Faculty are using it to write. Students are using it to study. Administrators are experimenting with tools their IT department hasn't reviewed. We help colleges and universities get ahead of AI rather than react to it.
The Challenge
Most colleges and universities don't have a shortage of AI tools. They have a shortage of clarity. What is AI being used for on campus? Who approved it? Where is student data going? What's the policy on academic integrity? What does responsible AI use actually look like in a classroom or advising context? These are institutional questions that technology alone can't answer.
Most institutions don't have a comprehensive AI policy. Individual departments and faculty are making their own calls, creating inconsistency across campus and leaving the institution exposed when something goes wrong.
FERPA protects student education records, but many AI tools in use on campus haven't been assessed for compliance. When faculty or staff input student information into a third-party AI tool, the institution may be in violation without knowing it.
AI systems used in admissions, advising, financial aid, or academic support can encode bias in ways that disproportionately affect underrepresented students. Without explicit bias review, institutions can build equity problems into their infrastructure.
Where AI Fits
From student services to research administration, here's where AI can meaningfully improve operations in higher education, and what needs to be true for each application to be responsible.
AI-powered academic advising tools can answer common student questions, surface degree audit information, and flag students at risk of dropping out. Built on validated institutional data with FERPA guardrails, these systems can meaningfully improve access to advising at scale.
Predictive models that identify students at risk of stopping out before they actually stop out. These require careful design to avoid bias, clear human-in-the-loop processes, and institutional commitment to acting on what the data surfaces.
HR, facilities, procurement, and communications teams across campus are prime candidates for AI automation. Lower stakes than student-facing applications, faster wins, and a good way to build institutional AI capacity before tackling more complex use cases.
AI can accelerate literature review, help structure grant proposals, and identify relevant funding opportunities. These are lower-risk applications with high ROI for faculty and research administrators, and good starting points for institutions building AI confidence.
Not an AI application, but perhaps the most urgent AI issue on most campuses. We help institutions develop clear, enforceable academic integrity policies that address AI-generated content in ways faculty can actually implement.
Governance only works if people understand it. We develop AI literacy training and change management programs designed for higher education audiences, from frontline staff to department chairs to academic leadership.
Governance Framework
Here's what we build into every higher education engagement to ensure AI is used responsibly across the institution.
We develop comprehensive AI use policies that cover academic integrity, student data, vendor assessment, research use, and staff guidance. Clear enough to enforce. Practical enough to follow.
Any AI system that affects student outcomes, from enrollment to advising to financial aid, requires explicit bias testing before deployment and ongoing monitoring afterward. We build this into the governance structure
Governance without education doesn't work. We build AI literacy programs designed for faculty, advisors, and administrative staff at different levels of technical comfort, so everyone understands the policies and can apply them.
We evaluate AI tools and systems in use against FERPA requirements: what student data is being accessed, how it's being processed, whether vendor agreements meet compliance requirements, and where gaps exist.
We help institutions evaluate AI vendors with a higher education lens: FERPA compliance posture, student data handling, training data use, and contractual protections. Most vendors don't volunteer this information.
We align institutional AI governance with NIST AI Risk Management Framework and ISO/IEC 42001, giving academic leadership an auditable, standards-based approach to managing AI risk across the institution.
Our Approach
Our three-phase approach is designed to move quickly without cutting corners on the governance requirements that matter most in higher education.
We assess your current AI landscape, compliance posture, and institutional readiness to build from a clear picture of where you actually are.
We build the governance structures and strategic roadmap your institution needs to manage AI responsibly at scale.
We help your institution execute the strategy and build the internal capacity to manage AI governance on an ongoing basis.
FAQ
Answers to the questions college and university leaders ask us before getting started.
Start with inventory and policy. Before you can make smart decisions about AI, you need to know what's already in use and establish a clear position on how AI should and shouldn't be used at your institution. These two things together give you a foundation to build from. Most institutions that feel overwhelmed haven't done the inventory step. Once you know what you're working with, the path forward gets clearer.
Yes, if those tools process student education records. FERPA applies to the institution, not just to specific systems it officially adopts. If a faculty member inputs student information into a third-party AI tool that hasn't been reviewed for compliance, that can create a FERPA issue for the institution. A big part of our higher education work involves helping institutions understand exactly where student data is flowing and building policies to manage it.
With a clear, enforceable, and consistently communicated policy. There's no technical solution to academic integrity in the AI era. Detection tools are unreliable and create their own problems. What works is a clear institutional policy that defines acceptable and unacceptable uses of AI in academic work, gives faculty practical guidance on how to apply it in their courses, and is communicated consistently to students. We help institutions develop these policies in ways that are practical rather than aspirational. Consistent adoption requires training, visible leadership use, and a culture that makes it safe to ask questions and learn. We build training programs designed for real estate workflows, not generic AI introductions.
Yes, when they're designed and used correctly. Predictive analytics can surface early warning signals for students at risk of stopping out. But the technology is only part of the equation. The institution has to have advisors and support staff who can act on what the data shows, a clear process for intervention, and a governance structure that ensures the AI isn't producing biased recommendations. We help institutions build all three, not just the model.
Involve them early, speak to their actual concerns, and make the policies workable. Faculty resistance to AI governance often isn't resistance to the idea of governance. It's frustration with policies that are developed without faculty input and then handed down from administration. We build governance development processes that include faculty voice, and we help institutions distinguish between governance (what the institution requires) and pedagogy (how individual faculty choose to incorporate or address AI in their courses).
A policy is a document. Governance is the system that makes the policy real. An AI policy states the institution's position on AI use. Governance is the committee structure, review process, training program, monitoring mechanism, and accountability structure that ensures the policy is actually followed. Institutions that only write a policy often find it doesn't change behavior. Institutions that build governance around the policy see lasting change.
Get Started
Let's talk about where your institution stands today and what responsible AI governance looks like in your specific context.