5 Common Cloud Migration Pitfalls and Future-Proofing Strategies

Vijay Verma
Persistent Systems

Of the $800 billion that enterprises plan to invest in cloud this year, $100 billion will be wasted due to poorly planned migration. While large enterprises aim to migrate over 80% of their applications to the cloud, only a third will end up realizing their cloud ambitions.

Cloud migration — a seemingly straightforward endeavor of shifting workloads and applications from on-premises data centers to cloud infrastructure — is, in reality, a highly strategic decision that involves leadership sponsorship, business justifications for moving to the cloud, and a clear understanding of expected value. Lack of this alignment can be the reigning cause of cost and budget overruns and why almost half of the migration efforts underway today will fail in the next three years.

The most common cloud migration pitfalls today are:

Lack of strategy and planning

Considering cloud migration an IT-only initiative is why it gets derailed. Running this in silos results in a lack of alignment on the expected value of cloud migration, which reflects in poorly planned execution timelines, a wide margin of error in estimating cost, effort and skills, delegating responsibilities, managing change, and weak governance frameworks.

Future-proofing strategy: Cloud migration initiatives should be steered by a center of excellence (CoE) comprising senior leadership, lines of business owners, technology leads, and operations teams. This sets the right expectations on the value to be harnessed from the cloud, which acts as the north star defining subsequent goals, execution phases, cost, effort, and skill requirements.

For example, if the goal is to innovate instead of reducing the total cost of IT, the CoE can decide which applications to consider for migration based on the business relevance and if they need to be refactored or rearchitected. Grounding such decisions in business value expectations sets the agenda for downstream activities where cross-functional teams collaborate and commit to an execution plan with pre-defined milestones.

Erroneous ROI calculations and underestimating costs

Cloud promises to reduce the total cost of ownership with on-demand consumption. However, enterprises often encounter the cloud cost paradox, witnessing an exponential cost increase once they migrate. This is because they treat the cloud as another data center and tend to overlook hidden costs such as network charges, data transfer fees, licensing costs, downtime, and bubble costs.

Future-proofing strategy: When allocating cloud budgets, perform a comprehensive cost-benefit analysis for each application cohort, considering the expected business value. The CoE can help factor in hidden variables and review financial projections after each milestone. While approaching the cloud from a cost angle seems lucrative, it proves myopic in the long run. By prioritizing business innovation, enterprises can generate five times the value they derive from the cloud than they would by simply reducing IT spending.

Not prioritizing cybersecurity and data security

Enterprises transitioning to the cloud tend to treat security as an afterthought that can be bolted in and are blindsided when they realize the cloud service provider does not take on the role. Securing cyber assets in the cloud is a different ball game than in an on-premises data center. The cloud's shared environment opens new vulnerabilities and data-residency laws that lead to compliance overburden and security incidents. Even so, data in transit is highly prone to attacks. Enterprises need to build observability and auditing capabilities that ensure their applications and data are secure as they move beyond company-controlled data centers.

Future-proofing strategy: Migration plans should include a robust security framework to guide migration activities. The CoE can help enterprises assess the extent of sensitive data and compliance obligations as they plan migration. It can also advise IT and business teams to implement measures and protocols to ensure data remains secure while transitioning to and running in the cloud. These protocols must be stress-tested and revised periodically to defend against evolving threats proactively.

Overlooking data egress and management

The challenge lies in migrating data or applications from one cloud service provider to another or back to an on-premises data center. Caught in vendor lock-in, enterprises must navigate a complex mesh of service provider contracts and exit fees that actively disincentivize their move to a multi-cloud strategy. Moreover, due to its vast volume, moving data between environments needs an explicit focus on integrity, accessibility, and availability.

Future-proofing strategy: To ensure their move to the cloud does not prove restrictive in the future, enterprises should include a comprehensive data management strategy that evolves with work requirements without triggering data quality issues. They should also include a data governance framework that factors in disaster recovery, sovereignty, security, and compliance with applicable laws. A cloud transformation partner with ties with hyperscalers can help enterprises broker an appropriate fee and approach for a multi-cloud or exit strategy.

Skillset gap and inadequate training

Before migrating workloads to the cloud, enterprises must assess application dependencies to identify impact areas and minimize unplanned downtime. Once in the cloud, the applications must integrate with cloud-native features, which could change functionalities and require users to adjust. Most enterprises — having spent decades building on-premises architecture, processes, or teams — are not equipped with the skills or change management plans that create a capability debt. Currently, only 35% of the required cloud skills exist in-house, whereas for enterprises to fully tap into their cloud potential, this share has to increase to 50%.

Future-proofing strategy: To ensure their cloud strategies stay on track, enterprises must aggressively invest in training programs that enable their staff to work with cloud-hosted applications. They also need to foster a continuous learning and development culture that incentivizes users to adopt change and pivot to new ways of working enabled by the cloud.

Conclusion: Turning Complexity into Competitive Advantage

Despite challenges, the cloud remains a cornerstone for business innovation, especially with the AI barrage. Enterprises can maximize the value of their cloud investments through a mindset change — it is a new environment. It requires a relook at the existing application stack and the technology landscape, necessitating change management to address behaviors entrenched in the pre-cloud era.

The simplest way forward is to involve employees, leadership, technology and business teams, and operations (including finance) early. This sets the stage for open communication and a more streamlined way to set expectations and delegate responsibilities. It makes everyone a stakeholder in cloud migration initiatives and empowers them to collaborate, leading to better alignment.

Vijay Verma is Chief Revenue Officer – Service Lines at Persistent Systems

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 ...

