It is an indisputable fact that Artificial Intelligence (AI) is an impactful tool in the tech industry. The rise of AI has undeniably empowered enterprises to leverage its capabilities in creating a wide array of applications, and significantly enhancing operational efficacy. However, when deploying these powerful generative AI technologies, organizations must remain cautious of certain complexities that might arise, especially in the realm of cloud operations, a critical component of Isotropic’s Full Stack Ops, which include CloudOps, DataOps, and AIOps services.
In my career as an AI specialist, I have experienced both the advantages and complications of integrating AI with enterprises and cloud architectures. While the benefits are noteworthy, it is crucial to be aware of the complexities and manage them strategically to limit any potential negative consequences. Let’s delve deeper into the top five complexities of generative AI for CloudOps professionals.
Accelerated Cloud Application Deployments
The advent of generative AI tools has simplified and expedited the application development process with no-code or low-code mechanisms. However, such acceleration has often led to a reckless approach towards application development, with organizations overlooking the comprehensive role of applications. There is a surge in applications that are usually created for specific tactical needs and are frequently redundant. This accelerated deployment of applications burdens the CloudOps teams who end up managing thrice or quintuple the amount of applications and related databases than necessary, thereby increasing the costs and making scalability a challenge.
Scaling Challenges
Generative AI systems demand substantial compute and storage resources. Merely increasing storage and compute services doesn’t necessarily solve the problem. It requires thoughtful planning to identify and deploy resources to support the rapid expansion of AI-powered systems, a task usually incumbent on the ops teams. They have to allocate resources effectively so as not to undermine the system’s value or limit its capabilities. It’s a delicate balancing act with seemingly endless trade-offs.
Cost Overruns
Generative AI systems can lead to spikes in cloud expenditure. Managing such cost overruns is more of a business issue than a technical one. Enterprises need to thoroughly understand the reasons behind their cloud spending and the business benefits derived from it. Only then can these costs be factored into pre-defined budgets, a process which may cause concern for businesses with limited cloud spending.
Business Justification
The appeal of generative AI technologies often lures line-of-business developers. However, these systems can be costly. Businesses must either find the necessary funding, justify the business need, or both. In many cases, the use of generative AI, though popular and hyped, cannot be justified cost-wise. Traditional development approaches may suffice for simpler tactical tasks, making the overapplication of AI an expensive choice.
Overuse of Generative AI
Generative AI, despite its complexities, remains popular. However, its usage for straightforward tasks that could be handled with more traditional development approaches is an ongoing issue. The overuse of AI technology for business problems for which it is not justified underscores the need for discerning application of the technology.
As generative AI matures, these complexities could pose significant challenges to cloud operations. Isotropic’s Full Stack Ops services offer a comprehensive solution to manage these complexities, ensuring optimal performance and business value. However, an understanding of these complexities and a strategic approach to manage them can minimize their impact on your cloud operations.
