AI for IT stalls as network complexity rises

Security remains the top concern, both as a barrier to deployment and a primary use case for AI itself. “You have to fight AI with AI from a network security perspective,” says Brandon Butler, senior research manager at IDC. “There’s a realization that nefarious actors are leveraging AI themselves.”

Integration with existing systems and a shortage of skilled talent follow close behind. “Most folks don’t feel their staff can fully evaluate and select the right solutions,” Leary says. As a result, many organizations are turning outward for help: 81% say they are increasing spending on managed service providers (MSP) to support AI initiatives.

IDC

Infrastructure demand is accelerating

Even as adoption slows, AI’s impact on infrastructure is here. “The pressure is already on the network,” Butler says. “The question now is whether organizations can keep up with what AI is demanding of their infrastructure.”

For instance, 89% of data centers expect to increase bandwidth by at least 11% within the next year, driven by AI workloads. That demand extends beyond individual facilities, with 91% expecting similar growth in inter-data center connectivity, highlighting the strain on distributed architectures.

Cloud environments are seeing sharper increases. Organizations anticipate an average 49% rise in bandwidth for cloud connectivity over the next year. “The cloud is almost always involved,” Leary says. “The biggest group mixes one cloud platform with one or more data centers.”

Edge deployments set the next wave

Beyond the data center and cloud, the network edge is emerging as the next major growth area. Today, 27% of organizations have deployed AI workloads at the edge, and 54% plan to do so within two years. “Folks who are leveraging AI more extensively are already pushing workloads to the edge,” Butler says. “We see this as a leading indicator of where the market is going.”