It just looks like an AI problem. Before the policy, before the tools, before the vendor demos - the people who have to live with the decision need to understand what they're deciding. That's the work we do first.
Every AI failure we've seen on a campus traces back to the same thing: people asked to decide about something nobody taught them.
It shows up as deference. Someone in the room sounds technical, everyone else goes quiet, and a decision gets made by whoever seemed most confident rather than by the group deliberating. Eighteen months later there's a contract nobody can explain and an outcome nobody predicted.
It shows up as policy aimed at the wrong risk. Most AI policies we review govern students and say nothing about the financial aid office feeding records into a free chatbot, or the staff member drafting an accreditation narrative with a tool that invents citations.
And it shows up as resistance that gets misread as obstruction. Plenty of thoughtful educators have real objections to AI. Those objections deserve engagement, not a training mandate - and the ones raising them are often asking better questions than the enthusiasts.
None of that is a technology gap. It's a comprehension gap, and comprehension is something an institution can actually build. That's what AI literacy means: enough shared understanding that your people can deliberate, disagree productively, and decide well.
Three moves, in order. Institutions that skip ahead to tools without the first two end up with expensive pilots and no learning. The third is where nearly everyone falls short.
A
AI is here, it is real, and we must address how it will impact our current and future.
This is not the same as endorsing it. Acknowledgement means the institution stops treating AI as something that might blow over and starts treating it as a condition to be governed - whatever position you ultimately take.
A
We must find ways to adapt our operations and teaching to cater to our students' most pressing needs.
Adaptation starts with the student, not the software. The question isn't which tool to buy - it's what your learners need now that they didn't need three years ago, and what has to change to meet it.
A
This is where most fall short. We must continually assess what are good AI practices and what are not - what is working well, and where we need to abandon AI.
Assessment has to include the willingness to stop. An institution that has never abandoned an AI initiative isn't evaluating; it's committing. Deciding in advance what failure looks like is what separates learning from spending.
Why the third A matters most. Acknowledge and Adapt are where the energy goes - they're visible, they feel like progress, and vendors are happy to help. Assess is unglamorous and it's the only one that compounds. Institutions that build a real habit of evaluating and retiring AI practices get better every year. The ones that don't accumulate tools they can't explain.
Two programs that get an institution from uncertain to conversant, and then turn that understanding into a plan its people will actually accept.
A self-paced video program that takes any leader, educator, or staff member from uncertain to conversant - without tech jargon.
Once your team finishes Foundations, we don't hand them a strategy - we ask them what belongs in it. A short conversational assessment, in their own words.
Literacy is the first phase of a full AI strategy engagement - governance, faculty readiness, policy, and a phased roadmap built on what your own people told us.
Enough shared understanding of how AI systems work, where they fail, and who owns which decisions that an institution can deliberate about them competently. It is not technical training and it does not require anyone to learn to code. The test is whether your cabinet can disagree productively about an AI decision rather than deferring to whoever sounds most confident.
Broader than most institutions assume. Presidents and cabinets, because they approve what they must be able to explain to a board. Faculty, because assignment design is where most of this actually lands. Staff in financial aid, admissions, advising, and marketing, because they are already using these tools and rarely think of themselves as AI users. Trustees, because they vote on it.
It's built for them as much as for anyone. Literacy is not advocacy - the goal is a shared factual baseline, including a clear-eyed account of what these systems get wrong and where they don't belong. Several of the sharpest objections we've heard came from people who went through the program, and those objections made the resulting strategy better.
No. The framework and the training apply across education - community colleges, universities and graduate programs, K-12 districts, private and charter schools, online and alternative providers, and education nonprofits. The core problem is the same everywhere: decisions being made by people who were never given the grounding to make them. CBT's consultants come primarily from higher education, which is where our deepest institutional expertise sits.
It's our three-part framework for building AI literacy in education, and the spine of how we run engagements. Acknowledge that AI is here and real, and address how it will impact your institution now and in future. Adapt operations and teaching to your students' most pressing needs. Assess continually - what's working, and where you need to abandon AI. The third is where most institutions fall short, and it's the only one that compounds. Read the full framework above.
AI Foundations is about seventy minutes total, self-paced, and most people finish it across a week or two. A full literacy-and-strategy engagement typically runs twelve to twenty weeks, beginning with training so everyone shares a common language before any decisions get made.
The welcome video and first module of AI Foundations are open to anyone - no form, no sign-up. See whether it would land with your cabinet before you talk to us.
