Is your organization in the right place to take advantage of AI? How well positioned are you to reap its ability to deliver greater productivity, improved efficiency and better business outcomes?

If you thought that you were fairly well set, here’s a sobering statistic for you: A report from MIT, published late last year, reveals that around 95% of AI initiatives don’t deliver any measurable returns, with most getting cancelled before they can get out of pilot stage and into production.

The truth is that enterprise AI sits at a difficult crossroads. It has moved decisively beyond experimentation, and is starting to pop up in isolated use cases across the enterprise. But it has yet to break through to the core, and deliver truly autonomous decision making, end-to-end automation and real time intelligence in a mission critical setting. Adoption is widespread, but true enterprise-wide scale is rare. Proof of concept is a million miles from sustained, production-grade deployment.

The network is everything

If you are not as ready to take advantage of AI as you first thought, then that probably isn’t down to a lack of ambition or failure to invest in the right AI tools. It’s far more likely to be about your ongoing reliance on network architectures that were not designed for high-bandwidth, low-latency, policy-driven AI workloads.

Let’s stop to consider a handful of the connectivity challenges that must be resolved before AI can thrive at scale:

• The network must be able to cope with AI workloads that are distributed across multiple clouds, geographies, and edge locations
• Your network must be ready for the kind of unpredictable performance and uncertain costs that will be the inevitable result of greater autonomy
• A network won’t be able to manage AI workloads well if it only gives you fragmented visibility and throws up security blind spots
• An older network will make trouble for you when it comes to scaling AI securely and cost effectively, let alone with the right kind of governance and auditability to keep regulators happy

All of this means that the priority for CIOs must be to re-architect their digital foundation for AI, rather than layering AI on top of legacy infrastructure. AI systems introduce continuous, machine-driven workloads that put new demands on connectivity, latency, resilience and data movement. But you can’t reverse-engineer AI readiness into systems that were designed for a different world. AI is already piling unprecedented pressure on to digital foundations that were not designed for it, and that isn’t going to get better on its own.

Don’t let your network let you down

It is vital not to allow your network, designed as it was for elastic, human-paced applications, to become the limiting factor in your AI transformation. You need a better solution that eliminates infrastructure silos across network, cloud, edge and security end points. You need a unified, AI-optimised digital backbone that enables AI workloads to move seamlessly.

Before they can call themselves truly ‘AI ready’, CIOs need a connectivity solution that removes infrastructure complexity and lets them focus on AI-led innovation, faster time to value and measurable business outcomes. There can be no successful AI transformation without it. Only a foundation like this can determine whether AI delivers ROI or a world of trouble, risk and disappointment.

NetEvents will be hosting an event titled “Unleashing the Power of AI within the Enterprise” on Friday, April 24th, 2026. It will allow CIOs to engage in an expert-led discussion including London School of Economics, industry analysts Omdia plus technologists, on all the above issues.