The Hidden Constraint on AI Value
The current enthusiasm for AI often focuses on compute power and model sophistication, but a less-discussed factor may be limiting its impact: the network infrastructure that supports these applications. As generative AI becomes more integrated into business processes, any latency or unreliability in data transmission can significantly degrade user experience and limit ROI.
The Growing Productivity Tax
According to recent surveys, IT teams spend an average of 11.5 hours each week resolving cloud connectivity issues—more than a full working day lost to troubleshooting. This “hidden productivity tax” stems from the increasingly distributed nature of AI workloads, which often span multiple cloud environments and geographic locations.
When an employee requests data or analysis from an AI assistant, that request may need to traverse several network hops before reaching its destination. Each delay adds up, particularly when considering the millions of interactions occurring across an enterprise each day. This explains why McKinsey’s 2025 global AI survey found that only 39% of respondents could attribute any measurable EBIT impact from AI initiatives.
Beyond Compute Power
The reality is that even the most advanced models can only perform as well as their underlying network allows. While compute capacity may command a large portion of AI budgets, it’s network performance that ultimately determines user satisfaction and business value.
Consider these scenarios:
- A sales team using an LLM-powered chatbot experiences inconsistent response times based on the customer’s location
- Data scientists struggle to train models due to intermittent connectivity issues between cloud environments
- Business analysts waste time resolving network errors instead of deriving insights from AI tools
These operational challenges represent a significant drag on AI investments, preventing organizations from realizing their full potential.
Addressing the Imbalance
IT leaders need to shift focus beyond compute and prioritize network optimization for distributed AI workloads. This includes:
- Implementing private network connections between cloud environments
- Optimizing routing protocols to minimize latency
- Enhancing network monitoring and automation capabilities
- Investing in edge computing solutions for localized data processing
By treating the network as a strategic asset rather than an afterthought, organizations can unlock greater value from their AI investments and ensure that these transformative technologies deliver on their promise.