Preparing Enterprise Networks for AI Success

Enterprise Management Associates (EMA™), an IT research and consulting firm, announced the release of its latest research report, "Readying Enterprise Networks for Artificial Intelligence," authored by Shamus McGillicuddy, Vice President of Research for Network Infrastructure and Operations at EMA.

As artificial intelligence (AI) adoption gains momentum, network readiness is emerging as a critical success factor. AI workloads generate unpredictable bursts of traffic, demanding high-speed connectivity that is low latency and lossless. AI adoption will require upgrades and optimizations in data center networks and wide-area networks (WANs). This is prompting enterprise IT teams to rethink, re-architect, and upgrade their data center and WANs to support AI-driven operations.

EMA's new report serves as a strategic guide for IT decision-makers who have started their AI journey and:

Examines how enterprises are preparing their networks for AI
Explores changes to data center networks and WANs
Investigates some of the security implications of these networks
Identifies changes made to network observability tools
Explores some of the basic aspects of overall AI strategy, such as who leads these projects and what kinds of technologies companies are adopting

"Networks will make or break enterprise investments in AI technology. IT organizations are well-aware of this fact. However, preparing networks for AI will be expensive and complex," McGillicuddy said. "This research shows that AI workloads will be distributed across public clouds, data centers, and the enterprise edge. This kind of architecture requires improvements to both data center and wide-area networks."

Some of the key findings from the report include:

The top business challenges to networking for AI are security risk (39%), budget issues (34%), and difficulties with keeping up the pace of AI innovation (33%)
42% of companies have established AI centers of excellence to lead strategy across technical teams and business units
Organizations that can automatically apply quality of service or routing policies specific to AI-related traffic report more success with preparing their networks for AI

Source: Enterprise Management Associates