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Compare AI-assisted and manual migration on the same workloads, target architecture, success criteria, and staffing assumptions. AI assistance can speed up specific tasks, but it is not a migration strategy by itself—and the available quantified results are vendor-reported outcomes and a modeled scenario, not a neutral, universal head-to-head benchmark. Keep workload decisions, review, testing, and cutover controls explicit.

Separate the migration strategy from the execution method

A migration strategy describes what you plan to do with an application: move it with little change, make limited improvements, redesign it, replace it, retain it where it is, or retire it. AI-assisted or manual describes how people carry out particular tasks within that strategy. A team could use AI to help assess dependencies in a rehost, for example, while requiring engineers to review the findings and approve the plan.

AWS lists seven strategies: retire, retain, rehost, relocate, repurchase, replatform, and refactor or rearchitect. Microsoft’s Azure guidance uses a somewhat different set—rehost, replatform, refactor, rebuild, retire, and retain. Both emphasize choosing per workload, based on business drivers and constraints, rather than applying one method to an entire portfolio. See AWS’s migration-strategy guidance and Microsoft’s strategy descriptions.

Build a fair comparison baseline

Start with an inventory that is specific enough to compare like with like. AWS Prescriptive Guidance puts it plainly: “The Assess phase is built on the principle that you can’t effectively move what you do not measure.” Its migration strategy and readiness guidance treats readiness as a combination of business, people, governance, platform, security, and operations. AWS also recommends progressive portfolio assessment and reassessment as understanding changes; its application portfolio assessment guidance explains that approach.

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For each workload in both comparison groups, record the same baseline fields:

  • Business context: owner, business value, criticality, acceptable downtime, and the outcome the move is meant to achieve.
  • Technical context: application and infrastructure inventory, dependencies, versions, technical debt, reliability needs, and current maintenance burden.
  • Constraints: security and compliance obligations, data residency, licensing, specialized hardware, and change windows.
  • Migration plan: target platform and architecture, selected strategy, migration wave, cutover and rollback plan, and what counts as complete.
  • People and operations: available skills, partner involvement, pipeline maturity, support ownership, and the intended steady-state operating model.
  • Cost and validation: source and target costs, migration and tool costs, staff effort, test criteria, and post-move remediation expectations.

Microsoft’s modernization preparation guidance also calls out cloud-service skills, DevOps and CI/CD maturity, technical debt, outdated technology, maintenance load, reliability, and business value as readiness considerations.

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Compare outcomes across the same dimensions

Keep scope, target design, definition of completion, staffing assumptions, and validation standards consistent. Otherwise, a faster result may reflect a simpler workload or less testing rather than the use of AI. Count setup, review, correction, and remediation effort alongside time saved on assisted tasks. The dimensions below are a practical comparison framework, not measured findings from the cited sources.

Dimension What to measure in both approaches Questions to ask
Time Assessment and planning effort; execution time; cutover duration; elapsed time to the agreed target state. Did assistance shorten the whole path, or only one task? Were review and rework included?
Total cost Tools, migration work, staff and partner time, training, licensing, parallel running, and expected operating costs. Does the comparison include the cost of running old and new systems during transition, and the cost of operating the result?
Risk and control Dependency accuracy, data handling, compliance checks, approval points, inspectability of plans and generated code, and rollback readiness. Can the team see what changed, verify it, and stop or reverse a risky step?
Quality and validation Functional and performance test coverage, security review, observability, acceptance results, and defects or remediation found. Were both approaches held to the same tests and acceptance criteria?
Operational fit Required skills, maintainability, deployment pipeline, support ownership, reliability, and ongoing management effort. Can the team operate and support the target environment after the migration team leaves?
Business outcome Disruption, reliability, agility, and progress toward the workload’s stated business goal. Did the chosen changes solve a real workload problem, or add scope without a clear benefit?

A cloud-bill comparison alone is not a total-cost comparison. Include migration labor and tools, partner and training costs, any dual-running period, licensing, and expected ongoing operations. AWS’s 2026 article discusses total cost of change as well as cloud costs in its illustrative scenario; its figures and assumptions are described below.

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Choose modernization depth workload by workload

The more a migration changes an application, the more important it is to justify the change against its business value, risk, skills, and schedule. AWS describes refactoring as the most complex and costly strategy for large migrations and generally recommends modernizing after migration where feasible. Microsoft likewise advises selecting a strategy according to the application’s needs rather than treating modernization as a single all-or-nothing choice.

Strategy When it may fit What to account for
Rehost Speed and low application change are priorities. Minimal change can leave technical or platform problems in place. AWS and Microsoft both describe rehosting as moving with limited or no application modification.
Replatform A managed service or modest platform change could reduce infrastructure management or improve operations without a full redesign. Allow for packaging or code changes, new operating practices, and validation of the changed components.
Refactor or rearchitect Technical debt or architectural limits block a material business outcome, and the expected value justifies redesign. Plan for greater complexity, cost, time, and skills needs. Consider moving first and modernizing later when that reduces migration risk.
Retain Moving is premature, constrained, high risk, uneconomic, or unnecessary. AWS lists data residency, dependencies, specialized hardware, and high risk among reasons a workload may stay in place.
Retire The workload no longer has continuing business value. Confirm with the workload owner that functionality and dependencies are no longer needed before decommissioning.
Other named options AWS also includes relocate and repurchase; Microsoft’s Azure list includes rebuild. Terminology and categorization differ across providers. Use the linked provider guidance to define the chosen approach for the target platform.