5 Common Cloud Migration Pitfalls and Future-Proofing Strategies

Vijay Verma
Persistent Systems

Of the $800 billion that enterprises plan to invest in cloud this year, $100 billion will be wasted due to poorly planned migration. While large enterprises aim to migrate over 80% of their applications to the cloud, only a third will end up realizing their cloud ambitions.

Cloud migration — a seemingly straightforward endeavor of shifting workloads and applications from on-premises data centers to cloud infrastructure — is, in reality, a highly strategic decision that involves leadership sponsorship, business justifications for moving to the cloud, and a clear understanding of expected value. Lack of this alignment can be the reigning cause of cost and budget overruns and why almost half of the migration efforts underway today will fail in the next three years.

The most common cloud migration pitfalls today are:

Lack of strategy and planning

Considering cloud migration an IT-only initiative is why it gets derailed. Running this in silos results in a lack of alignment on the expected value of cloud migration, which reflects in poorly planned execution timelines, a wide margin of error in estimating cost, effort and skills, delegating responsibilities, managing change, and weak governance frameworks.

Future-proofing strategy: Cloud migration initiatives should be steered by a center of excellence (CoE) comprising senior leadership, lines of business owners, technology leads, and operations teams. This sets the right expectations on the value to be harnessed from the cloud, which acts as the north star defining subsequent goals, execution phases, cost, effort, and skill requirements.

For example, if the goal is to innovate instead of reducing the total cost of IT, the CoE can decide which applications to consider for migration based on the business relevance and if they need to be refactored or rearchitected. Grounding such decisions in business value expectations sets the agenda for downstream activities where cross-functional teams collaborate and commit to an execution plan with pre-defined milestones.

Erroneous ROI calculations and underestimating costs

Cloud promises to reduce the total cost of ownership with on-demand consumption. However, enterprises often encounter the cloud cost paradox, witnessing an exponential cost increase once they migrate. This is because they treat the cloud as another data center and tend to overlook hidden costs such as network charges, data transfer fees, licensing costs, downtime, and bubble costs.

Future-proofing strategy: When allocating cloud budgets, perform a comprehensive cost-benefit analysis for each application cohort, considering the expected business value. The CoE can help factor in hidden variables and review financial projections after each milestone. While approaching the cloud from a cost angle seems lucrative, it proves myopic in the long run. By prioritizing business innovation, enterprises can generate five times the value they derive from the cloud than they would by simply reducing IT spending.

Not prioritizing cybersecurity and data security

Enterprises transitioning to the cloud tend to treat security as an afterthought that can be bolted in and are blindsided when they realize the cloud service provider does not take on the role. Securing cyber assets in the cloud is a different ball game than in an on-premises data center. The cloud's shared environment opens new vulnerabilities and data-residency laws that lead to compliance overburden and security incidents. Even so, data in transit is highly prone to attacks. Enterprises need to build observability and auditing capabilities that ensure their applications and data are secure as they move beyond company-controlled data centers.

Future-proofing strategy: Migration plans should include a robust security framework to guide migration activities. The CoE can help enterprises assess the extent of sensitive data and compliance obligations as they plan migration. It can also advise IT and business teams to implement measures and protocols to ensure data remains secure while transitioning to and running in the cloud. These protocols must be stress-tested and revised periodically to defend against evolving threats proactively.

Overlooking data egress and management

The challenge lies in migrating data or applications from one cloud service provider to another or back to an on-premises data center. Caught in vendor lock-in, enterprises must navigate a complex mesh of service provider contracts and exit fees that actively disincentivize their move to a multi-cloud strategy. Moreover, due to its vast volume, moving data between environments needs an explicit focus on integrity, accessibility, and availability.

Future-proofing strategy: To ensure their move to the cloud does not prove restrictive in the future, enterprises should include a comprehensive data management strategy that evolves with work requirements without triggering data quality issues. They should also include a data governance framework that factors in disaster recovery, sovereignty, security, and compliance with applicable laws. A cloud transformation partner with ties with hyperscalers can help enterprises broker an appropriate fee and approach for a multi-cloud or exit strategy.

Skillset gap and inadequate training

Before migrating workloads to the cloud, enterprises must assess application dependencies to identify impact areas and minimize unplanned downtime. Once in the cloud, the applications must integrate with cloud-native features, which could change functionalities and require users to adjust. Most enterprises — having spent decades building on-premises architecture, processes, or teams — are not equipped with the skills or change management plans that create a capability debt. Currently, only 35% of the required cloud skills exist in-house, whereas for enterprises to fully tap into their cloud potential, this share has to increase to 50%.

Future-proofing strategy: To ensure their cloud strategies stay on track, enterprises must aggressively invest in training programs that enable their staff to work with cloud-hosted applications. They also need to foster a continuous learning and development culture that incentivizes users to adopt change and pivot to new ways of working enabled by the cloud.

Conclusion: Turning Complexity into Competitive Advantage

Despite challenges, the cloud remains a cornerstone for business innovation, especially with the AI barrage. Enterprises can maximize the value of their cloud investments through a mindset change — it is a new environment. It requires a relook at the existing application stack and the technology landscape, necessitating change management to address behaviors entrenched in the pre-cloud era.

The simplest way forward is to involve employees, leadership, technology and business teams, and operations (including finance) early. This sets the stage for open communication and a more streamlined way to set expectations and delegate responsibilities. It makes everyone a stakeholder in cloud migration initiatives and empowers them to collaborate, leading to better alignment.

Vijay Verma is Chief Revenue Officer – Service Lines at Persistent Systems

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 ...