Implementing AI Across the Institution
Practical Strategies for Adoption, Capacity Building, and Campus Implementation
Dr. Kollin Napier · Chief Artificial Intelligence Officer, Mississippi Artificial Intelligence Network (MAIN)
Session Materials
Get the session materials
Use the worksheets and reference sets from the live session to evaluate opportunities, assess readiness, engage stakeholders, and plan practical next steps. All materials are also available in the conference app under the Implementing AI session.
Activity 1 · 9:45 AM
Use-Case Prioritization Template
Score your AI use cases on value, feasibility, risk, and mission fit, then post your table’s top pick.
Outcomes 1 and 4
Readiness · 10:35 AM
Institutional Readiness Self-Assessment
Rate your campus across seven readiness dimensions, from leadership and culture to governance.
Outcome 2
Activity 2 · 11:00 AM
Stakeholder Engagement and Capacity-Building Worksheet
Map the stakeholders whose support you need, spot the biggest capacity gap, and choose two change-management moves.
Outcomes 2, 3, and 4
Plan · 11:30 AM
One-Page Campus Implementation Plan
Pull your work into one page: priority use case, readiness, stakeholders, near-term steps, responsible practices, and measures of progress.
Outcomes 4 and 5
Reference · both activities
Worked Examples
The completed Riverbend State set: scored use cases and a full stakeholder map to work from.
Completed sample set
Download PDFTake home
Post-Session Resource List
Required readings, accreditation and readiness references, and links to MAIN courses, policy guides, and prompting guides.
Outcome 5
Download PDFAbout this session
A working session on AI implementation
This applied session helped institutional leaders move from AI interest to action through four connected decisions: where AI can add value, whether the institution is ready, who must be engaged, and what to do next.
Who it was for
Academic administrators, assessment leaders, institutional effectiveness professionals, and campus teams responsible for responsible AI adoption.
What we worked through
Use-case prioritization, institutional readiness, workforce capacity, stakeholder engagement, change management, and implementation planning.
What participants took home
Reusable worksheets, reference guides, implementation prompts, aggregated Activity 1 results, and the complete session slides.
Learning Outcomes
What you will be able to do
- 1Identify high-value opportunities for AI adoption across academic, administrative, assessment, and student support functions within their institutional context.
- 2Evaluate institutional readiness factors that influence successful AI implementation, including leadership, culture, workforce capacity, infrastructure, resource constraints, and change management.
- 3Apply practical strategies for stakeholder engagement, faculty and staff development, training, and change management that drive effective AI adoption.
- 4Design campus-specific implementation approaches that include priority use cases, near-term action steps, responsible practices, and measures of progress.
- 5Develop strategies for implementation on their campus, including evaluation considerations, scaling plans, and a tailored next-step framework.
Post-session resources
Session results and slides
Review the combined Activity 1 findings and download the complete presentation in the format that works best for you.
Activity 1 results
Executive Summary
Review the aggregated use-case findings, priority patterns, recommended pilot portfolio, and practical next steps.
Aggregated session results
Download WordPresentation
Session Slides
Download the complete Implementing AI Across the Institution presentation deck.
PowerPoint and PDF
Keep Learning
Keep learning with MAIN
Contact
Questions or follow-up?
Contact Dr. Kollin Napier, Chief Artificial Intelligence Officer for the Mississippi Artificial Intelligence Network.