Most institutions do not have an accreditation department. They have an accreditation officer, which is usually one role tucked under the provost or dean’s office, sometimes a fraction of an associate provost’s job, occasionally a dedicated director at larger institutions. On the official organization chart, that’s where accreditation actually lives.
The official organization chart is a fiction. What institutions actually have is a shadow organization chart; a network of roles that quietly activates when a reaffirmation cycle ramps up, performs the work of accreditation for two to three years, then dissolves back into “regular duties” until the next cycle. It never appears in a budget document, is rarely acknowledged in workload policies, and is almost never compensated proportionally to what it produces. Yet without it, no institution would maintain accreditation.
The previous post in this series argued that the decade-long debate about the $27 billion cost of accreditation has been pitched at the wrong altitude, that the more important question is not how much the work costs in aggregate, but who specifically, is absorbing it. This post is about who.
And like any org chart, the shadow one has tiers, so the cleanest way to answer is to explicitly map the chart no budget document ever does, top to bottom.
The named owners
The top tier of the shadow chart is the only one that overlaps with the official one. A small number of people on every campus have accreditation in their job description. Their names appear in the self-study; they get the email from the accreditor; they sit on the steering committee.
The accreditation liaison officer or associate provost owns the timeline, the institutional narrative, and the political risk. In peak years this is functionally a full-time role layered on top of an existing full-time role.
Deans and department chairs assemble program-level evidence such as assessment plans, curriculum maps, alignment to accreditor standards. A chair with 3 classes per semester load running a department of fifteen does this on top of, not instead of, normal work. A 2024 study in the Journal of Higher Education Management found that chairs already report administrative work as the dominant source of role strain; reaffirmation compounds an existing overload rather than creating a new one.
Faculty assessment coordinators often on stipends in the low four figures run rubric calibration, closing-the-loop documentation, and artifact collection, frequently with no course release. Annualized against actual hours, the stipend often falls below the institution’s own adjunct pay rate.
These are the people whose accreditation labor at least exists somewhere in the institutional record. Their hours are still undercompensated for the amount of work, but at least they are nominally counted.
The bottleneck infrastructure
One tier down sit the offices everyone else’s deadline runs through. On the official chart, they report somewhere else entirely; on the shadow chart, every arrow passes through them. They aren’t “in charge of” accreditation, but nothing about it works without them.
The institutional research office generates the data: completion and retention rates, time-to-degree, faculty credentials, financial ratios, learning outcomes.
The same dataset gets cut differently for the federal report, the regional accreditor, the state licensing authority, the internal dashboard, and any programmatic accreditors on their own timelines.
IR offices are commonly staffed at one or two analysts for the entire institution. During a self-study year, they become the single point of contention for half a dozen simultaneous deadlines.
The CFO’s office demonstrates financial sustainability; facilities demonstrates classroom adequacy; the library demonstrates information literacy outcomes. Registrar, financial aid, student affairs, IT, and HR staff field document requests on accelerated timelines, usually with no acknowledgment in their evaluations.
Accreditation work moves to the top of their queue without ever being formally added to it. The hours come from whatever else those offices would otherwise be doing.
In normal times, these offices have their own work and their own service portfolios. During reaffirmation, accreditation work moves to the top of their queue without ever being formally added to it. The hours come from somewhere. They come from whatever else those offices would otherwise be doing.
The invisible labor
The bottom tier of the chart is the one the field talks about least. It is also, like the bottom of most org charts — the widest. And on the shadow chart, it is drawn in invisible ink.
As per the American Association of University Professors’ Fall 2023 data, roughly 68 percent of faculty appointments in U.S. higher education are now contingent non-tenure-track, often part-time, typically paid by the credit hour with little or no compensation for their service work.
Accreditation requires those faculty to do service work anyway.
CAEP, ABET, AACSB, ACEN, CCNE, and CACREP all expect documentation that adjuncts are evaluated, oriented, and participating in continuous improvement. Someone has to collect it. In practice, either full-time faculty absorb the load on behalf of uncompensated adjuncts, or adjuncts are pulled into uncompensated service to make the documentation defensible.
This is not marginal.
At community colleges, where contingent faculty teach the majority of credit hours, accreditation labor has a built-in equity problem most institutions have not honestly grappled with: the faculty with the least job security and the least time are the ones whose participation is most documentation-dependent.
The system has been resting on their goodwill, and that goodwill has nowhere left to come from.
Why naming this matters
When institutions estimate the cost of accreditation, they almost always undercount this shadow chart, for two reasons.
The first is methodological. Most cost studies use accounting categories that map to budget lines such as fees, dedicated salaries, consultant invoices. The shadow chart shows up in none of them; its labor is absorbed into salaries counted as “regular duties.”
The second is cultural. Higher education has long treated service work as something that simply happens, performed by professionals whose job description is flexible enough to absorb it. That tradition was built when workloads and the regulatory environment were lighter. The State Authorization Network’s 2026 report found that 85 percent of institutions reported a significant or moderate increase in compliance workload, and that is one regulatory domain among many.
When the shadow chart is invisible, three things follow. The labor stays uncompensated. The people doing it burn out and leave, taking institutional memory with them, often right after a reaffirmation. And the next cycle gets harder, because the people who knew how the last one worked are gone.
What the AI conversation usually misses
Everything above describes labor that stays invisible because it’s disguised as paperwork.
The associate provost’s negotiation looks like a scheduling email. The IR analyst’s framing decision looks like a spreadsheet query. The coordinator’s norming call looks like data entry. That disguise is exactly why the current conversation about AI in accreditation is getting something subtly, consequentially wrong.
Most of it is about AI, not with AI.
Accrediting bodies and policy groups such as CHEA, UNESCO, the Middle States Commission’s 2025 policy, the ACCME’s January 2026 guidance for continuing medical education have been writing about how institutions should govern, audit, and ethically deploy AI in their own teaching and operations. That work matters. It is also a different conversation from the one this post is in.
The narrower conversation about using AI to do the labor of accreditation, not to be regulated by it, is smaller and nuanced.
Tawnya Means’ April 2026 essay, argues that AI could make continuous improvement actually executable: faculty design the rubrics and interpret the results, AI handles the volume work of evidence aggregation and pattern detection.
The National Accreditation Commission’s AIHub project, funded by the GitLab Foundation, OpenAI, and Ballmer Group, is the most concrete attempt we know of to actually build this.
We want to add one thing, and it follows directly from the shadow chart as metaphorically elucidated above.
The data layer and the judgment layer are not cleanly separable, because the shadow chart already fused them. The associate provost negotiating with a chair over what counts as adequate evidence is doing data work and judgment work in the same conversation. The IR analyst deciding how to cut a dataset is doing both in the same query. The coordinator running a norming session is doing both in the same room.
Much of what looks like “document labor” from the outside is compressed judgment labor — decisions about what the evidence means, who needs to see it framed which way, and which institutional fights are worth having.
This is why “AI handles the documents, faculty handles the judgment” is a useful first frame but an incomplete one. The harder design question is which specific parts of the document layer are safe to automate, which parts carry compressed judgment that automation will quietly erase, and how to tell the difference at the design stage rather than after the fact.
That distinction is what this series returns to later.
For now the point is structural: AI is most likely to help with the labor in the bottleneck infrastructure layer such as the IR cuts, evidence aggregation, rubric application at scale, and most likely to cause real damage in the parts that look like documentation but are actually institutional judgment.
Telling those apart is the work the field has not really started yet.
