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Cloud could generate about $3 trillion in earnings by 2030; $1.1 trillion in the United States alone. Almost all industries can find 20% to 30% in value through cost reduction, productivity improvement, and new business models. But turning that potential into bottom-line performance has proved challenging.
Enter generative artificial intelligence (GenAI). We think GenAI could represent an inflection point for cloud platforms, both by creating high-impact use cases and by reducing the time and cost to remediate applications to run in the cloud.
When McKinsey spoke with senior leaders at more than 80 large enterprises, only about 10% said they had fully captured cloud value and half admitted that they had moved few applications to the cloud. Large companies typically run only 15% to 20% of their applications in the cloud, even though two-thirds say they want 80% of their systems in the cloud by 2030. (“Digital-native” companies tend to do better; just eight of them accounted for nearly a third of the EBITDA value gained in the last decade.)
One reason for the disconnect between aspiration and reality is that many companies seek to change gradually, spreading out investment by tapping existing funding and limiting increases to their IT budgets. An effective cloud strategy, however, means not just changing servers but refining and sometimes reinventing how technology is developed and managed. Different application architectures and infrastructure services are also required. Finally, companies may need to implement operational and organizational changes.
All this adds up, in time and money: hundreds of millions of dollars for medium-sized enterprise technology organizations and billions for larger ones. In effect, many companies are not convinced they can afford the cloud, or that the return on investment is sufficient, so they are going slow.
The risk -- or rather, the reality -- is that this approach means that the full benefits of cloud are not getting captured. But that could change. According to technology and cloud program leaders McKinsey has spoken with, GenAI could set the pace, by increasing developer productivity and cutting migration and modernization costs. In so doing, it could accelerate return on investment -- and thus cloud adoption.
There are two elements here. The first is using cloud to support GenAI initiatives. With its massive calls on computing, storage, and networking, GenAI needs enterprise cloud platforms to scale; disconnected pilots and initiatives run by individual development teams will not do the job. The creation of end-to-end, gen AI-enabled workflows, then, could create incentives for companies to speed up migration to the cloud.
The second is the inverse: using GenAI to support cloud initiatives. Remediating an application to run effectively in the cloud, for example, is expensive, and take investments equivalent to several years’ worth of support and maintenance. GenAI can augment human efforts throughout the process. In the discovery and assessment phase, for example, it can parse millions of lines of outdated code and translate them into plain language so experts can understand which code blocks drive which functions. In the planning phase, GenAI tools can help map and prioritize which code blocks to modernize and what new capabilities to add. Finally, in the conversion phase, GenAI can translate the legacy code while generating new code and creating test scripts for quality control. Early efforts to apply GenAI to these tasks have cut time and investment by an estimated 40%, although much work still is needed to understand how to do so for different applications.
To put it simply, cloud needs GenAI and GenAI needs cloud. One implication is that investment in both is needed to reap their combined potential; it is not either/or.
In terms of cloud, for example, it’s well known that it can help reduce IT costs, but the much greater value -- as much as $2.5 trillion of the total -- comes from enabling businesses to innovate. For that to happen, it’s critical to build a strong cloud foundation that provides ready-to-use, configurable solutions that connect the back end, data, and cloud infrastructure. This effort is most likely to succeed under an agile operating model, meaning one that combines stable elements that evolve slowly with dynamic capabilities that adapt quickly. In this sense, the cloud looks a lot more like software engineering -- where the idea of agile originated -- than traditional IT system administration. And an agile operating model also works best for gen AI.
Cloud platforms could enable massive value in every sector. GenAI could dramatically reduce the cost of adopting cloud -- and also unlock new business and tech use cases. No matter how attractive the ultimate benefits might be, those “coulds” won’t just happen. Companies will have to move with purpose and speed. Those that don’t could find themselves at a significant disadvantage, especially as new technologies emerge.
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