as a major challenge. More than one-quarter reported difficulty scaling AI solutions across their organisation.
Insufficient infrastructure appears to be a common issue. A survey last year by data centre specialist Flexential also found that 43% of companies using AI were experiencing bandwidth shortages, while 34% had problems scaling data centre space and power.
Increased bandwidth is not enough to make infrastructure “AI ready.” A rebrand of existing digital infrastructure can’t support the next phase of AI. Flexibility, intelligence and control are every bit as critical for enterprises leveraging AI tools at scale. Luis S. Martínez Arnal, Principal Consultant at Colt believes organisations need a network infrastructure that ensures high levels of performance, but also low latency, security, resilience and tools for governance.
“There is pressure on the infrastructure at every level” says Martínez Arnal, referring not just to the servers behind AI operations, but the network and telecoms infrastructure that links them to the enterprise.
He also notes that enterprises are also being held back by concerns around compliance, data sovereignty and security. “They’re trying to understand how they can control their AI processes and make sure that they don’t breach any of the regulatory rules in the different geographies where organisations operate.”
Indeed, the rapid democratisation of AI applications – and the willingness of employees to use them – only increases the impetus to mitigate such risks. Corporate and customer information can be unwittingly shared on public AI services, leaving the business open to risk exposure that can’t be patched just by increasing bandwidth.
Reducing AI friction
Here, providers such as Colt have an advantage. Being a Tier-1 provider , they not only offer wider pipelines between the corporate network and the data centres where their AI workloads are typically being hosted but also provide local reach for businesses investing in AI processing at the edge. By reducing latency, they can ensure time-sensitive AI capabilities run smoothly and reliably where employees or customers need them most.
This also assists businesses with their data sovereignty issues, as data is processed locally. “They are not only gaining proximity,” says Martínez Arnal, “but also gaining security and assurance with regards to the data sovereignty and geographical location of their important assets.”
With AI services and capabilities evolving rapidly – along with regulatory frameworks – there’s a need to remain flexible and handle sudden change. Here, software-defined networks, harnessing the power of AI, can power a new era of widespread inferencing, where the enterprise relies on AI models running continuously across distributed environments. This requires more than rebranded legacy networks; it demands infrastructure that is intelligent by design and more efficient in the way it uses energy and resources.
Colt’s focus on software-defined networking, network as a service and deep enterprise partnerships positions it as a key enabler for AI success, not just today, but as the landscape evolves. To discover the solutions helping IT leaders ease the pressure of transformation and ready their tech stack for the next phase of AI, visit colt.net/solutions