Build an executive-ready migration business case with AWS Transform Assessment

Organisations often spend weeks consolidating infrastructure inventories, validating assumptions, estimating target costs, and converting technical findings into a business case. Scattered data, incomplete utilization records, unclear licensing positions, and disconnected financial models can delay decisions and reduce confidence in projected savings.

AWS Transform Assessment is an agentic, chat-based assessment experience that analyses IT environments and turns inventory data into right-sizing recommendations, TCO analyses, business-value insights, migration scenarios, and executive-ready deliverables. Teams can investigate results, refine assumptions, and compare options through natural-language conversation instead of repeatedly rebuilding spreadsheets.

In this walkthrough, you use a VMware RVTools export as an example input. You will upload and validate the inventory, define scope and financial assumptions, run and compare assessment scenarios, then generate outputs that support migration decisions. VMware demonstrates the workflow, but the assessment capability is designed for broader infrastructure assessment use cases and multiple supported data sources.

Prerequisites

• Access to an AWS Transform workspace and a migration job with the appropriate permissions.
• A recent RVTools export from the VMware environment. Use either the Excel workbook or a ZIP archive containing the relevant CSV files.
• A basic understanding of the source VMware inventory and the contacts who can clarify missing or unexpected records.
• An approved process for handling infrastructure metadata, including host names, IP addresses, and other potentially sensitive details.

Step 1

Create a current RVTools export

Run RVTools against the vCenter scope that represents the workloads you intend to assess. Export the results in a supported format, then record when and how the file was collected. A current, well-scoped export reduces rework and makes later conversations about inventory completeness more productive.

Before upload, perform a quick quality check:

• Confirm that the export opens successfully and contains virtual machine records.
• Check that the collection scope includes the expected vCenter instances, clusters, hosts, networks, and datastores.
• Remove accidental duplicate exports from the upload package. AWS Transform can de-duplicate records, but clean inputs simplify review.
• Confirm that powered-off or retired systems are handled according to your assessment policy. Do not assume every discovered machine should migrate.
• Store the original export in a controlled project location so that the source record is traceable.
• For the purpose of this blog walkthrough, download the sample RVTools file rvtools_500_servers.xlsx from this link. This file contains mixed Windows and Linux servers, including SQL Server.

Step 2

Create an AWS Transform Assessment job

Sign in to the AWS Transform console and click Service management from the left pane.

Copy the Web Application URL, open a new browser tab and paste the URL to log in to the AWS Transform Web Application. Once you log in, you will need to create a new workspace to get started. A workspace serves as a permissions boundary, containing other resources such as connectors and jobs. First, select or type Assess Infrastructure in the chat window.

Click Create a workspace for assessment.

If required, expand the sidebar menu on the left. Click on the workspace you just created to enter it.

Click the Create Job button.

Click Migration on the right side of the screen.

Once you click on Migration, you will see some Migration job options. Click on Migration Assessment.

This will start the job with a Running status indication on the top right of the screen. We can now use this job to ingest server inventory data and create an assessment.

Step 3

Upload the RVTools dataset

Upload the RVTools Excel file, or a ZIP file containing the supported CSV exports. Supplementary discovery data should be packaged with the primary server data where applicable. AWS Transform automatically detects the format and structure, parses the file, extracts inventory entities, validates the data, and prepares a summary for review.

Paste the following prompt into the chat to upload the file and kick off the full analysis:

Keep the browser session available while the upload and processing steps complete. Processing time varies with the file size and environment complexity. If the service reports an unsupported structure or unreadable file, return to the original export rather than manually changing column names without understanding the expected schema.

Step 4

Review inventory readiness and define scope

After processing completes, review the discovery results and inventory readiness summary by clicking Review Summary in the chat. A pane slides in from the right showing two grouped views:

• Insights — top-line takeaways the agent surfaced from the ingestion (counts, distributions, flags worth your attention)
• Inventory Summary — the structured per-server breakdown the assessment will run against

This pane is your first checkpoint. Skim it before moving on — anything that looks off here is much cheaper to fix now than after the assessment runs. A successful upload means that AWS Transform could process the file; it does not automatically mean the dataset is complete enough for a defensible financial model. Confirm the in-scope population, exclude retired or irrelevant systems, and adjust inventory groupings where needed before configuring the assessment scenario.

Close the Review Summary pane (top-right corner of the slide-out). Back in the chat, scroll down to find the overall assessment summary the agent posted inline — this is the consolidated, scrollable view you’ll come back to throughout the walkthrough.

