The composite reads Critical because it measures only the public surface. The decision-relevant truth: of four AI engines tested, only one accurately surfaces your AI degrees — and Gemini actively tells prospective students you offer none. The fastest fix is three publishing decisions, not a technology overhaul.
Read past the composite. The most decision-relevant figure in this report is your generative-engine visibility.
Your composite AI-Readiness score is 2.6 — Critical, but that is a public-surface reading: it can't see your internal stack, work in flight, or the genuine AI curriculum already inside your MS CS, MSIT, and Data Analytics programs. The live exposure is 25% generative-engine visibility — only Perplexity surfaces your AI programs accurately. The fastest fix is not a technology overhaul; it is three publishing decisions.
The shape tells the story: the one dimension above the band floor (A4) is exactly where you have real assets — dragged down by a publishing problem, not a program problem.
| Dim | Dimension | Score shape | Score | Band |
|---|---|---|---|---|
| A1 | AI Strategic Posture | 2.9 | Critical | |
| A2 | AI in the Funnel | 1.5 | Critical | |
| A3 | AI / Edtech Stack Modernity | — | Insufficient data | |
| A4 | Generative-Engine AI Treatment | 5.2 | Healthy with gaps | |
| A5 | AI Governance & Compliance | 2.1 | Critical |
The honest framing this data demands, before the findings.
Dedicated AI concentrations in MS CS and MSIT, an MS in Data Analytics-AI track, named courses (AIT 600 Artificial Intelligence, ML Methods, Python for AI, GPT Engineering), and a published institutional stance endorsing generative AI in the classroom. Perplexity surfaces all of it accurately, with 35 verifiable citations. Third-party directories already carry the facts.
It cannot confirm a CRM-embedded chatbot, a mobile-app advising bot, or your actual LMS — all could exist behind authentication. "No AI vendor detected" means not on the public surface — never proof of absence. A3 returned insufficient data for exactly this reason. The gap between what we see cold and what first-party data would reveal is the upgrade path.
Prospective students increasingly shortlist schools by asking an AI engine. Here is what those engines say about ███████ today — 5 queries across 4 engines.
The content is real; three of four engines either can't find it or invert it. Every prospective student who researches ███████ through ChatGPT or Gemini — the two most-used engines — gets nothing, or worse, gets told the programs don't exist. Gemini's misinformation is the sharpest edge: it is confidently telling your market a falsehood that your live program pages contradict, with no correction signal from you.
AI-discovery competitive risk is surfaced here as narrative — the 25% visibility figure is volatile and we never hang a dollar on it cold. But the direction is unambiguous: as discovery shifts to AI answers, low presence is a widening competitive gap. A corrective GEO submission — structured JSON-LD on an AI-programs page, Organization schema disambiguating ███████ from Competitor 1 — could deploy within days and begin reversing Gemini's misinformation before any deeper work.
The activity exists; it's invisible where it counts most — the public institutional surface. Each dimension below shares the same root cause.
A 2.9 does not mean ███████ lacks AI activity — it means that activity is invisible where it counts most.
The dimension with the most immediate institutional risk.
No score — a blind spot, and a consequential one.
Every prospect who lands on admissions or apply meets a legacy Olark live-chat widget and a JavaScript form with a CAPTCHA — no 24/7 qualification, no program-matching, no guided application support.
In an institution this size, small funnel and retention gains move real money. All figures code-computed from sourced inputs.
| Opportunity | Basis | Value (base · range) | Def. |
|---|---|---|---|
| AI Track for Faculty/Staff (8-week program) | Revenue base (AL1) | $322K$129K–$643K | med |
| Revolution Stack — Virtual Tutor + AI-chat | Retention / persistence lift (AL5) | $39K$13K–$64K | med |
| Stack Analyzer / AI Operations Agent | Student-services efficiency (AL4) | $16K$5K–$46K | med |
Retention lift (AL5) — 966 students × 20% attrition × a cited AI-nudge retention lift × gross published tuition → $13K–$64K/yr (base ≈ $39K). Benchmarks: EdSights / Civitas / SNHU case studies.
Student-services efficiency (AL4) — ~7,728 routine inquiries/yr (proxied volume — flagged) × $6/inquiry × 35% AI-deflection → $5K–$46K/yr (base ≈ $16K). Benchmarks: EDUCAUSE / Ivy.ai / Ocelot.
What sharper data unlocks. Two levers are formally blocked cold until first-party data arrives:
Blocked: admits / net tuition not available.
Blocked: no CRM/LMS/SIS detected on the public surface.
None of these is hypothetical. Each is sourced to what the public surface shows right now.
The cold scan opened the door. A warm data overlay walks through it — internal CRM identity, LMS confirmation, actual funnel metrics, GTM payload access — unlocking AL3, AL7 and converting narrative findings into quantified opportunities.
Moves A1 and A5 immediately and differentiates ███████ across its peer set.
Add FERPA notice, GLBA safeguards, and third-party processor disclosures for Olark, the CRM plugin, and GTM.
Point /ai from the hospitality blog to a real AI-programs landing page; deploy JSON-LD on the programs page plus Organization schema disambiguating ███████ from Competitor 1 — to start reversing Gemini's misinformation.
So the funnel, retention, and efficiency levers in Section 05 have a compliant data foundation.