If you’ve sat through more than a handful of conversations about accreditation reform, you’ve heard the number.

$27 billion a year in federal regulatory compliance costs across U.S. higher education, with roughly $3 billion of that attributed to regional accreditation alone.

The figure comes from a 2015 Boston Consulting Group study commissioned by Vanderbilt University, and in the decade since it has done remarkable work. It’s been quoted in congressional testimony, pulled into op-eds, leaned on by every think tank with a position on higher ed regulation, and treated, more or less, as settled.

It is not settled.

And the fact that it has been treated as settled for ten years tells you something important about how higher education talks about its own labor.

The figure, and the pushback

The original BCG/Vanderbilt study sampled 13 institutions and extrapolated to the sector. It found that regulatory compliance represented 3 to 11 percent of core university operating expenses at the studied institutions, and that faculty and staff spent between 4 and 15 percent of their time on compliance activities.

From there, the study estimated roughly $17 billion in non-research higher-education compliance, $10 billion in research-related compliance, and inside that total, $3 billion for regional accreditation and another $3 billion for programmatic.

The pushback came almost immediately.

Belle S. Wheelan, then-president of the Southern Association of Colleges and Schools Commission on Colleges, and Mark A. Elgart of AdvancED published a sharp essay in Inside Higher Ed arguing that the headline figures were “significantly inflated as well as irresponsibly misleading.”

Their case had two parts.

First, much of what the study counted as accreditation cost was actually federal research-grant compliance — a separate regulatory regime that happens to overlap with accreditation timelines.

Second, they argued that genuine accreditation activity is “part of regular faculty service and committee work” that contributes to institutional improvement regardless of whether an accreditor exists.

Wheelan and Elgart pointed to a different number: across three accreditation regions, the average combined direct and indirect cost of accreditation at doctorate-granting research universities came out to roughly $415,000 per institution over the full review cycle.

That is not nothing.

It is also nowhere near the per-institution figure the $3 billion sector estimate implies.

So which number is right?

They’re both right, and that is the problem.

Here's the uncomfortable truth: both estimates are defensible, because they're answering different questions.

The Vanderbilt study counted everything an institution spends on activities that touch federal compliance, including faculty and staff time valued at full loaded cost. That methodology produces a big number because faculty and staff time is a big number when you actually count it.

Wheelan and Elgart’s framing counted only the activities that exist solely because an accreditor requires them — the marginal cost of accreditation, separate from work that institutions would do anyway.

You can argue all day about which framing is more honest. What you cannot argue is that either figure settles the question for the people doing the work. Because here’s what neither one tells you: how the cost is distributed inside the institution.

A $415,000 cycle cost at a research university sounds manageable in budget terms. Spread across a five-to-ten-year reaffirmation cycle, it is.

But that figure says nothing about whether the assistant provost coordinating the self-study is doing it on top of a full administrative load.

It says nothing about whether the institutional research office gets backfill for the analyst pulled into evidence prep for nine months.

It says nothing about whether the department chairs absorbing program-level documentation are getting course releases or just gratitude.

This is the gap that the entire $27 billion debate has been talking past.

The argument has been pitched, for a decade, as a referendum on whether accreditation is “too expensive” in aggregate.

The more interesting question — and the one that actually predicts whether a reaffirmation cycle will damage an institution — is whether the distribution of the cost is sustainable for the specific humans absorbing it.

That question has been almost completely absent from the policy conversation, and the people who could answer it most precisely are also the people too busy carrying the load to write about it.

What the more recent data adds

The 2015 Vanderbilt figures are now a decade old, and the regulatory environment has only thickened since.

The State Authorization Network’s 2026 report Between the Lines, Behind the Work offers a more current window into one slice of the load.

Eighty-five percent of institutions reported a moderate or significant increase in licensure compliance workload under newer federal certification requirements.

Nearly three-quarters added between one and ten staff hours per week.

Twelve percent added 21 or more hours weekly — the equivalent of a half-time hire’s worth of new work, often with no half-time hire to do it.

That is a single compliance domain at a single staffing level.

It does not include faculty time, IT coordination, cross-departmental meetings, or the cascading effect on other priorities those hours displace. Multiply across the full accreditation and compliance landscape, and the labor question gets sharper, not vaguer.

What the number was always for — and what the next decade will ask of it

The $27 billion figure has been useful precisely because it is big and quotable.

It moved the conversation.

It got accreditation onto agendas it would not otherwise have reached. That has value, even if the number itself is contested.

But a decade in, the figure has become a stand-in for a conversation we are not actually having.

The real question was never “is accreditation too expensive in aggregate.” It was always “who, specifically, is paying for it, and is the load distributed in a way the institution can sustain.”

