Experts predict cloud spending will increase in 2024, primarily due to the increased use of artificial intelligence (AI) and generative AI (GenAI). Savvy enterprises are already evaluating their use of AI and GenAI, focusing on costs associated with their cloud budgets. The next step is to adopt management strategies that optimize investments and ensure sustainable business growth.
AI and Data: A Symbiotic Relationship
How does AI contribute to cloud costs? Data is a big part of it. AI relies on high-quality, large volumes of data. You can think of it this way: the more data an AI algorithm has available, the better its results. But working with large volumes of data poses certain challenges and often makes supporting applications more expensive, especially in the cloud.
And AI isn’t just AI. For example, one form of AI, natural language processing (NLP), processes very large volumes of data. NLP can be used to automate customer service, summarize documents, and write emails, to name a few applications. Every task becomes more complex as businesses require context-specific answers, such as checking sensitive data in highly regulated industries like finance and healthcare.
The amount of data that can be analyzed has also increased dramatically; modern generative AI models are often tens to hundreds of times larger than AI models from a year or two ago. Larger models, greater data complexity, and more use cases mean growing data requirements and higher cloud costs.
To make matters further, the way cloud is integrated into enterprise IT infrastructure is also changing. Initially, many companies relied on a lift-and-shift migration approach, also known as rehosting, which was very costly. Many companies found themselves paying for services they did not use frequently. Recently, companies have been reassessing their cloud usage to eliminate overspending from recent years and reduce costs. However, the incredible growth of AI and GenAI has prompted companies to reconsider their cloud infrastructure.
Cloud Economics: A Strategic Approach
That’s why it’s important to understand the value AI can bring to your enterprise. Business leaders need to set clear expectations about what value AI can bring to the business, and all teams involved in AI projects need to collaborate under a common framework to approve AI-driven initiatives.
A good strategy for managing AI costs is to take advantage of cloud economics. This includes conducting a cost-benefit analysis to align cloud technology investments with business priorities. The goal is to maximize overall business value, not just reduce costs and increase efficiencies.
Cloud economics can help businesses manage the costs associated with AI while continuing to invest in innovative technologies. Applying cloud economics to AI costs ensures that AI efforts are aligned with long-term business goals.
For example, we worked with a company that wanted to overhaul their manual, bottom-up sales forecasting process. Previously, the company was using an inefficient system that lacked the accuracy to keep up with rapidly changing market conditions. The goal was to implement a data-driven approach that would improve forecast accuracy, drive sales and marketing strategies, and generate increased revenue. We built an AI-based solution that leverages 30 types of historical financial and marketing data, as well as publicly available consumer sentiment data. This data-driven approach reduced forecasting time by 50%, improved the reliability of revenue forecasts, provided actionable insights to sales and marketing teams, and increased revenue by 15% in the first year of implementation.
Another best practice is to automate resources using intelligent workload management to adjust resources on the fly based on AI requirements, freeing up resources for other projects.
To take this approach, companies should establish a cloud economy team and ideally organize a cloud center of excellence (cloud CoE) to serve as a coordinating role. This allows the company to agree on spending decisions and decide which projects to consider to ensure all AI initiatives are aligned with strategic goals and best practices.
A well-managed cloud economy team helps optimize performance across the organization. Benefits of strategic cloud management include:
- Improved decision-making through understanding data usage and variable cost models.
- Align AI investments with business goals to optimize business performance.
- Help businesses get the most out of their cloud and AI investments by making better use of resources and reducing unnecessary expenses.
AI and cloud investments are expected to continue to surge in 2024 as businesses realize the transformative value of adopting intelligent management strategies that help maximize value and control costs. Cloud Economics provides a framework to help balance business goals with AI and cloud costs, ensuring technology investments deliver maximum value. A strategic approach can help businesses navigate the complexities of AI adoption to position themselves for long-term success.
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