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3 Lessons About the Future of the Cloud

Steve Francis

Most companies are adopting a cloud strategy. In ServiceNow's Cloud Tipping Point Survey, half of all enterprises reported they are now "cloud-first," meaning that the next workload they deploy will go to the cloud instead of their data center.

It took 20 years from the time the term "cloud computing" was coined to reach this milestone. When will we be at a point where virtually all enterprise workloads are run in the cloud and how will that change things for IT?

To find out, we commissioned our own survey, Cloud Vision 2020: The Future of the Cloud. We started with a group of core influencers – people whose job it is to follow cloud computing. This group of 88 "cloud cognoscenti" included industry analysts, media, consultants and cloud vendors. We then followed that up with another survey fielded at the 2017 AWS re:Invent conference in Las Vegas where we received 195 additional responses from the people tasked with workload deployments in the real world.


The results were fascinating. I'll share three fundamental lessons we learned in the survey as well as some advice for going forward.

Lesson 1: The Reasons Why Enterprises Embrace Cloud Computing Are Changing

Why do enterprises use cloud today? The drivers of cloud computing today will sound very familiar: digital transformation, IT agility and the rise of the DevOps culture.

Those make perfect sense. Digital transformation aims to put the customer at the center of a company's automation strategy, and cloud is an excellent way to accomplish that. IT agility is much easier to achieve when someone else is responsible for your infrastructure, and you can focus on applications. The DevOps culture relies on cloud computing to achieve the speed and efficiency it was designed to deliver.

But by 2020 we expect those drivers to shift, revealing a new top-driver: artificial intelligence (AI)/machine learning. That wasn't what I expected, but it makes sense.

First, AI provides the ability to extract insight from the massive "data lakes" that enterprises are collecting about their application performance and behavior. Similarly, public cloud provides the scale to provide the massive compute resources AI needs.

But more than storage and compute, the public cloud is quickly becoming a hub for AI services that developers can integrate to build sophisticated AI applications. AWS, for example, has been busy adding Machine Learning-as-a-Service capabilities.

Lesson 2: It's Going to be a Hybrid World for the Foreseeable Future

ServiceNow's survey showed that half of all enterprises are now cloud-first, but that means half are not. And, even if an enterprise is cloud-first, it will still have many legacy workloads in an on-premises data center.

We asked survey respondents to forecast when they felt nearly all (95 percent) workloads would finally be in the cloud. Predictably, a few enthusiastic cloud supporters predicted this would happen within one year (6 percent) or two years (9 percent). However, nearly two-thirds (64 percent) felt that we won't reach the 95 percent threshold for 7 years or more. In fact, one in eight respondents say we'll never reach that important threshold.

Clearly, we'll be living in a world with both on-premises and cloud workloads for the foreseeable future.

Lesson 3: AWS Dominates, But The Marketshare Race Isn't Over

Amazon's AWS has been an amazing success. Analysts report that AWS enjoys 47 percent of the public cloud market today, with Microsoft Azure at 10 percent and Google Cloud Platform at 4 percent. Other companies, such as IBM Softlayer, make up the remainder with 2 percent or less each. That's a commanding lead, but will it hold going forward?

Industry influencers expect both Microsoft and Google to gain ground against AWS going forward. They forecast that by 2020 AWS will grow slightly to a 52 percent market share, with Microsoft growing to 21 percent and Google growing to 18 percent. Those are impressive gains in a short period of time, and point to a robustly competitive market for public cloud.

Reas Part 2: How to Prepare for the Future of the Cloud

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

3 Lessons About the Future of the Cloud

Steve Francis

Most companies are adopting a cloud strategy. In ServiceNow's Cloud Tipping Point Survey, half of all enterprises reported they are now "cloud-first," meaning that the next workload they deploy will go to the cloud instead of their data center.

It took 20 years from the time the term "cloud computing" was coined to reach this milestone. When will we be at a point where virtually all enterprise workloads are run in the cloud and how will that change things for IT?

To find out, we commissioned our own survey, Cloud Vision 2020: The Future of the Cloud. We started with a group of core influencers – people whose job it is to follow cloud computing. This group of 88 "cloud cognoscenti" included industry analysts, media, consultants and cloud vendors. We then followed that up with another survey fielded at the 2017 AWS re:Invent conference in Las Vegas where we received 195 additional responses from the people tasked with workload deployments in the real world.


The results were fascinating. I'll share three fundamental lessons we learned in the survey as well as some advice for going forward.

Lesson 1: The Reasons Why Enterprises Embrace Cloud Computing Are Changing

Why do enterprises use cloud today? The drivers of cloud computing today will sound very familiar: digital transformation, IT agility and the rise of the DevOps culture.

Those make perfect sense. Digital transformation aims to put the customer at the center of a company's automation strategy, and cloud is an excellent way to accomplish that. IT agility is much easier to achieve when someone else is responsible for your infrastructure, and you can focus on applications. The DevOps culture relies on cloud computing to achieve the speed and efficiency it was designed to deliver.

But by 2020 we expect those drivers to shift, revealing a new top-driver: artificial intelligence (AI)/machine learning. That wasn't what I expected, but it makes sense.

First, AI provides the ability to extract insight from the massive "data lakes" that enterprises are collecting about their application performance and behavior. Similarly, public cloud provides the scale to provide the massive compute resources AI needs.

But more than storage and compute, the public cloud is quickly becoming a hub for AI services that developers can integrate to build sophisticated AI applications. AWS, for example, has been busy adding Machine Learning-as-a-Service capabilities.

Lesson 2: It's Going to be a Hybrid World for the Foreseeable Future

ServiceNow's survey showed that half of all enterprises are now cloud-first, but that means half are not. And, even if an enterprise is cloud-first, it will still have many legacy workloads in an on-premises data center.

We asked survey respondents to forecast when they felt nearly all (95 percent) workloads would finally be in the cloud. Predictably, a few enthusiastic cloud supporters predicted this would happen within one year (6 percent) or two years (9 percent). However, nearly two-thirds (64 percent) felt that we won't reach the 95 percent threshold for 7 years or more. In fact, one in eight respondents say we'll never reach that important threshold.

Clearly, we'll be living in a world with both on-premises and cloud workloads for the foreseeable future.

Lesson 3: AWS Dominates, But The Marketshare Race Isn't Over

Amazon's AWS has been an amazing success. Analysts report that AWS enjoys 47 percent of the public cloud market today, with Microsoft Azure at 10 percent and Google Cloud Platform at 4 percent. Other companies, such as IBM Softlayer, make up the remainder with 2 percent or less each. That's a commanding lead, but will it hold going forward?

Industry influencers expect both Microsoft and Google to gain ground against AWS going forward. They forecast that by 2020 AWS will grow slightly to a 52 percent market share, with Microsoft growing to 21 percent and Google growing to 18 percent. Those are impressive gains in a short period of time, and point to a robustly competitive market for public cloud.

Reas Part 2: How to Prepare for the Future of the Cloud

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...