That question matters more now than it did in 2015, for a reason that hasn’t fully landed in the policy literature yet.

The same decade that produced the $27 billion debate also produced a new generation of tools — large language models, agentic systems, document-aware AI — that are genuinely good at exactly the kind of work the shadow labor of accreditation consists of: reading, summarizing, mapping, evidence aggregation.

Whether and how those tools belong inside accreditation work is a real question, and one we’ll come back to later in this series.

But it cannot be answered honestly until the labor itself is honestly counted.

You cannot redesign work you cannot see.

Session Details:

What We Are Exploring

Module 1 surfaced where the AoL process feels most burdensome and where AI could genuinely help.

Module 2 moves from that discussion into practice: we’ll walk through what an agentic AoL workflow actually looks like, step by step.

What We’ll Cover

We'll walk through a practical example of an agentic workflow, using it as a starting point for discussion. Along the way, we'll examine how different components work together, where faculty expertise remains essential, and the questions institutions should consider when evaluating similar approaches.

Some of the themes include:

• Where AI adds value—and where it doesn’t

• Human oversight, trust, and workflow design

• Institutional considerations for adopting AI responsibly

• Open practitioner discussion

Facilitators

• Daisuke (Dai) Nishimura, Managing Director, Macnica

• Dolma Rawat, Research Lead, Macnica

• Jerelyn Co, AI Engineer, Macnica

About the Roundtable Discussion

Our roundtables are practitioner-led discussions built to explore emerging questions around AI and Accreditation. They’re intended as a space for shared learning, thoughtful discussion, and exchanging perspectives across institutions.

Interested in Participating?

If this topic resonates with you and you’d like to take part in this roundtable or future ones, we’d love to hear from you.

Register using the button below. If you have any questions, reply to this post or get in touch—we're always looking to bring new perspectives into the discussion.

Register for the Session

We recently concluded our webinar on June 10, 2026, where we held the first session the Roundtable Series: a small, practitioner-led conversation about how agentic AI can be leveraged in accreditation, particularly Assurance of Learning (AoL). Our cohort was extremely small, but spread across two institutions in the United States and Canada, which gave us a diverse perspective. An hour of conversation led us to various insights, which we noted down not only for us, but for you as well.

Purpose of the Roundtable

While we are discovering a lot through our own research, nothing compares to hearing it from the people living inside the process on a day to day basis.

We wanted to hear their voices, and make the session as conversational as possible.

Faculty Engagement as the #1 Hurdle

One of the questions we really wanted to understand was which part of the whole AoL process takes the most amount of time. As we have been exploring the role of AI in accreditation, this one was crucial for us to see whether there was a fit.

To our surprise, two practitioners without a flinch named faculty engagement as the biggest hurdle.

What's interesting to us is decoding why faculty engagement is the hurdle. Is it because the process is too tedious? Is it because of the additional work? Or because of the hidden responsibilities?

From our research, we discovered that the additional responsibilities are what weigh on faculty and keep them from engaging with the accreditation process in a meaningful way. The indirect consequence of this is that AI can be leveraged to ease some parts of the process, making AoL more efficient and smoother.

Understanding Agentic AI

What followed was the team clearly defining the capabilities of Agentic AI, and how it differs from the standard GenAI LLMs that are more commonly used.

Agentic AI works differently from GenAI. It goes a step beyond answering to a prompt, it can take a goal, plan out the steps, use tools like files, databases, or APIs, and keep working through the task in a loop until the goal is met.

One analogy from the session that stuck with us while thinking about Agentic AI: if generative AI is the engine, then agentic AI is the whole car, and it can run several of those engines at once.

Where it Fits in Accreditation, particularly in the AoL process

After establishing the definition and capabilities of Agentic AI, the team then explained all the parts of the process in AoL to explore of parts where Agentic AI can be a good fit and parts where it can’t.

The steps pointed out in the session were the following:

One observation quickly became clear: participants saw the greatest potential for agentic AI in the second stage of the process—rater norming. Which primarily indicates the rating unevenness across faculty while scoring artifacts.

While AI can never be a replacement for a faculty member grading the rubric, there is context to the grading process that an AI as of today can never understand, whether it's how the curriculum was delivered, or how the curriculum changed before a course was phased into the semester, which led to a difference in learning outcomes. All of this is still out of scope.

However, what AI is specifically good at is creating a baseline evaluation, which ensures there is consistency in grading.

If five faculty members apply a rubric to student work and each applies it slightly differently, then there is a calibration problem. An AI system doesn't drift between graders, and human spot-checks can confirm reliability without re-doing every score by hand.

