Beyond the license fee: The hidden costs of AEM and the escalating demands of AI

Most enterprise technology teams already know they need to change something. Recent research into modernizing infrastructure for the digital enterprise found that 83% of enterprises are actively rationalizing their technology infrastructure but only 35% say their current approach is working well (Source: IDC, Modernizing Infrastructure for the Digital Enterprise).

So the intent to switch platforms is there for most organizations, but there’s a lot less confidence about whether the direction they’re moving in is the right one, or how much standing still is costing them in the meantime.

For platforms like AEM, a lot of that cost sits outside the license fee. Specialist skills, platform-specific support, and ongoing infrastructure all add to the picture, and once routine changes start depending on specialist developers, cost tends to go up while speed goes down. Ticket queues, handoffs, and platform constraints can turn what should be a simple change into a much longer delivery cycle, which ends up delaying campaigns and slowing down experimentation more generally.

Part of that cost comes from AEM’s specialized tooling. Platforms built on more widely used technologies like JavaScript and React reduce hiring and operational overhead, and new pages and features go live and iterate faster.

A second audience for your content

At the same time, how buyers research has been changing. Traditional search’s share of B2B buyer journeys fell from 68% to 41% in 14 months, with AI assistants accounting for 31% of that shift, according to AuthorityTech’s 2026 research.

A growing proportion of buyers are building their shortlist through AI systems, such as GPTBot, PerplexityBot, and ClaudeBot, before they ever visit a website directly. Most enterprise sites were designed for experiences, but lack an underlying content and data structure that AI crawlers rely on to parse and cite content.

A site can work well for that original audience and still be difficult for AI systems to read properly, largely because that second audience barely existed a couple of years ago.

The content maturity curve

Looking at both issues together, they tend to come from the same place: content that’s built and managed as individual pages rather than as structured, reusable data.

Most enterprise content operations sit somewhere on a maturity curve. And a lot of large-scale AEM implementations are still at the earlier stages, where every page is built and changed by hand, or where templates exist but changing one still means a ticket and a wait, with content unable to move between channels without being rebuilt.

Further along that curve, content is modeled as structured data instead of pages. An article, product story, or campaign block is authored once and reused across web, app, and campaign, and every instance stays in sync.
Editorial roles and approval workflows are tied to the content model, and scheduled releases and live preview let marketing and product teams stage and publish changes without a deployment.

For groups running several brands, parallel AEM instances can be consolidated into one governed content model, with shared components and taxonomy where the brands overlap and local control where they differ.
Eventually, the same structure lets content work for AI systems in the same way it works for a browser.

Keeping the site live through migration

Getting there means migrating the full estate. In large estates, dozens of components need rebuilding, and content and rendering often span multiple systems with dependencies between them. Much of that scope isn’t fully known until an audit is done, which is why sequencing matters as much as the migration.

Separating where content is authored from where it’s rendered lets each part move and be verified on its own track. Scope needs to be mapped before anything moves, and a parallel run keeps the site live until production traffic is verified on the new platform. After launch, your team can extend the content model without starting a new project.

We’ve worked with enterprises at a few different points on that path. For a national pet retailer, we migrated over 100,000 pages of content off AEM within three months, which brought costs down by more than 50%. For a national environmental services enterprise, we took the whole organization off AEM across four major domains, including a recently acquired brand, without losing organic search performance in the process.

See where your platform stands

If any of this sounds familiar for your own platform, we’ve put together two roadmaps that dive further into both problems, one on where cost and friction actually sit in your current setup, and one on how ready your content architecture is for AI.

CMS Cost and Content Architecture Review: [Click here]
AI Content Readiness Assessment: [Click here]

Joe Comins

Director of Strategy, eCommerce

Read Joe’s full bio