Review the following areas:

• Inventory totals: compare discovered servers and virtual machines with the source team’s expected counts.
• Operating systems: identify unknown, unsupported, or obsolete versions that need remediation or a separate disposition.
• Compute and storage attributes: look for missing CPU, memory, disk, or utilization fields that could affect sizing and cost analysis.
• Network information: confirm that available network metadata is appropriate for the next planning stage.
• Duplicates and validation findings: understand what AWS Transform merged, rejected, or flagged, and whether corrective data is required.

Step 5

Configure the assessment scenario

Configure the pricing and infrastructure assumptions that shape the assessment. Depending on the analysis, these can include target AWS services and Regions, purchasing options, utilization assumptions, growth, migration timing, licensing approaches, and other financial parameters. Record the rationale for material assumptions so reviewers can understand how the model was constructed.

Use the conversational interface to verify scope and expose exceptions. For example:

• How many Windows and Linux servers are included in this scenario?
• Which operating systems or versions are unknown?
• Which records are missing CPU, memory, storage, or network attributes?
• Are there duplicate server records across the uploaded files?
• Which systems appear powered off, inactive, or suitable for exclusion?

Validate important findings and assumptions with infrastructure, application, finance, procurement, and licensing stakeholders. Agentic analysis accelerates the assessment, but the business case should remain grounded in customer context, commercial agreements, architecture standards, security requirements, and operational ownership.

Step 6

Run, refine, and compare assessment scenarios

Run the initial assessment to generate right-sizing recommendations, cost estimates, business-value insights, and migration recommendations by typing Proceed with defaults in the chat.

Review the results through chat and refine the model by changing assumptions or scope. Create additional scenarios to compare options side by side, such as alternative Regions, purchasing models, licensing approaches, target architectures, or migration timelines. If the readiness review identifies material inventory issues, correct them at the source where possible and upload updated data to the same assessment job.

Do not wait for a theoretically perfect dataset before beginning useful analysis. Define a minimum evidence threshold and clearly identify assumptions, exclusions, and confidence levels. Early scenarios can support directional discussions, while funding approval and migration execution require progressively stronger technical, utilization, licensing, and business data.

Step 7

Generate executive-ready deliverables and next actions

After reviewing and refining the preferred scenario, generate and download assessment deliverables such as an executive presentation and detailed report shown below.

Artifact	Format	Use for
Business Case Presentation	PPTX	Executive readout
Analysis Workbook	XLSX	Finance/procurement
Business Case Report	PDF	Customer-facing narrative

Use these outputs to communicate projected costs, potential savings, right-sizing opportunities, business value, sustainability impact, assumptions, and recommended next steps. The assessment can then inform deeper migration planning, application grouping, wave planning, architecture validation, and execution activities.

RVTools is a useful VMware example, but a decision-ready assessment should also incorporate business criticality, application ownership, dependencies, compliance constraints, maintenance windows, licensing considerations, and modernization objectives.

Troubleshooting common issues

Issue	Recommended action
File is not recognized	Regenerate the export in a supported RVTools Excel format or package the supported CSV files in a ZIP archive. Avoid renaming extensions or altering headers without a documented reason.
Inventory count is lower than expected	Check vCenter scope, collection permissions, filters, powered-off systems, and whether multiple environments require separate exports.
Key attributes are missing	Correct the source inventory where possible, collect a new export, or enrich the job with additional discovery sources.
Sensitive identifiers cannot be shared	Apply an approved anonymization process that preserves analytical fields and maintain a controlled mapping for authorized de-anonymization.
Duplicate or stale records appear	Review collection dates and source overlap. Remove obsolete uploads where appropriate, then re-upload current data.

Summary and next steps

In this walkthrough, you used a VMware RVTools export as an example input for AWS Transform Assessment. You prepared and uploaded inventory data, reviewed readiness and scope, investigated gaps, configured assessment assumptions, ran and compared scenarios, and prepared executive-ready outputs. This approach helps teams turn scattered infrastructure evidence into transparent TCO analyses, business cases, and actionable migration recommendations.

Try AWS Transform for free to explore how an agentic, chat-based assessment experience can accelerate analysis, improve financial transparency, and help stakeholders make confident migration decisions. Review the current AWS Transform pricing and service terms before use, because charges may apply to other AWS services or resources used during assessment, testing, or migration.

For hands-on enablement across APAC, reach out to Tech Data APAC. Our AWS team can help partners and customers prepare inventory data, facilitate assessment and TCO workshops, validate assumptions, compare migration scenarios, develop business cases, and translate AWS Transform Assessment findings into governed migration and modernization roadmaps.