Enterprises face sustained pressure from rising cloud costs, VMware pricing changes, operational toil, and uneven modernization maturity. As cloud programs scale, these pressures compound rather than resolve themselves. Infrastructure teams must shift from one time migration projects to a continuous modernization model enabled by AI. This article outlines the industry context, the challenges blocking progress, and how AI driven transformation frameworks address cost, technical debt, resilience, and hybrid multi cloud complexity.
At OpSky, we see this shift firsthand. Many enterprises are no longer struggling with how to move workloads, but with how to operate, optimize, and modernize them after migration.
1. The Hybrid Multi Cloud Reset
A clear market reset is underway as enterprises reassess the true cost, complexity, and readiness of large scale cloud programs.
Public cloud adoption remains high but uneven. Flexera reports that 89 percent of enterprises use multiple public clouds, and 70 percent operate hybrid architectures combining on prem, private cloud, and hyperscaler services. Despite this adoption, most organizations struggle to achieve expected ROI. Bain reports that more than 70 percent of enterprises in regulated industries have slowed or paused cloud programs because cost, security, and modernization challenges proved harder than planned.
Cloud spend continues to rise faster than budgets. Average enterprise cloud spend now exceeds twelve million dollars per year, up roughly 35 to 40 percent since 2021. This growth often occurs without a corresponding increase in delivered business value. The rapid growth of GenAI further accelerates GPU consumption, storage expansion, and cross region network traffic. As a result, cloud bills grow faster than the workloads or outcomes they support.
Modernization gaps limit return on cloud investment. Bain finds that financial services companies expect 20 to 30 percent savings from cloud but typically achieve only 5 to 10 percent without modernization. Kearney estimates that 30 to 40 percent of cloud spend is avoidable waste, driven by poor workload placement, idle resources, and misconfigured storage or instances. These inefficiencies are usually locked in early during migration decisions.
Hybrid cloud is no longer a transitional state, but an operating model. Enterprises retain significant on prem infrastructure due to latency needs, data residency rules, licensing constraints, or application complexity. The result is a durable hybrid footprint that requires new operational models rather than a pure migration mindset.
VMware pricing pressures add urgency to cloud and modernization decisions. Post acquisition pricing changes have driven up to eight times higher license and support costs, accelerating timelines for enterprises to evaluate cloud, containerization, or alternative virtualization platforms. Large VMware migrations often require thousands of dollars per VM, multiple full time engineers, and multi year timelines, making accuracy in planning and automation essential rather than optional.
Together, these trends define a hybrid multi cloud environment where cost pressure, modernization requirements, and hybrid operations demand AI assisted planning, execution, and long term management.
2. Industry Context and Modernization Drivers
While the pressures are universal, their expression varies by industry.
- Financial services organizations face compliance and sovereignty constraints that slow modernization, yet cloud adoption still grows at roughly 25 percent year over year.
- Healthcare environments continue to run a majority of workloads on legacy EHR platforms, while cloud native analytics can reduce infrastructure cost by 20 to 30 percent.
- Manufacturing organizations adopt hybrid models due to OT and IT integration and latency requirements, with more than half planning container adoption by 2026.
- Public sector agencies use hybrid architectures to reduce cost while meeting regulatory and sovereignty mandates.
- Mid market enterprises rely heavily on automation and managed services due to limited staffing.
Across all segments, modernization success depends less on the target cloud and more on assessment quality and execution discipline.
3. Key Challenges with Migration
Enterprises consistently encounter the same structural challenges during migration.
- Unpredictable costs driven by storage growth, cross region traffic, and egress.
- Application complexity caused by incomplete dependency mapping and shared services.
- Skills gaps across VMware, AWS, Azure, and Google Cloud environments.
- Legacy databases and middleware that resist modernization.
- Governance drift across identity, segmentation, and compliance controls.
- Operational toil that consumes 40 to 60 percent of engineering time.
The consistent failure point across these challenges is not execution, but assessment. Enterprises lack a reliable, data driven understanding of workload readiness, dependency risk, and post migration operating cost before migration begins.
4. Beyond Lift and Shift, AI Driven Transformation and Management
Forced lift and shift migration does not deliver modernization. In many cases, it accelerates cost growth and operational fragility. The following four domains describe the shift from manual migration and fragmented operations to AI driven transformation and continuous management.
A. AI Enhanced Migration
Discovery blind spots make early planning unreliable. Traditional tools miss cross application dependencies, shared services, and data flows, causing incorrect grouping and sequencing.
Technical debt carries forward during lift and shift. Oversized instances, outdated operating systems, legacy storage tiers, and networking patterns move unchanged into cloud environments, creating immediate cost and stability issues.
Cutovers strain teams due to heavy manual coordination across planning, scheduling, rollback preparation, and validation. Without predictive insight, teams take conservative and slow approaches.
There is no pre cutover simulation to model cloud cost, latency impact, or identity conflicts, so issues are discovered only after migration.
Tooling remains disconnected, forcing engineers to manually reconcile CMDB data, discovery outputs, migration plans, and observability dashboards.
AI driven migration improves outcomes by correlating runtime behavior, traffic patterns, and operational telemetry to reduce false assumptions early. It enables automated wave design, predictive simulation, post migration optimization, and safer cutover orchestration.
B. Cloud Native Modernization
Modernization often stalls due to legacy databases, virtualization centric architectures, and deep application dependencies. Network and security mismatches further slow progress when on prem constructs do not map cleanly to cloud native equivalents.
AI accelerates modernization through suitability scoring, dependency mapping, automated container blueprints, security rule translation, and prioritized modernization pathways ranked by ROI and risk.
C. Cost Avoidance and Optimization
Cloud costs escalate due to fragmented visibility, inefficient commitments, and structural cost drag carried over from on prem environments. Governance processes remain manual and reactive.
AI driven optimization improves outcomes through predictive forecasting, automated modernization levers, telemetry informed rightsizing, anomaly detection, and continuous waste cleanup.
D. Hybrid Multi Cloud Operations
Hybrid operations are difficult due to fragmented tooling, skills shortages, configuration drift, and manual governance. Teams lack a unified view correlating performance, cost, and security.
AI enables unified cross cloud correlation, automated remediation, policy as code enforcement, natural language operations, and intelligent workload placement across environments.
Conclusion
Enterprises are shifting from reactive, project based migration to a continuous modernization operating model. AI accelerates each stage of the journey by improving cost efficiency, reducing technical debt, strengthening resilience, and simplifying hybrid operations.
Migration is the entry point, not the destination. The quality of assessment and planning determines whether cloud becomes a source of agility or a long term cost burden.
In the next post, we will focus on how migration assessments must evolve, and why modern, AI driven assessments are essential for predictable cost, lower risk, and sustained modernization outcomes.
Sources
Bain and Company, Cloud Value Realization Study 2024
Flexera, State of the Cloud Report 2025
Kearney, Cloud Optimization and Modernization Benchmark 2024
Gartner, Public Cloud Spending and Modernization Trends 2025
Industry analyses on VMware pricing changes 2024 to 2025
