For such a rapid innovation cycle, AI has been given unprecedented levels of responsibility. According to the Stanford AI Index 2026, 88% of organizations used AI in 2025, with 70% using generative AI in at least one business function. Most analysts agree that this technology, particularly when it comes to generative and agentic AI, is yet to reach full maturity, yet it’s already making itself indispensable to most enterprises. Employees expect LLM-based copilots to respond as readily as any other business application, customers are increasingly exposed to AI as part of their user experience, and emerging agentic models need to communicate continuously with applications and infrastructure as they crunch data and carry out tasks. Any hint of delay within those interactions has the potential to disrupt productivity, sow mistrust in the technology, and limit any return on investment (ROI).

Most businesses now inhabit a multi-cloud environment that spans countries and continents. When an AI request depends on information stored in one cloud environment, processing capacity hosted in another, and an application delivered somewhere else entirely, latency becomes the deciding factor. Each network hop, particularly through public Internet pathways, increases response time. Repeat these delays across hundreds, thousands, or even millions of individual requests – from both humans and AI agents – and the whole organization becomes artificially hampered. Sometimes connectivity becomes so hampered that IT teams must step in and spend valuable time rectifying it.

A recent Censuswide survey commissioned by DE-CIX found that IT teams in the US and UK spend an average of 11.5 hours every week resolving cloud connectivity issues – that’s more than a full working day each week spent on network troubleshooting. This is the “hidden productivity tax” that organizations are now paying, and it may be why the ROI for AI initiatives feels lukewarm at best, and impossible to measure at worst. McKinsey’s 2025 global AI survey found that only 39% of respondents could attribute any enterprise-level EBIT impact to AI, and most of those reported a contribution below 5%. That’s why even the best models can only get you so far – compute power may command the budget, but it’s the network that determines value.