Advancing to adaptive cloud [MIT Tech Review]

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For many years now, cloud solutions have helped organizations streamline their operations, increase their scalability, and reduce costs. Yet, enterprise cloud investment has been fragmented, often lacking a coherent organization-wide approach. In fact, it’s not uncommon for various teams across an organization to have spun up their own cloud projects, adopting a wide variety of cloud strategies and providers, from public and hybrid to multi-cloud and edge computing.

The problem with this approach is that it often leads to “a sprawling set of systems and disparate teams working on these cloud systems, making it difficult to keep up with the pace of innovation,” says Bernardo Caldas, corporate vice president of Azure Edge product management at Microsoft. In addition to being an IT headache, a fragmented cloud environment leads to technological and organizational repercussions.

A complex multi-cloud deployment can make it difficult for IT teams to perform mission-critical tasks, such as applying security patches, meeting regulatory requirements, managing costs, and accessing data for data analytics. Configuring and securing these types of environments is a challenging and time-consuming task. And ad hoc cloud deployments often culminate in systems incompatibility when one-off pilots are ready to scale or be combined with existing products.

Without a common IT operations and application development platform, teams can’t share lessons learned or pool important resources, which tends to cause them to become increasingly siloed. “People want to do more with their data, but if their data is trapped and isolated in these different systems, it can make it really hard to tap into the data for insights and to accelerate progress,” says Caldas.

As the pace of change accelerates, however, many organizations are adopting a new adaptive cloud approach—one that will enable them to respond quickly to evolving consumer demands and market fluctuations while simplifying the management of their complex cloud environments.

An adaptive strategy for success

Heralding a departure from yesteryear’s fragmented cloud environments, an adaptive cloud approach unites sprawling systems, disparate silos, and distributed sites into a single operations, development, security, application, and data model. This unified approach empowers organizations to glean value from cloud-native, open source, and AI technologies across hybrid, multi-cloud, edge, and IoT.

“You’ve got a lot of legacy software out there, and for the most part, you don’t want to change production environments,” says David Harmon, director of software engineering at AMD. “Nobody wants to change code. So while CTOs and developers really want to take advantage of all the hardware changes, they want to do nothing to their code base if possible, because that change is very, very expensive.”

An adaptive cloud approach answers this challenge by taking an agnostic approach to the environments it brings together on a single control plane. By seamlessly collecting disparate computing environments, including those that run outside of hyperscale data centers, the control plane creates greater visibility across thousands of assets, simplifies security enforcement, and allows for easier management.

An adaptive cloud approach enables unified management of disparate systems and resources, leading to improved oversight and control. An adaptive approach also creates scalability, as it allows organizations to meet the fluctuating demands of a business without the risk of over-provisioning or under-provisioning resources.

There are also clear business advantages to embracing an adaptive cloud approach. Consider, for example, an operational technology team that deploys an automation system to accelerate a factory’s production capabilities. In a fragmented and distributed environment, systems often struggle to communicate. But in an adaptive cloud environment, a factory’s automation system can easily be connected to the organization’s customer relationship management system, providing sales teams with real-time insights into supply-demand fluctuations.

A united platform is not only capable of bringing together disparate systems but also of connecting employees from across functions, from sales to engineering. By sharing an interconnected web of cloud-native tools, a workforce’s collective skills and knowledge can be applied to initiatives across the organization—a valuable asset in today’s resource-strapped and talent-scarce business climate.

Using cloud-native technologies like Kubernetes and microservices can also expedite the development of applications across various environments, regardless of an application’s purpose. For example, IT teams can scale applications from massive cloud platforms to on-site production without complex rewrites. Together, these capabilities “propel innovation, simplify complexity, and enhance the ability to respond to business opportunities,” says Caldas.

The AI equation

From automating mundane processes to optimizing operations, AI is revolutionizing the way businesses work. In fact, the market for AI reached $184 billion in 2024—a staggering increase from nearly $50 billion in 2023, and it is expected to surpass $826 billion in 2030.

But AI applications and models require high-quality data to generate high-quality outputs. That’s a challenging feat when data sets are trapped in silos across distributed environments. Fortunately, an adaptive cloud approach can provide a unified data platform for AI initiatives.

“An adaptive cloud approach consolidates data from various locations in a way that’s more useful for companies and creates a robust foundation for AI applications,” says Caldas. “It creates a unified data platform that ensures that companies’ AI tools have access to high-quality data to make decisions.”

Another benefit of an adaptive cloud approach is the ability to tap into the capabilities of innovative tools such as Microsoft Copilot in Azure. Copilot in Azure is an AI companion that simplifies how IT teams operate and troubleshoot apps and infrastructure. By leveraging large language models to interact with an organization’s data, Copilot allows for deeper exploration and intelligent assessment of systems within a unified management framework.

Imagine, for example, the task of troubleshooting the root cause of a system anomaly. Typically, IT teams must sift through thousands of logs, exchanging a series of emails with colleagues, and reading documentation for answers. Copilot in Azure, however, can cut through this complexity by easing anomaly detection of unanticipated system changes while, at the same time, providing recommendations for speedy resolution.

“Organizations can now interact with systems using chat capabilities, ask questions about environments, and gain real insights into what’s happening across the heterogenous environments,” says Caldas.

An adaptive approach for the technology future

Today’s technology environments are only increasing in complexity. More systems, more data, more applications—together, they form a massive sprawling infrastructure. But proactively reacting to change, be it in market trends or customer needs, requires greater agility and integration across the organization. The answer: an adaptive approach. A unified platform for IT operations and management, applications, data, and security can consolidate the disparate parts of a fragmented environment in ways that not only ease IT management and application development but also deliver key business benefits, from faster time to market to AI efficiencies, at a time when organizations must move swiftly to succeed.

Microsoft Azure and AMD meet you where you are on your cloud journey. Learn more about an adaptive cloud approach with Azure.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.