Faculty Oversight is Non-Negotiable

One thing the room was very clear on is that faculty oversight is not optional. AACSB allows AI to be used across many steps of the process, as long as faculty review and approve the outcomes, and that requirement doesn't go away.

One participant also pointed out that a process which looks too automated can fail the "smell test" with a review team, even if the outputs are good.

What's also interesting is that oversight tends to erode over time. As people build trust in a system, the review gets lighter pass over pass. It's not a flaw in any one person, it's just a very human pattern, and it's something any workflow has to be designed against from the start.

Data Privacy as a Primary Hurdle for AI Adoption

Prior to our roundtable, we also asked the participants to fill out a survey, and one of the questions was about the biggest hurdle they foresee in adopting AI.

And rightly so. Handling student data is extremely sensitive. Sensitive data such as student names, IDs, and grades are all things that faculty may be hesitant to upload to a cloud-based service such as ChatGPT or Claude.

That's why AI shouldn't be adopted without being mindful of the risks. It also means adopting technologies that are specifically FERPA-compliant and aligned with institutional data privacy frameworks, while building workflows that keep all the right guardrails in mind for how the infrastructure handles that data.

But compliance is really just the floor.

The harder questions sit above it: Who holds the vendor contract, and what does it say about data use? Where is student data actually stored, and in which jurisdiction? Is there any possibility the model is being retrained on institutional inputs? Who inside the institution has access to query logs, and who audits that access?

These are data governance questions, and most institutions haven't had to ask them this rigorously until now. AI is forcing that conversation whether schools are ready for it or not.

Where We’re Headed

Through our conversation, it was clear that there's a real fit for AI in the workflow of AoL.

The key question now is where to focus: identifying which parts of the process AI can genuinely strengthen, and which are better handled without it. That's the calibration: understanding each institution's specific needs, how they handle data internally, and at what stage AI can add the most value.

In our next session on July 28, we'll move from theory to practice by demonstrating of how to design an agentic workflow within the AoL process. If you work in AoL and and this resonated with you, we'd love to have you join the conversation.

Registration details to follow.

There’s a particular kind of work in higher education that almost nobody outside it has heard of, and the people inside it tend to talk about in tired, slightly resigned tones (oftentimes vocal about the burdens of extensive paperwork).

It goes by different names depending on which accreditor your particular department/school answers to. Assurance of Learning if you're in a business school. Outcomes assessment if you're in engineering. Competency-based programmatic review in the health professions. The vocabulary changes; but the work doesn't. You define what students are supposed to learn, you collect evidence that they actually learned it, and every few years you prove to an accrediting body that you've documented the whole process meticulously, often rigorously and as accurately as possible.

On paper it’s straightforward. In practice, the task is much more complicated than it looks —and the work falls disproportionately on a small number of people in each program outside their formal work duties.

Why we started writing

The starting point was something narrower than a blog. We were running a proof-of-concept study with Claremont Graduate University’s AI for Humanity Lab, exploring whether AI could meaningfully support rubric-based competency evaluation—the kind of scoring work that sits at the center of most accreditation cycles.

At first, it was a research question: can an LLM model help here, and where does it fail?

We started Aolia because we kept finding ourselves in conversations with assessment directors, program coordinators and associate deans across different institutions, and noticing how similar their pain points were — and how rarely they had a space to compare notes.

So this is an attempt to change that: a community of practice for people inside accreditation, built by people who’ve spent time listening carefully to what the work actually looks like from the inside.

What we'll be writing about

Some of them will be about AI, because that question is unavoidable now and most of the writing on it is either too optimistic or too dismissive to be useful.

Most of the posts will be not be about AI directly.

It will be about the administrative burden problem. What reviewers actually look for. Why so much assessment data never makes it back into curriculum decisions.

It’s about the gap between what accreditation asks programs to demonstrate and the infrastructure they’re actually given to demonstrate it with.

If you’ve ever felt that gap personally, you’ll recognize it.

Beyond the writing

That listening turned into something more structured this year, starting with conversations across the AACSB community. We started the AoL Roundtable, a small, invite-only series where Assurance of Learning (AoL) directors, faculty, and deans get hands-on with Agentic AI, exploring how it can be applied within their own institutions workflows.

As the series runs, we’ll be publishing what comes out of it here, alongside what we’re learning from the research side.

Before you go

If you have a few seconds, we'd genuinely like to hear this: where in your assessment or accreditation cycle does the process feel least supported?

If something is slow, manual, duplicated, or quietly held together by workarounds—we’re especially interested in that.

Hit reply and tell us what you work on and what you wish someone would take seriously. Much of what we publish here will come from those conversations, and we read every response.

Glad you’re here. We’re looking forward to building this with you.