This post was originally published on IT Pro Today
By Eric Ethridge, DoiT
According to recent research from Grand View Intelligence, the global AI industry generated approximately $197 billion in revenue during 2023, a figure expected to increase at a compound annual growth rate (CAGR) of 37.3% through 2030. With this influx of capital and new technology, challenges remain for organizations to integrate and capitalize on AI quickly and efficiently. Any use of the technology must be customized to fit specific business needs, and the return on investment (ROI) of every initiative must be monitored. A good way to ensure this happens is by leveraging financial operations (FinOps), a management practice emphasizing shared responsibility for cloud infrastructure and its cost.
Foundation to Forecasting
Just as cloud computing allows on-demand scaling of resources, AI must advance decision-making and scale automation to produce greater cost efficiency and savings. The problem is that many businesses create AI strategies on the fly, hoping to quickly realize gains and boost workforce productivity. What’s really needed before applying AI to operations is a strong foundation focused on managing costs, resource allocation and tracking ROI.
These elements are also critical to FinOps, and by employing its practices, companies can overcome AI technical complexity, enhance processes, and greatly reduce errors. Further,
— Read the rest of this post, which was originally published on IT Pro Today.
