Why AI Projects Fail

A new MIT study revealing that 95% of corporate generative AI pilots fail to scale has sparked widespread concern across global enterprises. Many assume the issue lies in AI models or data readiness. Experts point to a deeper structural problem: outdated network infrastructure. According to Eric Wong, President of APAC at Expereo, the so-called “AI-Network Gap” has emerged as one of the biggest barriers to successful enterprise-wide AI adoption. 

AI pilots transition from controlled environments to full-scale deployment, network weaknesses are exposed. These bottlenecks directly affect productivity, increase project delays, and prevent organizations from achieving expected ROI. “Legacy networks were not designed for the intensive. Real-world data demands of modern AI workloads. These issues often lead to canceled projects and sunk investments. 

Key findings and insights include: 

• Scalability and performance gaps are primary causes of AI implementation failures. 

• Flexible, cloud-based networking solutions such as SD-WAN and SASE are vital for sustainable AI scalability. 

• CIOs must shift from incremental upgrades to platform-first strategies, building unified digital fabrics that seamlessly integrate data, connectivity, and automation. 

• The shortage of skilled networking talent in regions like APAC underscores the importance of partnering with managed service providers (MSPs) to accelerate implementation and maintain performance. 

As AI transforms from experimental to mission-critical, the CIO’s role is also evolving. The article concludes that the future of AI success depends less on algorithms and more on the strength of the underlying network. Organizations that prioritize infrastructure readiness today will best position to scale AI innovation sustainably tomorrow. 

Source: 

https://www.itnews.asia/news/is-your-network-sabotaging-your-ai-strategy-621077