Turn AI strategy into capability your school can demonstrate.
Business schools are moving quickly on AI. Faculty are changing courses, students are adopting new tools, and leadership teams are setting new expectations. The next challenge is knowing whether those efforts are producing the student capability, course quality, and learning evidence the school intends.
Navigate AI gives business-school leaders an independent view of what has been built and what the current evidence can support. From that baseline, the school can strengthen the parts that need attention and document improvement over time.
Implementation has advanced further than the evidence.
Strengthen the measure, preserve the process, and improve the inference.
How do you know the work is producing capability?
An AI plan can generate visible activity quickly. Workshops, policy changes, course redesigns, and new tools can all create meaningful progress. Leadership eventually needs a different kind of answer: what students can actually demonstrate, how consistently courses support that capability, and what evidence the school can use to judge progress.
Institutional activity
Faculty experiments, policy changes, new tools, and program initiatives establish momentum.
Defined capability
The school clarifies what students should be able to do and where those capabilities develop.
Credible evidence
Direct measures, course artifacts, and improvement records show what the work is producing.
Separate what has been implemented from what can be substantiated.
Institutional progress and institutional evidence rarely develop at exactly the same pace. A school may have strong faculty practice that has never been documented beyond individual courses. It may also have polished policies or competency statements that have not yet produced consistent student evidence.
Navigate AI examines implementation maturity and evidence strength separately. That distinction helps leadership see where the school has already built meaningful capability, where the evidence still needs to catch up, and where a more fundamental design problem remains.
The result is a clearer basis for the next decision. Leadership can see whether the priority belongs in curriculum design, a course, a direct measure, faculty support, or the next improvement cycle.
“We have a lot happening.”
- Faculty are experimenting
- Policies have been updated
- Courses are changing
- Students are using AI widely
“Here is what we can support.”
- Defined scope and reviewer-rated maturity
- Named artifacts tied to specific claims
- Direct student evidence where available
- Prioritized evidence lag and improvement needs
AI capability now belongs inside the learning-evidence conversation.
AACSB’s 2026 Global Standards give business schools an important quality context. Digital agility now sits alongside established expectations for learning competencies, assessment, and continuous improvement. The practical question is broader than accreditation: can leadership explain what AI capability means for its programs and support that story with credible evidence?
Navigate AI helps schools prepare that evidence and strengthen the underlying educational system. Accreditation judgments remain with AACSB and the peer-review process.
Establish what the school has built and what the evidence supports.
The AI Capability & Evidence Diagnostic reviews six connected areas of institutional practice. It evaluates implementation maturity and evidence strength separately, identifies evidence lag, and turns the findings into a prioritized twelve-month roadmap.
Know where the next decision belongs.
The school receives a verified baseline and a named inventory of the evidence behind it. The final package also identifies the highest-priority gaps, connects them to relevant quality expectations, and sets out a twelve-month roadmap. Together, those deliverables show leadership where to strengthen learning evidence, where to build missing capability, and where existing work is already stronger than the documentation suggests.
See the full Diagnostic →Begin with the fifteen-item Pulse Check.
The free tool provides a provisional orientation across the same six domains. It can help leadership identify which questions deserve closer examination before scheduling a full evidence review.
Take the Pulse Check →Strengthen the evidence behind student learning.
The strongest Navigate AI engagements focus on the places where AI changes what a final artifact can tell us about learning. That includes course quality, direct assessment, student capability, and the evidence leadership uses to judge improvement.
| Leadership question | What Navigate AI examines | Typical output |
|---|---|---|
| What should students be able to do with AI? | Capability definitions, developmental progression, curriculum placement, and evidence expectations. | AI-Ready Graduate map and evidence portfolio architecture |
| Do priority courses still produce trustworthy learning evidence? | Course purpose, AI expectations, learning design, assessment evidence, verification, and human oversight. | CourseReady AI scorecard and redesign plan |
| Can our direct measures still support the inference we need? | Competency, task design, process evidence, rubric logic, findings, and reassessment. | AI Assessment Evidence Sprint package |
| Can leadership substantiate the institutional story? | Implementation maturity, evidence strength, artifact quality, scope, and evidence lag. | Capability & Evidence Diagnostic |
| Can we show improvement over time? | Updated artifacts, direct measures, decisions, and year-over-year movement. | Annual AI Capability & Evidence Review |
Strengthen the part of the system that matters next.
AI Assessment Evidence Sprint
Redesign a direct measure so student reasoning, verification, and judgment remain visible when AI is available.
Strengthen assessment evidence →CourseReady AI
Review priority courses for purposeful AI use, clear expectations, valid assessment, and appropriate human oversight.
Review a priority course →AI-Ready Graduate
Turn student AI capability expectations into curriculum progression and observable evidence across business programs.
Build the capability map →Faculty AI Fluency Studio
Build faculty capability when the evidence shows a need for stronger course practice, shared expectations, or implementation support.
Explore the Studio →Annual AI Capability & Evidence Review
Refresh the evidence, document movement, and set the next improvement cycle after the initial work is complete.
See the annual review →Let the evidence set the sequence.
A short briefing can clarify whether the school needs an independent baseline, a focused assessment or course review, or a capability-building engagement.
Request a briefing →Move from evidence to the next measurable result.
Verify
Review implementation, examine artifacts, and identify the evidence gap.
Define
Clarify the student capability, quality expectation, or assessment inference that matters.
Strengthen
Improve the course, assessment, curriculum, or faculty practice tied to that priority.
Measure
Gather direct evidence and use it to judge the result.
Improve
Document what changed and set the next cycle’s priorities.
Focused on the gap between AI adoption and demonstrable capability.
Navigate AI draws on an ongoing research program examining student readiness, trust, verification, disclosure, human review, oversight, and demonstrated judgment. That work informs the questions we ask about capability and evidence while institutional pilots establish the separate client evidence base.