In the rush to adopt artificial intelligence, many institutions are making a critical mistake: assuming that off-the-shelf AI solutions will seamlessly integrate into their unique academic environments. This oversight undermines the very essence of what makes each institution distinct and valuable.

Higher education stands at a unique crossroads. Our institutions possess three powerful advantages that make us ideally suited to shape AI implementation:

• Deep expertise in learning science and pedagogy,
• A fundamental commitment to inclusion and accessibility, and
• Vast repositories of specialized knowledge across disciplines.

Consider this: Every institution has its own distinctive DNA—unique terminology, specific policies, particular processes and individualized pathways for student success. A campus chat bot trained on generic data can’t possibly understand that your first-year experience program is called Launch Pad or that your student success center is actually The Hub. These aren’t just semantic differences; they reflect your institution’s culture, values and approach to education.

The stakes are high in an era of contracting budgets, unpredictable enrollment patterns, information overload, and increasing student needs. We cannot afford to misallocate our most valuable resource: human talent.

The Real Power of Properly Trained AI

When AI is trained with your institution’s specific context, it becomes more than a cost-cutting tool. It becomes a force multiplier that:

• Handles routine queries with institutional accuracy,
• Identifies at-risk students before they struggle,
• Directs resources where they’re needed most, and
• Frees staff to focus on meaningful student interactions

AI transforms our approach from broadcasting general information to providing targeted support. Imagine AI that recognizes your unique early alert indicators, understands your specific financial aid processes, knows your specific mental health resources and protocols, and speaks in your institution’s voice and values.

Relying on vendor-trained AI means that you are missing crucial institutional context, perpetuating generic solutions and losing opportunities for personalized support or potentially misguiding students with incorrect information.

Higher education institutions must take an active role in training their AI systems. Remember: Every time you allow an untrained or generic AI to interact with your students, you’re missing an opportunity to provide the personalized, institution-specific support that sets your school apart.

Breaking It Down

A generic AI is like a new employee who has read every manual but doesn’t understand your institution’s unique culture, language or processes—it has broad knowledge but lacks specific context. Untrained AI systems, while powerful in general applications, are essentially operating on publicly available information without the benefit of the institutional expertise, proprietary processes or specific student success patterns that make your organization unique.

Fear of Failure

The fear of AI implementation manifests in various ways across higher education, often masquerading as practical concerns while hiding deeper anxieties. Like an untrained AI system that lacks institutional context and produces generic responses, an unprepared organization can generate resistance that undermines successful AI adoption.

• Process guardians: These experienced professionals, while openly complaining about overwhelming workloads, harbor deeper concerns. They worry that AI might not just streamline their processes but potentially replace their expertise. Their resistance often appears as skepticism about AI’s accuracy or reliability—a valid concern that actually points to the need for proper AI training rather than AI avoidance.
• Generational tensions: Some view AI adoption through a generational lens, suggesting that retirement is the solution to resistance. This perspective misses a crucial point: Seasoned professionals possess valuable institutional knowledge that should be captured and used to train AI systems, not lost to retirement. Their experience isn’t an obstacle; it’s an asset for effective AI implementation.
• Faculty concerns: In academia, faculty members wield significant influence in approval processes. Their hesitation often stems from legitimate concerns about academic integrity and the quality of education. However, this anxiety about reopening settled decisions can be addressed through proper training and demonstration of how AI can enhance, rather than diminish, academic rigor.

The Bottom Line

In higher education, we don’t just need AI—we need AI that understands our individual institutional contexts, speaks our unique language and supports our specific student success goals. This level of customization only comes through intentional, institution-specific training.

Our mission isn’t just to adopt AI; it’s to shape it into a tool that authentically represents and serves our individual institutions and students. The time and resources invested in proper AI training today will pay dividends in more effective, personalized student support tomorrow.

The choice is clear: Either train AI to truly understand and represent your institution, or watch as generic solutions fail to meet your unique needs and challenges. In an era where personal attention matters more than ever, can we afford to leave this critical tool untrained?