From Data to Decisions: Building AI-Ready Institutions in Higher Education

Conversations about AI in higher education so far have remained surface-level, with a focus on chatbots, copilots, and futuristic sim lab classroom experiences. In a recent episode of EdTech Connect: Innovators in Conversation, I met with Sam Burgio, President and COO at Jenzabar, to discuss what should really be at the root of AI implementation decisions for higher education: data, trust, and decision-making.

Institutions do not become AI-ready by adopting tools into their toolkit. They become AI-ready by building the foundations that allow data to meaningfully inform decisions.

Why Is Higher Education Moving Beyond Static Reporting?

Traditionally, higher education has relied on dashboards that just explain what has already happened, such as enrollment numbers, retention rates, and budget summaries. Today, institutions face a different reality.

Many colleges, universities,  and smaller institutions are operating under increased financial pressure and board pressure. Their leadership teams are being pushed to make faster, more consequential decisions with limited resources. This is in addition to trying to meet student, the board’s, and workplace demands to incorporate AI into their workflows and curricula. These shifts are pushing data from just a reporting function into a strategic one.

This is where predictive insight, understanding what will happen. However, that shift only works when data is centralized, trusted, and continuously refreshed. Static dashboards alone cannot support this high-performing kind of decision-making.

AI Runs on Data and Institutions Are Feeling That Gap

AI is only as powerful as the data behind it. Personalization, predictive analytics, and decision automation all depend on reliable data and data infrastructure.

Many institutions are eager to “use AI,” but without consistent pipelines, validated sources, and repeatable analytics, AI initiatives stall or produce unreliable results. Predictive models for retention or enrollment do not succeed because they are clever and look pretty. They succeed because the underlying data is accurate, timely, and complete. They are an ongoing operational commitment.

Why ROI Has Entered the Higher Ed Chat

The term ROI is usually associated with business and is something higher education rarely discussed in the past. Today, that is no longer true.

Presidents, CFOs, and cabinet leaders are now asking hard questions about return on investment. Not every ROI is purely financial, but institutions are increasingly expected to justify spending through measurable outcomes.

Data plays a central role here by enabling accurate value attribution. Decision-makers need to be able to quickly understand which channels drive enrollment, which interventions improve retention, and which investments deliver results.

What AI Readiness Actually Means

Despite all of the AI talk, AI readiness is still only roughly defined at an industry level. According to higher education leaders, AI readiness derives from:

1. Clear Policies and Governance
Institutions need defined guardrails around data use, privacy, and AI applications. Without them, initiatives stall in analysis paralysis or expose institutions to unnecessary risk.

2. Trusted Data Infrastructure
Data pipelines must be explainable, auditable, and current. Leaders and staff will only trust AI-driven insights if they trust where the data comes from and how it is validated.

3. Clear AI Training and Adoption
Even with natural language style querying and AI-assisted analytics, institution staff need to understand what questions they can ask, where the data lives, and how much trust and confidence to place in the AI-generated answers.

Institutions should resist chasing futuristic, sci-fi-esque discussions. AI adoption works best when it starts with achievable, operational use cases, often in administrative and data-driven areas, before expanding outward.

Operating Like a Business Without Losing the Mission

Higher education must find a balance between operating more like a business to survive, without compromising its mission and tradition. Efficiency, accountability, and proactive decision-making do not diminish education’s purpose. They protect it. AI, when backed and built by trusted data and aligned leadership, becomes a tool for sustainability and impact, rather than disruption.

Institutions should focus less on visible AI features and more on the data foundations that drive real outcomes. In higher education, the path from data to decisions is not just about moving faster. It is about moving deliberately, to create a positive impact, with trust and clarity.