Much of the AI revolution is quietly at risk, new research shows, not because of model drift or rogue code, but due to something far more basic: unreliable IoT connectivity.
The future of AI is being sold to the world with near-religious fervor: AI will cure diseases, run our cities, optimize global logistics, and make business operations near-flawless. But there’s one critical flaw in this vision, and it’s not the algorithms. It’s the pipes.
The intelligence is only as good as the information it receives. And that data pipe is still leaking. For years, the pairing of AI and IoT has been marketed as a match made in digital heaven. Smarter sensors feed AI models that, in turn, make better decisions. Cleaner energy grids. Safer vehicles. Faster supply chains. It’s an elegant theory—until the connection drops.
In a global survey of over 1,200 senior IoT decision-makers conducted by connectivity firm Eseye, just two percent of organizations said their IoT deployments consistently achieved the near-100 percent connectivity that real-time AI needs (Eseye, 2025). That’s a staggering gap for a system increasingly reliant on seamless, streaming data to power everything from industrial automation to predictive healthcare.
“IoT is the unsung hero. Without real-time, high-quality data from connected devices, there’s nothing for AI to analyse and no insight to act on,” the report said, quoting Volvo’s IoT expert Julien Bertolini as sayiing, "The goal of IoT is to get quality data, and that’s the foundation to build an AI model.
But Eseye’s 2025 State of IoT Report reveals that one in three businesses now view poor IoT connectivity as a direct barrier to AI adoption. Most respondents had deployed devices across multiple countries via cellular networks, yet still couldn’t maintain stable, real-time connections.
The consequences go beyond corporate frustration. 36 percent of those surveyed said data unreliability had already led to poor business decisions or damaged reputations. Another third cited increased operational costs and reduced efficiency as direct outcomes of spotty connectivity.
In some industries, bad data means lost revenue. In others, it means something far worse. For instance, a medical device monitors oxygen levels in a critical-care patient. If that sensor fails to transmit a data point due to poor connectivity, the AI system analyzing it won’t raise an alarm. No alert reaches the medical team as the patient’s health deteriorates. A preventable outcome becomes irreversible.
What makes this situation particularly ironic is that executives recognize the importance of connectivity. 74 percent of those surveyed agreed that near-100 percent uptime is “crucial to the business case” for deploying IoT in the first place. And yet, most still rely on patchwork infrastructure: commodity hardware, inconsistent cellular access, and complex regulatory environments across borders. As IoT expands globally, these gaps multiply.
So far, the industry has been too focused on volume and deploying devices at scale, without solving the deeper problem of persistent, high-quality connectivity.
If the AI economy is to scale safely and effectively, organizations will need to rethink how they architect their IoT ecosystems, from hardware to cloud, from edge processing to telecom partnerships. That may mean fewer cheap devices and more investment in resilience. It could require reimagining how edge AI is deployed, minimizing the need for constant connectivity by keeping more intelligence local. But most of all, it demands that companies stop treating connectivity as an afterthought and start treating it as a strategic foundation