When agencies and organizations plan a CMS migration, they typically budget hours for "content migration." Someone will go through the documents, pull out the relevant fields, and enter them into the new CMS. It's tedious, but it's not complicated. How hard can it be?
In practice, manual content migration has several costs that project budgets routinely underestimate.
The inconsistency problem
Manual extraction produces inconsistent output. Different operators interpret ambiguous fields differently. Dates get formatted differently. Field values that should come from a controlled vocabulary drift. Categories that seem obvious to one person aren't obvious to another.
By the time you've manually migrated several hundred documents, you have a CMS full of subtly inconsistent data. Some fields are sometimes present, sometimes not. Some values are in one format, some in another.
Inconsistent data creates downstream problems: queries that miss records, filters that don't work, content that renders differently depending on which operator migrated it.
The expertise mismatch
Manual migration tasks get assigned to whoever has bandwidth, which often means junior staff or contractors who aren't familiar with the content domain. For legal documents, healthcare policies, or government records, this creates an expertise problem.
Someone who doesn't know the difference between an effective date and a filing date will get them wrong. Someone who doesn't understand the document taxonomy will misclassify documents. The review burden falls on senior people who have to catch errors that shouldn't have been made.
In the worst case, errors make it into the CMS uncaught and become a data quality problem that's expensive to fix after the fact.
The velocity ceiling
Manual migration has a hard velocity ceiling: the number of documents a person can process per hour times the number of people you're willing to put on the task. Beyond that ceiling, the only options are more people (expensive, introduces more inconsistency) or more time (blows the project timeline).
AI extraction doesn't have this ceiling. Processing throughput scales with your subscription tier, and the review step stays roughly constant per document regardless of batch size.
What the review step should actually be for
The right role for human reviewers in a migration project isn't data entry — it's quality control. Reviewers should be checking what the automated system flagged as uncertain, not transcribing content that doesn't require judgment.
When you use Pith, that's exactly what the review step is: reviewers see confidence scores, focus on low-confidence fields, and approve high-confidence content without touching it. The human time goes where human judgment is actually needed.