Cloud Migration Delays Are Putting Businesses at Risk

How to Build a Strategy for Long-Term Success
Jonathan LaCour
Mission

Over the past 18 months, AI has been improving at a breakneck pace, and businesses globally are itching to take advantage of the most transformational new technology in decades. But, the harsh reality is that not all businesses are running on modern cloud infrastructure. Critically, their data estate requires significant evolution to even begin taking advantage of AI. They’re starting the race from the parking lot.

The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before.

But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck. They're encumbered by legacy, and the internal friction around moving to public cloud is real. Security concerns, compliance questions, technical debt, cost control anxiety — these aren't trivial objections. They're legitimate concerns that slow everything down while the opportunity cost keeps growing.

What's Actually Holding Companies Back

The Flexera 2025 State of the Cloud Report nails the two biggest blockers: 77% of organizations cite security as a top cloud challenge, and 84% struggle with cost control. These aren't just survey numbers — they're the reasons why cloud initiatives stall in committee meetings and budget reviews.

If you're a CTO or infrastructure leader, you're being asked to move faster on AI while simultaneously being told to lock down security and control costs. That's a tough position. And when you're dealing with legacy systems that have been running business-critical workloads for years, the risk of a botched migration feels very real.

The problem is that waiting doesn't make it easier. Technical debt compounds. The gap between what your business needs and what your infrastructure can deliver just keeps widening. And critically, you're missing the window to build AI capabilities while your competitors are already experimenting and learning.

AI as the Accelerator

Here's some good news: the same AI technology creating urgency can also help solve the migration challenge. Business Insider recently covered how organizations are using AI tools to actually accelerate and de-risk migrations — mapping dependencies, estimating costs, identifying risks, and automating steps that used to require weeks of manual analysis.

This matters because it addresses both sides of the equation. You can move faster (which you need to do to unlock AI capabilities) while also reducing risk (which addresses those security and governance concerns that are keeping stakeholders up at night). AI-assisted migrations can catch configuration issues, predict cost impacts, and identify security gaps before they become problems.

But — and this is important — tools alone don't solve organizational readiness issues. You still need clear objectives, cross-functional alignment, and a realistic understanding of what you're trying to achieve. The migrations that fail usually fail because of people and process issues, not technology.

Migration Is Just Step One

The other thing I want to emphasize: getting to the cloud isn't the finish line. It's the starting line.

I see companies treat cloud migration like a project with a beginning, middle, and end. They move workloads, declare victory, and move on. Then six months later, they're shocked by their cloud bill or discovering that they're not actually more agile than before.

Cloud requires continuous optimization. You need ongoing governance, regular cost reviews, performance tuning, security monitoring, and constant alignment with best practices. The cloud providers are releasing new services and capabilities constantly. The companies that win are the ones that treat cloud as a continuous practice, not a one-time project.

This is where working with an expert partner can make a huge difference, especially if your organization is in the middle of this internal shift to public cloud. A good partner doesn't just help you migrate — they help you operationalize cloud management so you're constantly optimizing, governing, and evolving your cloud estate as your business needs change.

The Bottom Line

If your organization isn't fully committed to public cloud yet, I understand the hesitancy. But AI isn't waiting for anyone. Companies that can iterate quickly on AI capabilities are going to have a significant advantage, and that requires modern cloud infrastructure.

The question isn't whether to migrate. It's whether you have the right strategy, the right approach to risk management, and the right support to do it well. Because done wrong, cloud migration is expensive and disruptive. Done right, it's the foundation for everything you're going to need to build over the next decade.

The companies that move with discipline and a clear-eyed focus on continuous improvement will be positioned to capitalize on AI and whatever comes next. The ones that keep waiting are not reducing risk — they're accumulating it.

Jonathan LaCour is CTO of Mission

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Cloud Migration Delays Are Putting Businesses at Risk

How to Build a Strategy for Long-Term Success
Jonathan LaCour
Mission

Over the past 18 months, AI has been improving at a breakneck pace, and businesses globally are itching to take advantage of the most transformational new technology in decades. But, the harsh reality is that not all businesses are running on modern cloud infrastructure. Critically, their data estate requires significant evolution to even begin taking advantage of AI. They’re starting the race from the parking lot.

