Strategy & Leadership
2 questions
AI vision tied to business outcomes; exec sponsorship; budget; AI ownership; clear business alignment
Our institution has a written AI strategy endorsed by the President, Provost, or Board.
AI initiatives have a named senior sponsor (e.g., Provost, CIO, VP for Strategy) with dedicated funding outside of routine IT operations.
Use-Case Portfolio
2 questions
Inventory + prioritization; opportunity matrix; ROI cases; AI-type fit
We maintain an inventory of candidate AI use cases classified by risk tier (administrative / teaching-and-learning / research) and by stakeholder (students / faculty / staff).
Each candidate use case has a documented owner (academic unit or business owner) and a measurable value hypothesis (advising contacts, application throughput, retention, time saved).
Data Foundations
2 questions
Quality, accessibility, lineage, governance, integration, labeling
Institutional data (SIS, LMS, CRM, advancement, financial aid) is accessible for AI workflows through documented extracts, a warehouse, or integration patterns.
We have a data governance program with named stewards, a data catalog, and quality metrics for the datasets that would feed AI use cases.
Technology & Infrastructure
2 questions
Cloud, APIs, integrations, security architecture, MLOps/LLMOps, model hosting
We have a defined approach for AI workloads — cloud strategy, model hosting, single-sign-on identity, LMS/SIS integration patterns — endorsed by central IT.
Research compute capacity (GPUs, secure enclaves) and shared services for faculty AI research are documented and accessible.
Security & Privacy
2 questions
Information security, data protection, vendor/3rd-party risk, ePHI / FERPA / PII controls
All AI vendors processing student records are covered by current Data Processing Agreements (DPAs) and qualify as "school officials with legitimate educational interest" under FERPA.
We have documented data classification (public, internal, restricted, FERPA, HIPAA) and route AI use cases through a security and privacy review before deployment.
Talent & Culture
2 questions
AI literacy, skills, training, change management, adoption appetite, resistance risk
AI literacy resources (workshops, syllabi templates, acceptable-use guidance) are available to faculty, staff, and students.
We have faculty champions or a faculty governance body actively shaping how AI is used in teaching, learning, and research.
Process Maturity
2 questions
Workflows documented; repetitive / high-friction / measurable tasks identifiable
Core administrative workflows (admissions, registration, advising, financial aid, IT helpdesk) targeted for AI are documented with cycle-time baselines.
We have change-management practices for technology rollouts that include faculty/staff feedback loops, training plans, and rollback triggers.
Governance, Risk & Responsibility
2 questions
Policies, AI inventory, risk tiering, human oversight, bias testing, explainability, audit trails
An AI governance committee with academic, IT, security, legal, and student-affairs representation reviews AI use cases before deployment.
Institutional policies exist for AI in academic integrity, syllabus disclosure, AI in admissions/financial aid, and research-IRB implications of GenAI.
Vendor & Procurement Readiness
2 questions
Build/buy/partner decisioning; vendor DD; contract templates; BAAs/DPAs; tool sprawl control
Vendor due-diligence includes algorithmic transparency, training-data disclosure, and accessibility (WCAG/Section 508) — not just security and price.
Procurement and IT contracts include FERPA-compliant data-use clauses, AI-training prohibitions, exit/data-deletion clauses, and accreditor-disclosure considerations.
Implementation Capacity & Operations
2 questions
Can the org actually pilot, buy, build, deploy, train, monitor? Post-deploy monitoring; shadow-AI discovery
We have a track record of moving pilots from prototype into operational use, with explicit go-live criteria and post-launch evaluation.
Production AI is monitored for drift, bias/equity (especially in admissions/financial aid), and shadow-AI use (faculty/staff using personal LLM accounts on student work).