Before The Mandate: A Business School Case For Philosophy First

Disclosure: The authors used Claude (Anthropic) as a collaborative drafting and editing tool in the development of this piece. Specific tasks included structural suggestions and prose drafting. All arguments, institutional details, and editorial judgments reflect the authors’ own expertise and professional experience. The authors reviewed, revised, and approved all content prior to submission.

Something significant is happening in business education right now, and the pace of it is worth both celebrating and examining. Across the country, business schools are launching AI-focused degrees, mandating faculty training, deploying custom tools, and redesigning curriculum at a speed that would have seemed implausible just a few years ago.

We have watched these developments with genuine admiration. While the acceleration of AI adoption is commendable, it also introduces variability in how institutions define “AI readiness,” creating a landscape where capability is often conflated with tool exposure rather than demonstrated competence. The institutions moving with urgency are responding to a real shift, and the ambition behind their strategies reflects a healthy seriousness about preparing graduates for a world that is changing faster than any traditional planning cycle can capture. At GCU, we have intentionally framed AI not as a standalone initiative but as an embedded institutional capability, ensuring that adoption is tied to measurable learning outcomes rather than reactive implementation cycles.

We want to add a perspective to that conversation, not as a counterpoint but as a complement. At Grand Canyon University’s College of Business, we have been doing much of the same work: building AI tools, training faculty, expanding programs, and integrating AI across our curriculum. What we want to share is not what we did, but what we did first, and why we think that sequence matters for every business school navigating this moment. Our approach has also included developing internal structures that align AI integration with program design, faculty development, and institutional effectiveness processes, ensuring consistency across modalities and disciplines. This alignment allows AI integration to scale without fragmenting the student experience, a risk that can emerge when adoption occurs independently at the course or faculty level.

THE QUESTION BEHIND THE STRATEGY

Before we built anything, we asked a question that is easy to defer when the pressure to act is high: What do we actually believe a business education is for? That sounds like a philosophical luxury. In practice, it turned out to be the most strategic thing we did.

Business schools have always faced a tension between preparing students for the workforce as it currently exists and developing the kind of judgment, character, and adaptability that serves graduates across a career. AI has made that tension sharper and more immediate. When a tool can generate a competent financial analysis, draft a market entry strategy, or produce a polished client presentation, the question of what we are actually developing in students becomes impossible to sidestep. We found that answering it honestly changed what we built and how we built it. This philosophical anchoring enabled us to define AI as an enabler of higher order thinking rather than a replacement for foundational skill development, which has become a key differentiator in our curriculum design. It also positioned us to proactively address academic integrity, not as a compliance issue, but as a design challenge rooted in how learning is structured.

Our answer, shaped by GCU’s identity as a values-driven institution, centered on a conviction that business education at its best develops the whole professional: someone with technical competence, yes, but also the ethical reasoning, relational capacity, and discernment to deploy that competence well. AI, in our framework, is a powerful tool that can either serve that development or quietly undermine it, depending entirely on how it is integrated. That distinction drove every subsequent decision. In practice, this has led us to redesign assignments and learning activities to emphasize synthesis, judgment, and decision-making under ambiguity, areas where AI can support but not replace human capability.

HOW PHILOSOPHY BECAME PRACTICE

It shaped how we approached faculty development. Rather than training faculty primarily on specific platforms, we invested in helping them reason about when and how AI use serves their students’ genuine learning and when it substitutes for it. A faculty member who understands the purpose of an assignment is equipped to make good decisions about AI regardless of which tools are available. One who has only been trained on today’s tools will need retraining when those tools change, which they will.

It shaped how we built our assessment practices. The question we ask when student work raises concerns is not whether AI was involved. It is whether authentic learning occurred. That shift is not merely semantic. It changes the entire orientation of the faculty-student relationship around academic work, moving it from surveillance toward genuine educational inquiry. This shift has also informed our broader assessment strategy, where we are exploring new indicators of authentic learning. By focusing on evidence of learning rather than tool usage, we are better positioned to assess student achievement and the demonstration of competency.

It shaped the tools we chose to build internally. Each custom AI tool at GCU was designed to serve a specific learning goal rooted in a specific educational commitment, rather than to automate a generic function. The question we brought to every tool decision was whether it would make our graduates more capable of the kind of work we are actually trying to develop in them. Our internal tool development has prioritized use cases that enhance, rather than bypass, critical thinking. This ensures that AI functions as a learning amplifier rather than a shortcut, reinforcing the competencies we expect graduates to demonstrate in the workforce.

URGENCY & CONVICTION AREN’T MUTUALLY EXCLUSIVE

None of this means business schools that began with training mandates and new degrees made a mistake. Urgency is appropriate. The workforce demands are real, and the institutions that have moved quickly deserve credit for taking the moment seriously. What we are suggesting is that the philosophical work and the programmatic work are not in competition. They are in sequence. The institutions that will sustain their AI leadership over time are the ones that can explain not just what they are doing, but why, in terms that connect to a coherent set of beliefs about what business education is for.

That explanation matters more than it might initially seem. It matters for faculty, who are more willing to engage deeply with AI integration when they understand the reasoning behind it rather than simply following a mandate. It matters for students, who develop genuine AI literacy when they understand the purpose behind their training rather than just acquiring platform proficiency. It matters for accreditors, employers, and prospective students, who are increasingly sophisticated about the difference between institutions that have a strategy and institutions that have a plan to keep up.

Most of all, it matters for the next wave of AI development, which will look different from this one. Business schools whose strategies are grounded in durable educational convictions will have a framework for evaluating whatever comes next. Those whose strategies are calibrated primarily to today’s capabilities will face the same decision point again, under greater pressure, with less time. This forward orientation reduces the need for disruptive overhauls and instead supports continuous, iterative improvement.

At GCU’s College of Business, we are proud of what we have built. We are more proud of why we built it. Our ongoing focus is to translate this philosophy into measurable outcomes, including student success metrics, employer feedback, and demonstrated competency in AI-augmented work environments. We share this perspective not because we think the rest of the field is moving in the wrong direction, but because we believe the conversation about AI in business education is richer when it includes not just what schools are doing, but what they believe about the education they are providing. That question is worth asking before the mandate goes out, and worth revisiting long after.

Ed Slover is Dean of the Colangelo College of Business at Grand Canyon University. B. Jean Mandernach, Ph.D., is Executive Director of the Center for Educational Technology and Learning Advancement at Grand Canyon University.

© Copyright 2026 Poets & Quants. All rights reserved. This article may not be republished, rewritten or otherwise distributed without written permission. To reprint or license this article or any content from Poets & Quants, please submit your request HERE.