How many Agencies are going to claim "Sitecore AI Powered Migration"?
In thirty years of building on enterprise platforms, I've learned to spot the difference between genuine innovation and marketing theater. The latest trend sweeping through digital agency pitches falls squarely into the latter category: claims of "AI-powered migrations" that promise to transform your platform transition into something magical and effortless.
I've watched these cycles before. First it was "automated migration," then "intelligent content transfer," and now we've arrived at the current buzzword: AI. The claims have escalated from tools that assist with migration tasks to agencies suggesting their AI systems are performing the heavy lifting of moving enterprise content at scale. Having led migrations involving hundreds of thousands of content items across Sitecore, Adobe Experience Manager, and modern headless platforms, I can tell you something that should be obvious but apparently isn't: these claims don't hold up to scrutiny.
The Fundamental Disconnect: Probabilistic vs. Deterministic Work
Let me explain why this matters with a concept that every CTO should understand before signing an agency contract: the difference between probabilistic and deterministic systems.
Large language models and generative AI are inherently probabilistic. They generate outputs based on statistical patterns learned from training data. When you ask an LLM a question twice, you may receive different answers. Even with temperature settings reduced to zero, research has demonstrated that LLM outputs remain non-deterministic due to factors including batch processing variations, floating-point arithmetic, and infrastructure optimization. A 2024 study found that models demonstrated considerable accuracy variation across identical runs, sometimes as high as 72% difference between maximum and minimum scores on certain tasks.
Content migration, by contrast, demands deterministic precision. When you move 50,000 pages from Sitecore to a headless CMS, every single page must arrive intact. Every reference must resolve correctly. Every asset must maintain its relationship to the content that depends on it. Every URL must map to its destination without ambiguity. There's no room for "mostly correct" or "statistically probable."
Consider what a 99% accuracy rate would mean on a site with 50,000 pages: 500 broken pages. 500 opportunities for missing content, broken references, or corrupted metadata. In enterprise environments where content supports compliance, product information, or customer transactions, even a handful of errors can create legal exposure or operational failures.
What AI Actually Does Well in Migrations
This isn't an argument against using AI in the migration process. We use it extensively at HT Blue, but we use it for what it excels at rather than pretending it can replace deterministic engineering work.
AI genuinely helps with content auditing and classification. Before any migration, you need to understand what you have. An intelligent system can analyze thousands of pages and identify patterns: which templates are in use, which content types dominate, which pages haven't been touched in years and might be candidates for archival rather than migration. This kind of analysis would take a human team weeks to complete manually.
AI assists with field mapping recommendations. When moving from Sitecore's data templates to a structured content model in a headless CMS, AI can analyze source schemas and suggest corresponding target structures. It can identify semantic similarities between field names and propose mappings that a developer can then verify and refine.
AI accelerates script generation. Given a well-defined mapping specification, AI can help write the transformation scripts that convert content from one format to another. But here's the critical point: those scripts then execute deterministically. The AI doesn't perform the actual migration; it helps create the tools that do.
AI improves documentation and knowledge transfer. It can generate comprehensive documentation of migration decisions, transformation logic, and testing procedures. This makes the migration more maintainable and helps organizations understand what happened after the project concludes.
The pattern should be clear: AI augments human expertise during planning and preparation phases, but the actual content transfer must execute with mechanical precision.
Why Scale Makes This Impossible for LLMs
Let's examine what an "AI-powered migration" would actually require if agencies meant what their marketing implies.
A site with 50,000 pages isn't just 50,000 text documents. Each page contains structured content with dozens of fields. There are asset references pointing to images, documents, and media files. There are internal links that must resolve to correct destinations. There are metadata structures that support search, personalization, and analytics. There are relationships between content items that define navigation, related content, and taxonomy assignments.
Conservative estimates suggest that a 50,000-page enterprise site contains between 2 and 5 million discrete content relationships that must remain intact during migration. Every one of those relationships must be transformed correctly, or the destination site breaks in ways that may not become apparent until a customer encounters a dead end.
LLMs have context window limitations. Even the most advanced models can only process a finite amount of information at once. A serious enterprise migration would require maintaining awareness of millions of relationships simultaneously to ensure consistency. This isn't a limitation that will be solved by the next model release; it's a fundamental architectural constraint.
The cost profile doesn't work either. Processing each content item through an LLM API at enterprise scale would generate API costs that exceed traditional migration tooling by orders of magnitude, with results that would still require complete verification by deterministic testing.
The Market Pressure Behind False Claims
I don't believe most agencies making these claims are deliberately dishonest. They're responding to market pressure.
