Navigating AI Vendor Selection in Higher Ed

About The Episode: In this episode, we tackle a timely issue for our higher education friends: how to choose the right AI vendor. As you enter planning mode and vet potential partners, we share our insights on navigating the good and bad of AI vendor selection. We discuss the challenges of implementing emerging AI technologies, where possibilities are vast but proven successes are still unfolding. Using real-world examples, including a recent case from the Los Angeles Unified School District, we offer practical advice on what to consider when selecting AI partners. Whether you're launching new projects or expanding existing ones, this episode will help you make informed decisions in the rapidly evolving world of AI in higher education.

‍

Key Takeaways

• Beware of Hype: Avoid falling for buzzwords; instead, demand transparency about the vendor’s capabilities and past successes.
• Proven Track Records Matter: Vendors should have verifiable experience and references from other institutions.
• Budget for Hidden Costs: AI projects often involve unforeseen expenses such as integration, infrastructure, and maintenance.
• Start Small with POCs: Proof of concept (POC) projects are vital for validating AI solutions before scaling.
• Focus on Data Readiness: Ensure data quality, accessibility, and privacy compliance before committing to AI deployments.
• Plan for Change Management: Successful AI integration requires organizational transformation and internal buy-in.

‍

Episode Summary

The LAUSD Chatbot Case: An Ambitious Project Gone Wrong

LAUSD partnered with AllHere Education to develop a personalized chatbot for students. However, the vendor’s financial collapse led to project failure. The root causes included overambitious scope, poor integration with existing systems, and a lack of readiness for the project’s scale.

Key lessons from the failure:

1. Scope Realistically: Attempting to integrate multiple complex systems simultaneously is risky.
2. Vendor Viability: Evaluate the financial stability and scalability of potential partners.
3. Test Before Scaling: Starting with a limited deployment could have highlighted challenges early.

‍

8 Pitfalls to Avoid When Selecting AI Partners

1. Falling for the Hype

AI is trending, but not every vendor delivers real value. Insist on demonstrations and evidence of how AI will address your specific needs.

2. Overlooking Track Records

Choose partners with a proven history of successful AI implementations. Ask for client references and examine case studies.

3. Ignoring Hidden Costs

AI projects can involve:

• Compute Costs: Running large language models can be expensive.
• Integration Costs: Connecting AI to CRMs or student information systems adds complexity.
• Maintenance Costs: Post-launch updates and debugging are ongoing expenses.

4. Neglecting Data Readiness

AI solutions rely on quality data. Before committing, ensure:

• Adequate Training Data: Sufficient volume and diversity to train models effectively.
• Data Integrity: Clean, structured, and well-organized data.
• Privacy Compliance: Adherence to FERPA and other regulatory standards.

5. Skipping Proof of Concept (POC)

POCs validate the feasibility of AI solutions. A successful POC requires:

• Defined Success Metrics: Clear goals to measure effectiveness.
• Narrow Scope: Focused objectives to test critical components.
• Time Boundaries: Short timelines (e.g., 30–60 days) to deliver results efficiently.

6. Misjudging Integration Complexity

Integration with existing systems (e.g., CRM, SIS) is often underestimated. Ensure the vendor has expertise in building robust integrations and scaling solutions.

7. Failing to Assess Vendor Transparency

Understand how vendors implement AI:

• Do they own the core technology or rely on third-party platforms?
• Can they explain their AI workflows clearly?

8. Underestimating Change Management

AI adoption often requires rethinking workflows, staff roles, and student experiences. Institutions must invest in training and communication to drive successful transformation.

‍

How to Approach AI Projects in Higher Education

• Start Small: Pilot AI in a specific department or workflow to build confidence and gather insights.
• Plan for Transformation: Treat AI as a catalyst for change, not just a quick fix for inefficiencies.
• Work with Partners, Not Just Vendors: Look for collaborative relationships where the vendor invests in your success.

‍

Final Thoughts

The failure of LAUSD’s chatbot project underscores the importance of careful planning, realistic expectations, and choosing the right AI partner. By avoiding common pitfalls and focusing on transparency, scalability, and organizational readiness, institutions can unlock the transformative potential of AI.

‍

Connect With Our Co-Hosts:
Ardis Kadiu

https://www.linkedin.com/in/ardis/
https://twitter.com/ardis

Dr. JC Bonilla

https://www.linkedin.com/in/jcbonilla/
https://twitter.com/jbonillx

‍

About The Enrollify Podcast Network:
Generation AI is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!  Some of our favorites include The EduData Podcast and Visionary Voices: The College President’s Playbook.

Enrollify is produced by Element451 — the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com.