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How to Prepare for the Future of the Cloud

Steve Francis

How do enterprises prepare for the future that our Cloud Vision 2020 survey forecasts? I see three immediate takeaways to focus on:

Start with: 3 Lessons About the Future of the Cloud

1. Visibility

You cannot manage what you cannot see. Yet, with your workloads split among on-premises, public, private and hybrid clouds, visibility is difficult to achieve.

Are your workloads available? Are they performing properly? Are you about to run out of a specific resource, such as storage? These are the kind of questions you need to answer to manage your computing fabric.

You need monitoring tools to do this, of course. If you're managing a diverse hybrid infrastructure, you need a monitoring partner that can handle the entire range of platforms — on-premises, public, private and hybrid cloud.

2. Build Your Staff's Skillsets

Think of the journey you are taking. Five years ago, most of your workloads were on-premises. Five years from now most will be in the cloud. That changes everything — your infrastructure, your management tools and how you develop applications.

While you are busy building this exciting new future, make sure you set aside time and money to train your staff for the key skills they'll need to help you get there. This includes how best to deploy and manage workloads in public clouds such as AWS, how to implement a successful DevOps culture, and how to deploy and manage the monitoring tools you'll need to keep everything running smoothly.

3. Choose Your Public Cloud Vendor Carefully

AWS has a commanding lead, but it is far from the only game in town. Start by taking stock of what you need from a cloud partner. Are you looking for public cloud service to provide a full development stack? Or is cloud just a place to stage data? Do a thorough inventory of what you need from a public cloud vendor and compare the major options. Amazon, Microsoft and Google are quite different, and there is no one-size-fits-all in public cloud. Investigate whether you want to jump fully into a cloud provider and take advantage of all their services (database-as-a-service, queuing systems, etc.), or try to remain cloud neutral, and use tools like Kubernetes to manage workloads — possibly even across multiple clouds.

Summary

Enterprises are in the middle of a massive transition, akin to a fourth industrial revolution. It's worthwhile to consider where you'll be in the near future and make plans to ensure you're prepared when you get there.

Hot Topics

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In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

How to Prepare for the Future of the Cloud

Steve Francis

How do enterprises prepare for the future that our Cloud Vision 2020 survey forecasts? I see three immediate takeaways to focus on:

Start with: 3 Lessons About the Future of the Cloud

1. Visibility

You cannot manage what you cannot see. Yet, with your workloads split among on-premises, public, private and hybrid clouds, visibility is difficult to achieve.

Are your workloads available? Are they performing properly? Are you about to run out of a specific resource, such as storage? These are the kind of questions you need to answer to manage your computing fabric.

You need monitoring tools to do this, of course. If you're managing a diverse hybrid infrastructure, you need a monitoring partner that can handle the entire range of platforms — on-premises, public, private and hybrid cloud.

2. Build Your Staff's Skillsets

Think of the journey you are taking. Five years ago, most of your workloads were on-premises. Five years from now most will be in the cloud. That changes everything — your infrastructure, your management tools and how you develop applications.

While you are busy building this exciting new future, make sure you set aside time and money to train your staff for the key skills they'll need to help you get there. This includes how best to deploy and manage workloads in public clouds such as AWS, how to implement a successful DevOps culture, and how to deploy and manage the monitoring tools you'll need to keep everything running smoothly.

3. Choose Your Public Cloud Vendor Carefully

AWS has a commanding lead, but it is far from the only game in town. Start by taking stock of what you need from a cloud partner. Are you looking for public cloud service to provide a full development stack? Or is cloud just a place to stage data? Do a thorough inventory of what you need from a public cloud vendor and compare the major options. Amazon, Microsoft and Google are quite different, and there is no one-size-fits-all in public cloud. Investigate whether you want to jump fully into a cloud provider and take advantage of all their services (database-as-a-service, queuing systems, etc.), or try to remain cloud neutral, and use tools like Kubernetes to manage workloads — possibly even across multiple clouds.

Summary

Enterprises are in the middle of a massive transition, akin to a fourth industrial revolution. It's worthwhile to consider where you'll be in the near future and make plans to ensure you're prepared when you get there.

Hot Topics

The Latest

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...