Experts predict that 2024 will see a rise in cloud spending, mostly attributable to the rise within the adoption of artificial intelligence (AI) and generative AI (GenAI). Savvy corporations are already examining their use of AI and GenAI, specializing in the prices related to cloud budgets. The following 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 giant a part of it. AI depends on high-quality data and a lot of it. Consider it this fashion—the more data that is out there to an AI algorithm, the higher the outcomes will likely be. Yet having massive amounts of information does present certain challenges that may often lead to a rise in costs to support the applying, especially within the cloud.
And never all AI is identical. For instance, one type of AI, natural language processing (NLP) could be very data intensive. NLP will be utilized in customer support automation, summarizing documents, and creating emails, simply to name a couple of applications. Each task becomes increasingly complex when a business requires contextualized responses. For example, reviewing sensitive data in highly regulated industries like finance or healthcare.
There has also been tremendous growth in the quantity of information that will be analyzed. Modern generative AI models are sometimes between ten to 100 times larger than even the AI models from one or two years ago. With larger models and increasing complexity of information, 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 corporations took a lift-and-shift migration approach, also referred to as rehosting, but that was very costly. It turned out that many corporations ended up paying for services that they didn’t use often. More recently, as businesses examine their cloud usage, they are attempting to seek out ways to chop costs by eliminating the overspending from the past few years. Yet the incredible growth of AI and GenAI has caused corporations to reconsider their cloud infrastructure.
 Cloud Economics: A Strategic Approach
That’s why it’s necessary to grasp what value AI can bring to a corporation. Company leaders must set clear expectations for a way AI will deliver value to the business, and all teams involved in AI projects should collaborate inside a shared framework for approving AI-driven initiatives.
A superb strategy for managing the prices of AI is to leverage cloud economics. This involves performing a cost-benefit evaluation to align the investments made in cloud technologies and business priorities. The goal isn’t simply to scale back costs and increase efficiency, it’s to maximise overall business value.
Cloud economics helps businesses manage the prices related to AI, while continuing to take a position in progressive technologies. Applying cloud economics to AI costs makes AI initiatives align with long-term business goals.
For instance, we worked with an organization that desired to overhaul their manual, bottom-up revenue forecasting process. Previously, the corporate had an inefficient system that lacked the precision to maintain 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 right into a 15% increase in revenue inside the first 12 months of implementation.
One other good practice is to deploy intelligent workload management to automate resources, and adjust on the fly attributable to AI demands, which frees up resources for other projects.
To start on this journey, an organization might create a team dedicated to cloud economics, ideally with a cloud center of excellence (Cloud CoE) performing a coordinating function. This permits businesses to agree on decisions about spending and which projects are price consideration, to be certain 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 your complete organization. The advantages 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.
- Higher resource utilization and a discount in wasteful spending, allowing businesses to reap the total advantages of their cloud and AI investments.
We’ll proceed to see an increase 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.