The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before.

But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck. They're encumbered by legacy, and the internal friction around moving to public cloud is real. Security concerns, compliance questions, technical debt, cost control anxiety — these aren't trivial objections. They're legitimate concerns that slow everything down while the opportunity cost keeps growing.

What's Actually Holding Companies Back

The Flexera 2025 State of the Cloud Report nails the two biggest blockers: 77% of organizations cite security as a top cloud challenge, and 84% struggle with cost control. These aren't just survey numbers — they're the reasons why cloud initiatives stall in committee meetings and budget reviews.

If you're a CTO or infrastructure leader, you're being asked to move faster on AI while simultaneously being told to lock down security and control costs. That's a tough position. And when you're dealing with legacy systems that have been running business-critical workloads for years, the risk of a botched migration feels very real.

The problem is that waiting doesn't make it easier. Technical debt compounds. The gap between what your business needs and what your infrastructure can deliver just keeps widening. And critically, you're missing the window to build AI capabilities while your competitors are already experimenting and learning.

AI as the Accelerator

Here's some good news: the same AI technology creating urgency can also help solve the migration challenge. Business Insider recently covered how organizations are using AI tools to actually accelerate and de-risk migrations — mapping dependencies, estimating costs, identifying risks, and automating steps that used to require weeks of manual analysis.

This matters because it addresses both sides of the equation. You can move faster (which you need to do to unlock AI capabilities) while also reducing risk (which addresses those security and governance concerns that are keeping stakeholders up at night). AI-assisted migrations can catch configuration issues, predict cost impacts, and identify security gaps before they become problems.

But — and this is important — tools alone don't solve organizational readiness issues. You still need clear objectives, cross-functional alignment, and a realistic understanding of what you're trying to achieve. The migrations that fail usually fail because of people and process issues, not technology.

Migration Is Just Step One

The other thing I want to emphasize: getting to the cloud isn't the finish line. It's the starting line.

I see companies treat cloud migration like a project with a beginning, middle, and end. They move workloads, declare victory, and move on. Then six months later, they're shocked by their cloud bill or discovering that they're not actually more agile than before.

Cloud requires continuous optimization. You need ongoing governance, regular cost reviews, performance tuning, security monitoring, and constant alignment with best practices. The cloud providers are releasing new services and capabilities constantly. The companies that win are the ones that treat cloud as a continuous practice, not a one-time project.

This is where working with an expert partner can make a huge difference, especially if your organization is in the middle of this internal shift to public cloud. A good partner doesn't just help you migrate — they help you operationalize cloud management so you're constantly optimizing, governing, and evolving your cloud estate as your business needs change.

The Bottom Line

If your organization isn't fully committed to public cloud yet, I understand the hesitancy. But AI isn't waiting for anyone. Companies that can iterate quickly on AI capabilities are going to have a significant advantage, and that requires modern cloud infrastructure.

The question isn't whether to migrate. It's whether you have the right strategy, the right approach to risk management, and the right support to do it well. Because done wrong, cloud migration is expensive and disruptive. Done right, it's the foundation for everything you're going to need to build over the next decade.

The companies that move with discipline and a clear-eyed focus on continuous improvement will be positioned to capitalize on AI and whatever comes next. The ones that keep waiting are not reducing risk — they're accumulating it.

Jonathan LaCour is CTO of Mission

Related Links

Hot Topics

The Latest

AI is Moving Fast, and Enterprise File Data Is Not Ready

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Move Fast Without the Usual Quality Penalty

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

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I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

AI is Breaking Enterprise Log Management

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

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For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

Production Inference Shifts Decisively to Private Cloud

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

Why DevOps Teams Build GenAI Tooling While Most Organizations Still Rely on Manual Monitoring

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Why Cloud Reliability Comes Down to People and How to Improve It

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

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77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

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In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...