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Gartner: Cloud Will Become a Business Necessity by 2028

Global Public Cloud Services Spending to Total $679 Billion in 2024

By 2028, cloud computing will shift from being a technology disruptor to becoming a necessary component for maintaining business competitiveness, according to Gartner, Inc.

IT spending on public cloud services continues to rise unabated. In 2024, worldwide end-user spending on public cloud services is forecast to total $679 billion and projected to exceed $1 trillion in 2027.

"Organizations are actively investing in cloud technology due to its potential to foster innovation, create market disruptions, and enhance customer retention in order to gain a competitive edge," said Milind Govekar, Distinguished VP Analyst at Gartner. "While many organizations have started to seize the technical advantages of cloud, only a few have unlocked its full potential in supporting business transformation. As a result, organizations are using the cloud to launch a new wave of disruption driven by artificial intelligence (AI), enabling them to unlock business value at scale."

The Role of Cloud in 2023

More than 50% of enterprises will use industry cloud platforms by 2028 to accelerate their business initiatives

Most companies currently consider the cloud as a technology platform. In 2023, organizations are using cloud computing either as a technology disruptor or capability enabler. Gartner predicts that more than 50% of enterprises will use industry cloud platforms by 2028 to accelerate their business initiatives. In 2028, most organizations will be leveraging cloud as a business necessity.

Organizations that are utilizing the cloud as a technology disruptor are harnessing its transformative potential to revolutionize non-cloud, data-center oriented computing styles and technologies.

"As businesses navigate through digital transformation journeys, movement to the cloud becomes a key decision point," said Govekar.

Companies that are adopting cloud technology as a capability enabler are using its potential to enable new capabilities such as elasticity, rapid continuous integration/cloud delivery (CI/CD), serverless functions and AI-infused APIs and processes that were difficult to achieve pre-cloud. To exploit these new capabilities, organizations must carefully evaluate factors such as their investment in skills development, breaking down operational silos, and promoting collaboration among teams to seamlessly adopt automation.

Cloud as a Business Necessity in 2028

Over the next few years, cloud computing will continue to evolve from being an innovation facilitator to a business disruptor and, ultimately, a business necessity.

With cloud computing as an innovation facilitator, organizations can distribute platform business concepts widely by using its underlying platform technology to provide interconnections, scale, aggregation and analysis capabilities, which allows the use of technology as a fundamental component of a business model.

"By leveraging the ecosystem of cloud providers, organizations can introduce innovative products and services, such as fraud prevention solutions for second-hand cars from tire manufacturers, or rapid vaccine development through cloud-based machine learning by pharmaceutical companies," said Govekar.

By 2028, most organizations will fully transform into digital entities capable of sensing and responding to business and market conditions. "With cloud computing becoming an integral part of business operations in 2028, CIOs and IT leaders will have to implement a highly efficient cloud operating model in order to achieve their desired business objectives," Govekar concluded.

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Gartner: Cloud Will Become a Business Necessity by 2028

Global Public Cloud Services Spending to Total $679 Billion in 2024

By 2028, cloud computing will shift from being a technology disruptor to becoming a necessary component for maintaining business competitiveness, according to Gartner, Inc.

IT spending on public cloud services continues to rise unabated. In 2024, worldwide end-user spending on public cloud services is forecast to total $679 billion and projected to exceed $1 trillion in 2027.

"Organizations are actively investing in cloud technology due to its potential to foster innovation, create market disruptions, and enhance customer retention in order to gain a competitive edge," said Milind Govekar, Distinguished VP Analyst at Gartner. "While many organizations have started to seize the technical advantages of cloud, only a few have unlocked its full potential in supporting business transformation. As a result, organizations are using the cloud to launch a new wave of disruption driven by artificial intelligence (AI), enabling them to unlock business value at scale."

The Role of Cloud in 2023

More than 50% of enterprises will use industry cloud platforms by 2028 to accelerate their business initiatives

Most companies currently consider the cloud as a technology platform. In 2023, organizations are using cloud computing either as a technology disruptor or capability enabler. Gartner predicts that more than 50% of enterprises will use industry cloud platforms by 2028 to accelerate their business initiatives. In 2028, most organizations will be leveraging cloud as a business necessity.

Organizations that are utilizing the cloud as a technology disruptor are harnessing its transformative potential to revolutionize non-cloud, data-center oriented computing styles and technologies.

"As businesses navigate through digital transformation journeys, movement to the cloud becomes a key decision point," said Govekar.

Companies that are adopting cloud technology as a capability enabler are using its potential to enable new capabilities such as elasticity, rapid continuous integration/cloud delivery (CI/CD), serverless functions and AI-infused APIs and processes that were difficult to achieve pre-cloud. To exploit these new capabilities, organizations must carefully evaluate factors such as their investment in skills development, breaking down operational silos, and promoting collaboration among teams to seamlessly adopt automation.

Cloud as a Business Necessity in 2028

Over the next few years, cloud computing will continue to evolve from being an innovation facilitator to a business disruptor and, ultimately, a business necessity.

With cloud computing as an innovation facilitator, organizations can distribute platform business concepts widely by using its underlying platform technology to provide interconnections, scale, aggregation and analysis capabilities, which allows the use of technology as a fundamental component of a business model.

"By leveraging the ecosystem of cloud providers, organizations can introduce innovative products and services, such as fraud prevention solutions for second-hand cars from tire manufacturers, or rapid vaccine development through cloud-based machine learning by pharmaceutical companies," said Govekar.

By 2028, most organizations will fully transform into digital entities capable of sensing and responding to business and market conditions. "With cloud computing becoming an integral part of business operations in 2028, CIOs and IT leaders will have to implement a highly efficient cloud operating model in order to achieve their desired business objectives," Govekar concluded.

Hot Topics

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...