01
Score the operating foundation
- Give one point if each critical workflow has a named owner: admissions, student records, attendance, LMS delivery, compliance, and reporting.
- Give one point if staff know which system is the source of truth for student profile, grade, attendance, document, and communication records.
- Give one point if manual exceptions are logged instead of handled only through private messages or memory.
- Subtract one point for every workflow where AI would speed up a process that is currently undefined.
02
Score data safety and governance
- Confirm whether staff have written rules for student data, PII, transcripts, support notes, payment records, and admissions data.
- Identify which AI use cases are allowed, restricted, or banned before tools spread informally across departments.
- Require human approval for student-impacting decisions, compliance exceptions, record edits, and outbound institutional messages.
- Document which vendors, tools, prompts, and data categories are acceptable for pilot projects.
03
Score adoption and measurable value
- Choose one first AI pilot tied to a measurable operational problem, such as faster inquiry follow-up or cleaner missing-document review.
- Estimate staff hours currently spent on the workflow so leadership can judge whether automation is worth the change effort.
- Define the training, review cadence, and escalation path before rolling the workflow out to more staff.
- Track quality signals after launch: fewer missed leads, fewer record gaps, faster review queues, or lower manual reporting time.