AI’s role in the enterprise is no longer experimental — it is essential
Artificial intelligence has firmly established itself as a cornerstone of modern business transformation. From enhancing customer service to streamlining operations and uncovering insights from data, AI’s role in the enterprise is no longer experimental — it is essential.
For many organizations, this journey has been powered by cloud-based infrastructure. The cloud has allowed businesses to quickly scale AI tools without investing in specialist hardware, accelerating adoption and innovation. However, as AI moves from tactical deployments to deeper integration across operations, cracks are beginning to show in the cloud-first model.Latency, Cost and Compliance Risks
Real-time responsiveness is critical to the success of AI applications, especially for tasks that require instant decision making. Cloud processing, by its nature, depends on the transmission of data across networks, introducing latency that can affect performance. In sectors like finance, healthcare or manufacturing, even small delays can make a meaningful difference.
At the same time, the financial burden of running large-scale AI models in the cloud is growing. What was once a cost-efficient way to experiment with AI can become expensive at scale. As models increase in complexity and the demand for high-volume data processing grows, many businesses are finding that cloud service costs escalate quickly and sometimes unpredictably.
Then there is the issue of data governance.
