In a world where the promises of technology often seem to fall short, the debate over the effectiveness of cloud computing continues to echo. Critics point to the rising costs and complexities, with some major players even returning to on-premise solutions. But is this criticism justified?
Now, let us dig into the discussion and take a closer look at cloud computing and artificial intelligence (AI). These technologies, while presenting some challenges, hold great potential for positive change when used wisely.
Paradox of Cloud Computing
Cloud computing is often celebrated as the solution for businesses in search of flexibility and cost savings. It has truly transformed our daily lives by making services readily accessible with just a click. It unquestionably fulfills its promises. However, the picture is not all bright and cheerful.
Large enterprises which are moving their IT workflows back on-premises do so largely because they have achieved economies of scale that make on-premises solutions more cost-effective. Ironically, they initially leveraged cloud computing to scale and grow in the first place. This raises the question whether the cloud is losing its cost-effective allure.
The idea that cloud costs are going up is often because things are not running efficiently, not because the technology is to blame. It is not that the cloud inherently became more expensive, but rather that the complexities of managing it were underestimated. Just as they created FinOps to fix these problems with cloud costs, we wll face similar challenges when we start using AI.
As organizations set out on their AI journey, they will face unique challenges. While AI holds immense promise, achieving its full potential demands a thoughtful approach. To facilitate a more streamlined, cost-efficient and impactful AI integration, it is essential to ponder over these four key questions:
One of the most significant mistakes organizations make with cloud computing is using it tactically to drive cost control rather than strategically for innovation. Ultimately, cloud computing aims to help companies create customer value. However, most organizations still need help tying their cloud strategy to organizational outcomes. This same mistake should be avoided with AI by having a clear and compelling use case tied to customer value.
Just as many organizations struggled to adapt to the cloud without a strong DevOps or Agile foundation, AI adoption requires a solid cloud architecture and a robust data foundation. AI relies heavily on cloud computing, especially for the intensive GPU (graphics processing unit) requirements. It is nearly impossible to move forward with AI adoption without these foundational principles in place. Additionally, establishing a strong data foundation including collection, cleaning and distribution is crucial. The “garbage in, garbage out” principle is as relevant for AI as it is for cloud computing.
It is not surprising that cloud cost optimization, known as FinOps, is on the rise, given that many companies have simply moved their applications from in-house to the cloud without making significant changes. Moving applications to the cloud without making improvements might save money initially, but in the long run, it could end up costing more. The same applies to AI. If organizations are struggling with the cost of cloud computing, they will be in for a rude awakening once they let their developers run machine learning models. The high cost of AI will be a reality and enterprises must learn from cloud computing to control the consumption of intensive and expensive compute utilization.
Just as many enterprises needed to see the breadth and depth of the cloud’s impact on their entire organizations, companies still need to improve their cloud computing skills today. Recent research found the largest cloud skills gaps exist in data, analytics, and storage, followed by security and governance. Clearly, a critical mass of cloud fluency is essential for fostering a sustainable transition to the new operating model. Cloud is a culture, and there’s a language to learn to participate. The same goes for AI.
For enterprises to succeed with AI, skills development must start early and often. Training your workforce should not be limited to just tech experts, but it should also involve business leaders and other people in the chain who bring value to the process. Ultimately, the goal is to connect artificial intelligence with a company’s most strategic advantage, human intelligence. The only way to do that effectively is through continuous education and upskilling.
The ongoing debate surrounding the effectiveness of cloud computing reflects the broader discourse on innovation’s promises and challenges. Moving away from the cloud doesn’t mean it failed, but it shows how tricky success can be. While cloud computing has transformed our lives, operational inefficiencies and the need for training persist as challenges. AI faces similar challenges and it needs a smart approach that focuses on customer value, basics, costs and a well-trained team. Learning from cloud computing can help organizations do AI better, making sure they get the most from these powerful technologies.
The journey of technology is not without its bumps and detours. Cloud computing and AI both hold the promise of transformation, but they demand careful navigation and a commitment to overcoming challenges to fully realize their potential. Embracing the cloud and AI may not always be a smooth ride, but it is a path toward innovation and progress that is worth taking.