Choice is Clear - Transform not Digitize

As I head to Alliance 2025 in New Orleans, I’m fired up for a conversation that will challenge us all. I’ll be leading a MainStage session on deploying AI and its impact on modernizing student systems (SIS) — a game-changing shift for higher ed.

We’ve been here before. The move from mainframes to ERP systems and the leap to the cloud were pivotal moments. But let’s be clear—the real choice isn’t just about technology. It’s about transformation.

AI isn’t just another tool; it’s an opportunity to rethink how campuses operate and serve students.

For decades, institutions have followed a traditional CRUD-based system implementation approach, where the focus is on replicating the past in a slightly shinier UX way. This process is long, expensive, and often leads to marginal improvements with massive costs.

On the other hand, AI-driven implementations will take an iterative, future-ready approach, ensuring faster adoption, higher efficiency, and a lower total cost of ownership—all while leveraging AI to enhance student experiences rather than just shifting legacy processes into the cloud.

Let’s break down the difference.

[Digitize] Traditional CRUD System Implementation

Slow, Expensive, and Just More of the Same

The traditional approach to implementing student systems follows a rigid, legacy mindset, often leading to multi-year projects that burn budgets without delivering true transformation.

CRUD System Implementation Steps

1?? As-Is Discovery – Consultants map existing processes, often focusing on how the legacy system works today rather than rethinking what’s possible.

2?? Fit-Gap Analysis – The team compares old processes to the new system’s capabilities, often forcing the new system to accommodate outdated workflows.

3?? Configuration of Legacy – Instead of designing future-ready experiences, this phase configures the new system to mimic old behaviors, reinforcing inefficiencies.

4?? Conversion of Legacy Data – Significant effort goes into migrating years of data, much of which is rarely needed or could be optimized for AI-driven workflows.

5?? Testing – Lengthy testing cycles focus on ensuring the new system behaves exactly like the old one, rather than testing how it could be improved.

6?? Deployment – After 17 to 24 months, the new system goes live, often with HR/Finance dependencies causing further delays.

CRUD Focused Cost & Timeline

?? Cost: $10M–$50M+

?? Timeline: 18–24 months (not including HR/Finance dependencies)

?? Ongoing Support: Requires continuous consulting and production support ($1M+ annually)

?? Licensing: $1M+ per year

The outcome?

The same processes with a new system, heavy third-party dependencies, minimal automation, and a huge cost with a slow ROI.

It’s a modernization effort that isn’t actually modern—just a lift-and-shift of what already exists.

[Transform] The AI-Driven Campus Transformation

Fast, Cost-Effective, and Impactful

Instead of recreating old workflows, AI-driven implementations start with a vision for student transformation and use AI to optimize, automate, and modernize campus operations.

AI-Driven Implementation Steps

1?? Vision – Define student-first outcomes before touching any technology. AI-driven campuses prioritize engagement, automation, and self-service.

2?? ThinkSpaces – Bring together key stakeholders to map student journeys, focusing on friction points that AI and automation can solve.

3?? AI Labs – Pilot real use cases before committing to large-scale system changes, ensuring every new feature delivers immediate value.

4?? Use Case Blueprint – Design a modular roadmap, allowing iterative deployment of AI-driven experiences, rather than waiting years for full implementation.

5?? AI-Driven Experiences – Deploy student-centered digital interactions, replacing outdated portals with AI-powered chat, self-service workflows, and proactive engagement.

6?? AI-Driven Engagement – Instead of static student records, AI proactively identifies risks, nudges students, and personalizes outreach.

7?? AI-Driven Insight – Move from reactive reporting to real-time AI insights, allowing leadership to predict enrollment trends, retention risks, and operational inefficiencies.

8?? Workflows & Automation – AI eliminates manual processes, reducing staff workload and automating everything from financial aid verification to advising recommendations.

9?? Iterative, Rapid Adoption – Implement in small, high-impact iterations instead of waiting years for full deployment. AI-driven workflows evolve organically rather than being locked into rigid configurations.

AI-Driven Cost & Timeline

?? Cost: $250K–$2M for implementation

?? Timeline: Rapid, iterative adoption

?? Annual Cost: $250K–$500K (a fraction of CRUD licensing & support costs)

?? Immediate ROI: Reduces manual workloads, eliminates third-party dependencies, and improves student experiences instantly.

The outcome?

Streamlined, intelligent processes with real transformation, reduced third-party dependencies, automation at scale, and a lower cost with faster ROI.

Why Higher Ed Leaders Must Choose to Transform

?? If your institution is still following the CRUD implementation model, it’s locking itself into another decade of inefficiency.

?? AI-driven implementations are cheaper, faster, and focused on transformation rather than just replacing old systems with newer versions of the same thing.

?? The institutions that adopt AI workflows now will set the standard for student experiences, operational efficiency, and long-term sustainability.

So, are you going to spend years and millions replicating the past, or are you ready to build a future-ready student system powered by AI to transform your campus?

The answer will determine whether your institution leads or lags behind in the next era of higher ed.