Why Cloud Economics is the answer to the AI innovation/cost issue
It’s an age-old riddle for business leaders: there’s an opportunity in front of you, but it comes at a cost. Can you afford to do it? Or perhaps even more relevant: can you afford not to?
This dilemma has probably never been as acute as it is now. The opportunities presented by emerging technologies such as AI are clear to all: it has the potential to supercharge the enterprise cloud platform and enable organizations to innovate and stay competitive.
However, unlocking this potential means using additional processing power and computational scale to hold and manage the massive amounts of reference data that AI requires. As a result, cloud investment decisions are increasingly driven by the need to support AI.
Managing Partner and Global Head of Wipro FullStride Cloud.
The cost management gap
But at what price? New research from Wipro shows that while 54% of organizations cite AI/GenAI as the main driver for cloud investment, 43% of UK organizations do not have a coordinated or centralized approach to managing their cloud costs – this is significantly higher than the corresponding figures for France and Germany 25% and 24% respectively.
There’s no doubt that the unique nature of cloud spending is a factor here. In addition to the additional processing and compute scale referenced above, creating test sandboxes and accommodating new user adoption are also both important requirements. Furthermore, cloud spend is also consumption-based and therefore continuously variable – unlike a fixed-cost model such as a non-SaaS ERP system. But just like an ERP system, many departments and business functions use the cloud, meaning a single company can have multiple functions and rack up their own cloud costs. Such siled management can lead to duplicate spend, increasing cloud costs and decreasing ROI for the business.
Taking all of the above into account, this gap in cost management is a real cause for concern, especially when you take into account two other key findings from the new research: namely that 54% of organizations plan to reduce investments in the hybrid cloud and 56% plan to increase hybrid cloud investments. to increase investments in the public cloud. With AI cloud investments set to continue to make up the majority of enterprise technology budgets, it is critical that we quickly close this gap in cost management.
But how do companies get the control they need over cloud spending without limiting innovation or the new technologies that are only truly made possible through the cloud?
From costs to value
The answer lies in cloud economics, a collaborative, pragmatic process that brings clarity to cloud spending by helping organizational leaders (IT, operations, finance, development, business units) define what value means in terms of cloud investments and then develop strategy accordingly. This approach encourages different business functions to look beyond cost optimization to make decisions that maximize the business value of the cloud.
To me, this shift in focus from cost to value is the key to a successful cloud program. The reason many first-time cloud migrations produce average results is because the company’s primary focus is on moving to the cloud as a way to save costs. And while it’s true that moving to the cloud can make some processes more cost-effective, it will also likely require upfront time and money.
Companies that focus solely on cost savings may view these investments as a “failure,” but they are not looking at the bigger picture. The key to saving money in the long term is integrating AI and automation into all operations, with confidence knowing that investing the time and resources in specific areas of cloud development will lay the foundation for capabilities and improvements critical to the larger business goals.
Cloud economics can help companies identify their own specific cloud goals and the actions needed to achieve them. In doing so, companies learn how to optimize cloud costs and maximize cloud value by aligning diverse business teams around shared investment goals. This is an attempt at organizational change management that requires the company to work together to achieve like-minded goals.
The role for FinOps
As the cloud program progresses – through the evolution of capital spending models and changing business needs – other tools like FinOps can help business managers further optimize cloud spend and business value.
FinOps is a part of cloud economics that focuses on operational aspects. Now that the company has moved to the cloud, how can it best manage its cloud spend to achieve its cloud goals? What areas are external cloud spends being made in? Which areas need more investment? How can teams focus or redirect their cloud investments without disrupting business operations?
To answer these questions, FinOps uses an iterative approach in three phases: inform, optimize and operate.
1. Inquire Increasing transparency of cloud spend, budgets, benchmarks, forecasts, etc., and giving teams the information they need to make cloud spend decisions that align with business goals.
2. Optimize Implement changes to optimize cloud usage.
3. Operate Integrate analytics and optimization into daily business operations, track the progress of cloud programs and adjust as necessary.
Through FinOps, companies can identify areas of overspending, take corrective action, and find the best way to reinvest those savings. For example, through FinOps, a company may find that it is paying for much more storage space than it needs based on average usage. Downsizing storage space could free up money that the company could reinvest in other directions based on the cloud goals outlined in the cloud economy.
Come on
When I assess the overall business landscape, the fact that more than 50% of organizations see AI as the primary driver for cloud investment truly opens up a whole new world of possibilities. But this emerging gap in cost management needs to be addressed.
For example, as a cloud leader, we play our role by training our employees on Google Cloud AI technologies to better help global enterprise customers provision, deploy and manage AI projects that solve their unique business objectives. This will significantly improve critical digital transformation projects such as application migrations and modernization, with GenAI-powered productivity improvements of up to 30%.
But if we want to collectively unlock AI’s true potential, we must work together to embrace a cloud economy approach that is driven by value, not cost.
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