Why Cloud Economics is the answer to the AI innovation/cost conundrum | usagoldmines.com

It’s an age-old conundrum for business leaders – there’s an opportunity in front of you but it comes with a cost. Can you afford to do it? Or, perhaps more pertinently, can you afford not to do it?

This dilemma has arguably never been as acute as it is today. The opportunity afforded by emerging technologies such as AI is clear for all to see – it has the potential to turbo-charge the enterprise cloud platform and empower organizations to innovate and stay competitive.

However, unlocking this potential means using additional processing power and computing scale to hold and manage the huge volumes of reference data required by AI and as a result cloud investment decisions are increasingly being driven by the need to support AI.

The cost management gap

But at what cost? New research from Wipro reveals that whilst 54% of organizations cite AI/GenAI as the top 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 at 25% and 24%, respectively.

There is no doubt that the unique nature of cloud spending is a factor here – as well as the additional processing and computing scale referenced above, creating testing sandboxes and accommodating new user adoption are both key requirements too. In addition, cloud spending is also based on consumption and, as a result, constantly variable – unlike a fixed-cost model such as a non-SaaS ERP system. However, like an ERP system, many departments and business functions use the cloud, meaning a single enterprise can have multiple functions accumulating their own cloud costs. Such siloed management can lead to duplicative spending, inflating the cost of cloud and diluting the ROI for the business.

When you consider all of the above, this cost management gap is a real cause for concern, especially when you factor in two other key findings from the new research – namely, that 54% of organizations plan to increase hybrid cloud investment and 56% plan to increase public cloud investment. With AI cloud investment set to continue to make up the majority of enterprise technology budgets, it’s vital that we bridge this cost management gap and quickly.

But how do businesses get the control they need over cloud spending without limiting innovation or the new technologies only truly enabled through the cloud?

Moving from cost to value

The answer lies in cloud economics, a collaborative, pragmatic process which brings clarity to cloud spending by helping organization leaders (IT, operations, finance, development, business units) define what value means in terms of cloud investments and then develop a strategy accordingly. This approach encourages the different business functions to look beyond optimizing costs in order to make decisions that will maximize the business value of the cloud.

For me, this shift in focus from cost to value is the key to a successful cloud program. The reason many first-time cloud migrations deliver average results is because the business is focused primarily on moving to the cloud as a way to cut costs. And whilst it’s true that moving to the cloud can help make some processes more cost-effective, doing so is also likely to require time and money upfront.

Businesses solely focused on cutting costs may view these investments as a ‘failure’ but they’re not looking at the bigger picture – the key to saving money in the long term is to incorporate AI and automation throughout operations, confident in the knowledge that investing time and resources in specific areas of cloud development will lay the groundwork for capabilities and advancements critical to the larger business goals.

Cloud economics can help businesses identify their own specific cloud goals and the actions necessary to achieve them. In doing so, the businesses learn how to optimize cloud costs and maximize the value of cloud by aligning the various business teams around shared investment goals. This is an organizational change management endeavor that requires the business to act in concert to achieve like-minded goals.

The role for FinOps

As the cloud program progresses – through the evolution of investment spending models and changing company needs – other tools such as FinOps can help business managers further optimize cloud spending and business value.

FinOps is a component of cloud economics that focuses on operational aspects. Now that the business has moved to the cloud, how does it best manage its cloud spending to achieve its cloud goals? What are the areas of extraneous cloud spending? What areas need greater investment? How can teams focus or redirect their cloud investments without disrupting business operations?

To answer these questions, FinOps uses a three-phase iterative approach: inform, optimize and operate.

1. Inform Increasing transparency of cloud spending, budgets, benchmarks, forecasting, etc., and giving teams the information they need to make decisions about cloud spending that align with the goals of the business.

2. Optimize Implementing changes to optimize cloud consumption.

3. Operate Integrating analysis and optimization into day-to-day operations, tracking the progress of cloud programs and adjusting as needed.

Through FinOps, businesses can identify areas of overspending, corrective actions and the best way to reinvest those savings. For example, through FinOps, a company may learn that it’s paying for much more storage space than it needs based on average usage. Downsizing the storage could free up funds that the company could reinvest in other avenues based on the cloud goals outlined through cloud economics.

Moving forward

As I assess the overall business landscape, the fact that over 50% of organizations view AI as the primary driver for cloud investment really does open up a whole new world of opportunities. But this emerging cost management gap needs to be addressed.

As a cloud leader, we are playing our part, for example, by training our associates on Google Cloud’s AI technologies to better help global enterprise customers scope, deploy and manage AI projects that solve their unique business objectives. This will significantly enhance vital digital transformation projects such as application migrations and modernization, with GenAI-powered productivity improvements of up to 30%.

But collectively, if we’re going to unlock the true potential of AI, we need to work together to embrace a cloud economics approach which is powered by value not by cost.

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