Clients have heard about AI transforming every industry. Procurement teams ask about AI capabilities as a checkbox item. RFPs include questions about AI utilization. Agencies that can't articulate an AI story risk being excluded from consideration before the conversation about actual technical competence begins.
So agencies stretch. They describe using ChatGPT to draft migration documentation as "AI-powered migration." They position LLM-assisted code generation as artificial intelligence performing the migration work. They conflate AI-augmented analysis with AI-executed transformation.
Some agencies genuinely don't understand the technical distinction. They've adopted AI tools without deeply understanding their limitations, and they market capabilities they believe exist but haven't rigorously tested at scale.
The result is a market full of claims that will disappoint anyone who believes them literally.
What Enterprise Migration Actually Requires
After leading migrations across Sitecore, Adobe Experience Manager, Drupal, and multiple headless platforms, I can tell you what separates successful projects from disasters.
Rigorous content modeling comes first. Before writing a single line of migration code, you must understand both the source and destination data models completely. This means documenting every template, every field, every relationship, and every edge case in the source system. It means designing a destination model that preserves information integrity while taking advantage of the target platform's capabilities.
Comprehensive field mapping follows. Every piece of data in the source system must have a defined destination. This isn't creative work; it's systematic analysis that produces a deterministic specification. When this specification is complete, there should be no ambiguity about where any content item or relationship ends up.
Automated transformation scripts execute the actual migration. These scripts read from the source, apply the transformation rules defined in the mapping, and write to the destination. They're deterministic by design. Given the same input and the same rules, they produce identical output every time.
Validation testing verifies results. Migration isn't complete when content arrives in the destination system. It's complete when automated testing confirms that content relationships resolved correctly, that assets render properly, that URLs produce expected behavior, and that the migrated site functions as intended.
This process is methodical, reproducible, and verifiable. It's not particularly exciting, but it's what protects organizations from the consequences of migration failures.
The Sitecore Reality
Let me speak specifically about Sitecore migrations since they represent some of the most complex enterprise work in our practice.
Sitecore's architecture presents unique challenges that highlight why deterministic approaches matter. The platform stores content in a hierarchical tree structure with sophisticated inheritance patterns. Items can inherit field values from templates, branch templates, and parent items. Presentation details define how content renders through a combination of layouts, sublayouts, renderings, and placeholder assignments.
A single Sitecore page might involve content from a dozen different items, with rendering logic determined by personalization rules, multivariate testing configurations, and context-specific conditions. Migrating that page to a headless CMS means understanding every source of content, every condition that affects presentation, and every relationship that must be preserved or transformed.
The typical enterprise Sitecore implementation includes custom field types, pipeline extensions, and event handlers that affect how content behaves. These customizations must be understood before migration can proceed, because they define constraints that the transformation process must respect.
Adobe Experience Manager presents similar complexity through its different architecture. Content fragments, experience fragments, editable templates, and the component dialog system all create interdependencies that migration tooling must navigate correctly.
These aren't problems that respond to probabilistic solutions. They require systematic analysis and deterministic execution.
Evaluating Agency Claims
When an agency tells you they use AI for migrations, ask specific questions.
Where exactly in the process does AI execute work versus assist human experts? An honest answer will describe AI involvement in analysis, documentation, and code generation rather than claiming AI performs the actual content transfer.
What's your verification methodology? A credible agency can describe their testing approach in detail, including how they validate content integrity, relationship preservation, and functional correctness.
Can you show me the transformation specifications for a comparable project? An agency with genuine migration expertise produces detailed documentation as a natural byproduct of their process. If they can't show you examples of field mappings, transformation rules, and validation criteria, they may be improvising rather than executing a proven methodology.
What's your rollback strategy? Enterprise migrations sometimes encounter issues that require reverting changes. An agency that relies on deterministic tooling can describe exactly how they would restore the source system if problems emerge. An agency relying on opaque AI processing may not have that option.
The Path Forward
AI will continue improving, and its role in content migration will expand. I expect to see better tools for content analysis, more sophisticated mapping suggestions, and improved automation of repetitive tasks.
But the fundamental requirement for deterministic execution won't change. Enterprises need migration results they can verify, reproduce, and trust. That requires tooling that behaves predictably and produces consistent outputs regardless of when or how many times it runs.
If you're evaluating agencies for a platform migration, look past the marketing language. Ask about methodology. Examine past project documentation. Talk to references about whether delivered systems matched specifications.
The best platform decisions are made slowly, with clear eyes about what you're actually building. The same applies to choosing who builds it.
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*The Arch of the North leads platform architecture at HT Blue, bringing thirty years of enterprise implementation experience across Sitecore, Adobe Experience Manager, Optimizely, and modern headless CMS platforms.*
