Guide: Moving from AEM to Magnolia DXP

Moving from Adobe Experience Manager (AEM) to Magnolia is a structured project with a known shape. Both platforms store content in a Java Content Repository (JCR), so most content can move by script. The real work sits in components, templates and integrations.

This guide is for teams evaluating Magnolia who want to know what a migration involves before they commit. It covers:

The guide assumes an AEM Sites estate built on the page and component model. If parts of your estate keep content outside the repository, discovery defines a separate extraction path for them.

AEM and Magnolia share a Java and JCR foundation, which is what makes a largely automated content migration possible. Concepts your team already knows have a direct counterpart.

Both platforms sit on a JCR, but they model content and rendering differently. These differences are where migration effort goes, and each one has a known mapping.

What varies from project to project is how much custom logic sits on top of these structures. That is what discovery measures.

Every AEM artifact in a typical Sites project has a Magnolia target. This mapping drives the migration scripts, so the same input always produces the same output.

Some things have no automatic mapping: custom Java services, workflows, and integration code. Discovery lists each one and decides whether to rebuild it, replace it with a Magnolia feature, or retire it.

Your cutover strategy decides when traffic and authoring move from AEM to Magnolia. The deciding question is simple: can your authors pause content changes for a defined window, and across how much of the estate?

AEM keeps serving all traffic and remains the authoring platform until the complete estate is ready on Magnolia. Ahead of go-live, authoring in AEM stops. This freeze gives a fixed content baseline.

During the freeze window, the team migrates the final content changes, validates the migrated content and critical functions, and runs agreed go/no-go checks. Traffic and authoring then switch to Magnolia in one coordinated cutover.

The migration runs in waves, one tenant (site, market or business unit) at a time. Each tenant is migrated, validated and cut over on its own. Only the tenant in its cutover window is frozen; the rest keep authoring in AEM as normal.

If no freeze is acceptable at all, even per tenant, both platforms run in parallel and authors make each change in both. This adds editorial effort and room for human error. Keep it to a short, controlled period with written authoring procedures.

For any estate beyond a handful of pages, migrate components and templates first, then pages. Once the target structure is validated, page migration becomes mainly a data migration task that runs at scale.

Think of it like a database migration: create the schema and relationships, then load the rows.

1. Migrate component templates and page templates. Platform-wide components come first, since most tenants use them. Tenant-specific components follow.
2. Validate each component once. Check fields, dialogs, rendering and visual match against the source.
3. Reuse the validated pattern for every instance of that component across the estate. Special cases and specific requirements still get human review.
4. Load pages in bulk against the validated structure.

A page-by-page approach makes sense only for a small proof of concept. At scale it creates duplicate effort, because different people end up solving the same component on different pages.

Validation follows the same logic. People don't need to check every page. Template-level verification plus sampling covers the estate, with the sampling depth set by your project's requirements.

A migration runs through four phases. The technical work inside the execution phase follows a fixed four-step workflow, repeated for each wave.

Discovery runs once. Execution, validation and go-live repeat for each wave, and hypercare follows the final go-live.

The team assesses your AEM environment: architecture, content structure, integrations, customizations and dependencies. Scripts and AI-assisted analysis speed up the inventory; experts interpret it. The result is an agreed scope, wave structure, timeline, roles, cost and success criteria.

Waves usually start with a pilot market or site. Each later wave builds on what the earlier ones validated. Within each wave, the work follows four steps:

1. Inventory. List every AEM artifact (components, templates, content fragment models, experience fragments, assets, tags) and map each one to its Magnolia target.
2. Build. Generate components and page templates in light modules to set the base patterns. Then build content types and content apps.
3. Migrate. Move content in dependency order: tags, then assets with binaries and metadata, then content fragments, experience fragments, then the full page tree with its components and live-copy relationships.
4. Verify. Update reference links, reconnect external integrations such as analytics, CRM and PIM, and run QA.

Under the hood, step 3 is an extract, transform, load (ETL) pipeline:

Run the first migration and validation in a lower environment, then promote validated content to higher environments.

Each wave goes through QA, functional testing, and content checks by your own authors and editors. Magnolia then runs a pre-go-live check. Nothing goes live until it meets the agreed criteria and is signed off.

After the final go-live, the delivery team stays on for an agreed hypercare period to monitor performance and fix issues. Once things are stable, a structured knowledge transfer equips your team to run Magnolia independently.

Some migrations complete in about six months. Larger estates take more waves, and discovery confirms the timeline for yours.

The migration itself runs as deterministic, repeatable scripts. AI assists people with analysis and refinement, but it is kept out of the scripts that move your content.

The reason is reproducibility. Migrations get re-run: rehearsals, delta runs before cutover, fixes after a mapping change. Each run must give the same result from the same input. Three designs were weighed:

In practice, the work splits like this:

Two consequences matter for most evaluations. First, the bulk content load does not pass through an AI model. Second, AI-assisted refinement is optional, so the migration can still run if your policies restrict AI use.

In a tool-assisted migration, pages are cheap and components are the work. Pages load in scripted batches once their components are validated, and each validated component is reused for every instance across the estate. So effort follows the number and complexity of your components, not your page count.

You don't have to estimate that from rules of thumb. The migration toolkit includes an estimator that scans your AEM estate before you commit, and discovery turns that scan into an agreed scope, wave plan and budget. Most effort concentrates in a predictable set of places.

The main effort drivers are:

Answering these before discovery shortens it and sharpens the estimate.

You can run a migration with your own team, with a partner, or with Magnolia. If you want Magnolia to own it, the Magnolia Navigator migration program covers the full journey from discovery to handover.

In Magnolia Navigator, a named delivery lead and a team experienced in both AEM and Magnolia run the four phases in this guide. Scope and investment are agreed in discovery and don't expand without your sign-off. After go-live, hypercare and a structured knowledge transfer prepare your team to run Magnolia; Magnolia Professional Services and SLA-based support remain available after that. Mixed teams with your developers or a partner are possible.

Next step: talk to a migration expert to map out your migration, or see the Magnolia vs Adobe Experience Manager comparison.

Adobe and Adobe Experience Manager are either registered trademarks or trademarks of Adobe in the United States and/or other countries. Statements about AEM in this guide are based on Adobe's public documentation, accessed October 27, 2026: