The cloud computing boom brought innovation and showed us the need for strategic planning to avoid costly mistakes such as “shadow IT” (IT activities or purchases made without the knowledge of the IT department), which led to unexpected expenses and security issues. With generative AI rising, IT leaders can apply these lessons to avoid similar pitfalls with “shadow AI,” the unchecked use of AI without precise planning.
Generative AI promises significant economic impact, but it demands careful strategic planning. Public clouds can’t be the go-to option for AI; you must also consider the value of on-premises deployments. Most cloud fans see this as blasphemy, but it will likely save many enterprises millions of dollars during the next few years. Studies show that running AI on premises can be more cost-effective than in the cloud. An on-premises approach prioritizes data safety, sovereignty, and proper management, sidestepping potential issues like data gravity and costly reconfigurations.
Why cloud isn’t always the answer for AI
The cloud computing revolution heralded a new era of innovation, offering unparalleled access to computing resources and enabling digital transformation on a massive scale. However, rapid adoption and hasty implementation also resulted in escalating costs, security vulnerabilities, and governance challenges, all common with shadow IT.