01 · Audit · education

AI Visibility Audit for Education & executive learning

Diagnose how ChatGPT, Gemini, Perplexity, Copilot and Google AI Mode describe, cite, ignore or misrepresent your brand.

Industry focus

Education & executive learning

For universities, business schools and programs competing on international searches and AI-assisted comparisons.

Focus areas

UniversitiesBusiness schoolsExecutive edOnline programs

How this plays here

What this engagement
actually does for this buyer.

An IE Business School-tier executive program watches international applicants describe their shortlist as 'the schools ChatGPT named first'. The audit runs the cross-country comparison prompts those applicants actually use: program ROI, alumni outcomes, language requirements, format flexibility. It scores how AI describes the program against 5-7 named peer schools, where the program disappears (often in a single language while being visible in others), and which ranking sources, alumni profiles or faculty publications anchor the gap.

Buyer prompts

The questions they're
asking right now.

Prompt 01

“best executive MBA in Europe for someone working full-time in Madrid”

Prompt 02

“compare IE vs IESE vs ESADE for someone in financial services”

Prompt 03

“is an Executive MBA worth the cost for a 38-year-old founder”

Priorities

What matters
in this industry.

01
International search visibility
Students compare programs across countries in one prompt. If your program isn't in the AI-generated shortlist, it's not in the application set either.
02
Outcome data wins
ROI, salary uplift, employment rate, alumni trajectory — these are the data points that persuade prospective students AI talks to.
03
Subject-matter authority
Faculty publications, research output, alumni networks. AI engines treat schools with thick external footprints differently than ones that only exist on their own site.

Buyer journey

How buyers
actually research.

Prospective students compare a US MBA against a French Grande École against an executive program in Spain — in one ChatGPT thread. They ask about teaching style, alumni outcomes, language requirements, application difficulty. AI synthesizes from rankings, alumni reviews, faculty publications and employment reports. If your school's data isn't structured, it's invisible to that synthesis.

Trust signals

What AI engines
are reading.

01
Faculty and alumni schema: Person, EducationalOrganization, Course
02
Outcome data: salary statistics, employment rates, career destinations
03
Rankings, accreditations, named partnerships
04
Multilingual content presence — especially for programs targeting international students

Deliverables

What you get
specifically here.

01
Citation table on 25 applicant-shaped queries vs 5-7 named peer schools, scored per engine
02
Outcome-data gap report showing where alumni salary, employment-rate or destination data is thin or missing vs cited peers
03
Language-specific visibility map showing where the program is named in English and Spanish vs missing in French, German and Portuguese

Sources we tune for

Where the citations
actually come from.

FT Business School RankingsQS World University RankingsThe Economist Executive EducationAACSBBloomberg Businessweek MBA Rankings

Regulatory shape

Different jurisdictions, different rules, different language. AI engines prefer sources that are explicit about which markets and which regulations apply. We map each per-jurisdiction descriptor before any optimisation work touches the source pool.

Service context

How this engagement
fits the sector.

01 · Audit