Experts predict that 2024 will see an increase in cloud spending, mostly due to the rise in the adoption of artificial intelligence (AI) and generative AI (GenAI). Savvy companies are already examining their use of AI and GenAI, focusing on the costs associated with cloud budgets. The next step is adopting management strategies that optimize their 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 is dependent on high-quality data and lots of it. Think of it this way—the more data that is available to an AI algorithm, the better the results will be. Yet having massive amounts of data does present certain challenges that can often result in an increase in costs to support the application, especially in the cloud.
And not all AI is the same. For example, one form of AI, natural language processing (NLP) is very data intensive. NLP can be used in customer service automation, summarizing documents, and creating emails, just to name a few applications. Each task becomes increasingly complex when a business requires contextualized responses. For instance, reviewing sensitive data in highly regulated industries like finance or healthcare.
There has also been tremendous growth in the volume of data that can be analyzed. Modern generative AI models are often between ten to 100 times larger than even the AI models from one or two years ago. With larger models and increasing complexity of data, plus additional use cases, the demand for data increases which also means the cloud costs increase.
Complicating matters further, methodologies for integrating the cloud into corporate IT infrastructures are changing. Early on, many companies took a lift-and-shift migration approach, also known as rehosting, but that was very costly. It turned out that many companies ended up paying for services that they didn’t use often. More recently, as businesses examine their cloud usage, they are trying to find ways to cut costs by eliminating the overspending from the past few years. Yet the incredible growth of AI and GenAI has caused companies to reconsider their cloud infrastructure.
Cloud Economics: A Strategic Approach
That’s why it’s important to understand what value AI can bring to an organization. Company leaders must set clear expectations for how AI will deliver value to the business, and all teams involved in AI projects should collaborate within a shared framework for approving AI-driven initiatives.
An excellent strategy for managing the costs of AI is to leverage cloud economics. This involves performing a cost-benefit analysis to align the investments made in cloud technologies and business priorities. The goal isn’t simply to reduce costs and increase efficiency, it’s to maximize overall business value.
Cloud economics helps businesses manage the costs associated with AI, while continuing to invest in innovative technologies. Applying cloud economics to AI costs makes AI initiatives align with long-term business goals.
For example, we worked with a company that wanted to overhaul their manual, bottom-up revenue forecasting process. Previously, the company had an inefficient system that lacked the precision to keep up with rapidly changing market conditions. The goal was to implement a data-driven approach that enhanced forecasting accuracy which drove sales and marketing strategies and generated revenue growth. We designed an AI-based solution leveraging 30 historical financial and marketing data types alongside publicly sourced consumer sentiment data. This data-driven approach led to a 50% reduction in forecasting time, which increased the reliability of revenue predictions, and provided actionable insights for the sales and marketing teams, which translated into a 15% increase in revenue within the first year of implementation.
Another good practice is to deploy intelligent workload management to automate resources, and adjust on the fly due to AI demands, which frees up resources for other projects.
To get started on this journey, a company might create a team devoted to cloud economics, ideally with a cloud center of excellence (Cloud CoE) performing a coordinating function. This allows businesses to agree on decisions about spending and which projects are worth consideration, to ensure that all AI initiatives are aligned with strategic objectives and best practices.
Having a well-managed cloud economics team in place helps optimize performance across the entire organization. The benefits of strategic cloud management include:
- The improved decision-making that comes from understanding data usage and variable cost models.
- Aligning business objectives with AI investments to optimize business performance.
- Better resource utilization and a reduction in wasteful spending, allowing businesses to reap the full benefits of their cloud and AI investments.
We’ll continue to see a rise in AI and cloud investments in 2024 as businesses see the transformative value in adopting intelligent management strategies, which help maximize value and control costs. The framework provided by cloud economics makes it easier to align business objectives with AI and cloud costs, ensuring that technology investments deliver maximum value. By taking a strategic approach, businesses can navigate the complexities of AI adoption and achieve long-term success.
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