Artificial Intelligence (AI) has quickly gone from buzzword to a daily campus leadership discussion. In higher education, campus leaders are already asking how it might reshape admissions, student success, classroom learning, and institutional operations. Meanwhile, faculty and staff are wondering what it means for their day-to-day work. 

For those of us who serve higher ed through technology and data solutions, AI represents both an exciting frontier and a call for responsibility. As a vendor who sits at the core of our customers’ data ecosystem, we see firsthand how essential it is to understand the foundations of AI before diving into applications. This post is meant as a primer on the key concepts, opportunities, and challenges AI brings to our work so that future discussions can build on a shared language. 

What Is AI, Really? 

The definition of AI shifts depending on who you ask: smarter searches, chatbots, even cars that drive themselves. Here’s how we break it down in higher ed terms: 

• Artificial Intelligence (AI): A broad field where computers simulate human intelligence tasks like reasoning, learning, or problem-solving. Example: Think of AI as teaching computers to act like people do in certain situations, like figuring out what a student is asking in a help desk chat and pointing them to the right office.
   

• Machine Learning (ML): A subset of AI where systems “learn” from data using algorithms designed to improve their performance over time by observing data. Example: An algorithm that analyzes past enrollment and GPA trends to flag students who may need additional advising before midterms.
   

• Generative AI (GenAI): Tools that don’t just analyze but create text, images, even code. Large Language Models (LLMs) are a type of generative AI trained on massive volumes of data in order to understand, produce, and interact with human language. OpenAI ChatGPT, Microsoft Copilot, or Google Gemini are a few GenAI LLMs that you may have already interacted with on your campus. Example: An instructor uses a GenAI tool to draft quiz questions aligned with a syllabus, then refines them for classroom use.
   

• Agentic AI: A model or system designed not only to generate outputs, but to take actions in iterative steps toward goals by making decisions, planning steps, and adapting to feedback. Example: During registration, an AI system predicts over-enrollment in a course and recommends opening a new section.
   

• AI Agents: Software entities powered by AI that can autonomously perform tasks (e.g., querying data, sending notifications, or orchestrating workflows) while interacting with other systems or users. Example: An AI agent pulls a daily retention report, summarizes key changes in plain language, and emails it to the provost’s office. 

These definitions matter, because they set expectations. Most of the AI you interact with today is just smart pattern recognition, even with cutting-edge LLMs and agents. Nevertheless, these systems can still be highly useful in many areas at your institution. 

What Are Some Ways We Can Use It? 

AI is finding practical applications across campus. We’re already seeing impactful use cases in areas like advising, productivity, and reporting. 

• Student Success: Identifying at-risk students earlier, scaling advising support, offering 24/7 tutoring through AI chat. 

• Administrative Efficiency: Automating repetitive tasks like scheduling, financial aid verification, or document processing so that staff can focus on higher-value work. 

• Academic Innovation: Personalizing course content, helping researchers analyze large datasets, enabling new forms of publishing and collaboration.  

• Reporting & Analytics: Transforming reporting from pre-built dashboards to conversational queries. 

The common thread? Data. Whether predictive or generative, AI depends on data quality, accessibility, and context. That means investing in clean data and clear governance should be a critical part of your reporting and data integration strategy to make the most of these possibilities.   

The Challenges We Need to Face Together 

It’s easy to get swept up in the promise of AI, but higher education also faces unique risks and responsibilities: 

• Data Privacy & Compliance: FERPA, GDPR, and emerging state regulations all apply to the AI space. AI tools must respect strict data boundaries, and the vendors behind them must deeply understand how to operate in those constraints. 

• Equity & Bias: Algorithms reflect the data they’re trained on. If we’re not careful, AI can unintentionally reinforce inequities in admissions or advising. Keeping humans in the interaction loop is critical. 

• Adoption & Change Management: Even the best AI won’t succeed if faculty and staff don’t trust it or know how to use it. 

• Integration with Existing Systems: Higher ed IT ecosystems are complex with everything from SIS, LMS, CRM, HR, Finance to reporting platforms and more. AI solutions must bring value to these systems without creating new silos. 

These challenges underscore why adopting AI isn’t just about selecting one new tool. It’s about strategy, governance, and partnership and understanding how AI impacts the business processes you care most about. 

Closing Thoughts 

This blog is just a starting point. Over the coming months, we’ll explore deeper questions: 

• How should institutions think about AI-related risks and responsibilities regarding security & compliance?  

• What does AI-powered reporting really look like in practice? 

• How can institutions evaluate vendors’ AI claims with confidence? 

• What is our view on the future of AI and reporting? 

Along the way, we’ll share more about our own path, which encompasses not only AI but the modernization of our entire platform. 

Our goal is to be a partner in this journey, not just by providing tools, but by helping higher education leaders navigate the opportunities and challenges of AI thoughtfully.