Prioritize with both business value and technical risk in view. Microsoft’s preparation guidance gives an example priority matrix that places high-value, high-risk workloads at the top and treats low-value, high-risk cases individually. Use a matrix to screen candidates, then confirm the result with workload owners.

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What AI assistance can cover—and what still needs review

AWS’s post “Accelerating Cloud Migration with AWS Transform and Generative AI,” published March 22, 2026, describes AWS Transform for VMware migration. AWS says the service can help discover VMware workloads and dependencies, develop plans and migration waves, convert network configurations, generate infrastructure-as-code, and support server conversion, replication, testing, and cutover. These are AWS product descriptions, not evidence that every step is automated or suitable for every workload. Check current supported workloads and regional availability before relying on a capability for a project.

Rather than treating “AI versus manual” as one switch, use those activities as separate measurement points:

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  • How much effort did discovery take, and how complete and accurate was the dependency data?
  • How long did planning and wave design take, including review and corrections?
  • How much network conversion work was accepted as generated, and what needed adjustment?
  • How much generated infrastructure-as-code passed review and testing without changes?
  • What testing, cutover support, and post-migration remediation were required?

Keep human approval at consequential points. Inspect plans and generated code, validate dependencies, apply the same tests used in the manual path, and retain a credible rollback plan. The cited sources do not establish a general AI-versus-manual defect rate or prove that human review can be removed.

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Read AWS’s reported results in context

The numerical evidence in the AWS post is useful as context, but it does not establish a universal advantage. Separate a customer case study, a partner statement, and a modeled scenario from a controlled comparison.

Figure What the source says How to interpret it
34% faster migration; 35% lower five-year total cost of ownership; 30% increase in team effectiveness; 60% of wave planning automated AWS reports these results for Vector Limited’s AWS Transform VMware migration, conducted with AWS Premier Partner Slalom, in its March 22, 2026 post. Vendor-reported outcomes for one customer engagement, not a typical result, guarantee, or independent head-to-head benchmark.
At least 50% lower VM migration time AWS quotes Accenture Managing Director Neil Redmond: “AWS Transform for VMware can reduce VM migration time to AWS by at least 50%. We’re now integrating AWS Transform into our tooling to enable even faster migrations.” A partner statement published by AWS, not an independent benchmark.
$1,000–$3,000 per VM; $50–$150 per TB of storage; 18–48 months AWS’s 2026 post cites these as Gartner 2024 benchmark ranges for large-scale migrations of 2,000+ VMs or 100+ hosts. These are ranges attributed to Gartner as cited by AWS; verify against the original Gartner report before using them as a project estimate.
30–40% potential reduction in cloud migration time AWS attributes this estimate to McKinsey but does not state a year for it in the post. An attributed estimate, not a result established for every project. AWS uses a 35% improvement assumption in its model.
33 months and $7.68 million versus 22 months and $4.82 million total cost of change AWS’s 2026 illustrative model compares a traditional migration scenario with an AWS Transform scenario. It assumes an estate of 1,800 production servers, 1,200 non-production servers, and 660 TB; its 35% improvement assumption is the midpoint of the McKinsey range AWS cites. A modeled scenario using assumptions and cited benchmarks, not an observed controlled comparison. The figures are not transferable project forecasts.
Five-year ROI of 22% versus 81% AWS reports these modeled ROI figures for the traditional migration and AWS Transform scenarios, respectively, against remaining on premises. They depend on the model’s inputs and assumptions; they are not general ROI predictions.

The AWS post therefore supports asking whether an AI-assisted VMware workflow could help with a specific task. It does not support applying its percentages to unrelated platforms, workloads, regions, or migration strategies.

Run a pilot that can inform a decision

A small, controlled pilot can reveal where assistance saves effort and where review or rework offsets it. The following is a practical comparison method, not a result claimed by the cited sources.

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  1. Select representative workloads. Include workloads that reflect the dependencies, risk, compliance needs, and migration strategies likely to matter in the broader portfolio. Avoid choosing only an unusually simple case.
  2. Freeze the comparison conditions. Define identical scope, target architecture, completion criteria, test requirements, staffing assumptions, and cutover constraints for assisted and manual workflows.
  3. Record effort by task. Track discovery, planning, conversion, code review, testing, approvals, cutover, correction, and remediation separately. Include tool setup and training where relevant.
  4. Use the same acceptance checks. Apply equal functional, performance, security, and operational validation to both paths; record failures and remediation rather than counting only successful steps.
  5. Compare the finished operating result. Assess total effort and cost, elapsed time, disruption, reliability, maintainability, and whether the business outcome was met.
  6. Expand only where evidence supports it. Use pilot results to identify which tasks and workload types benefit, which still need manual handling, and what controls should be required in later waves.

For VMware estates specifically, the AWS material identifies AWS Transform as a product to evaluate, but verify its current support and regional availability for the intended deployment. For a broader portfolio, AWS’s cloud migration strategy overview and Microsoft’s App Modernization Guidance for Azure provide additional provider-specific context.