The AI enrolment engine, measured

Is your enrolment engine AI-ready?
Don't guess. Measure it.

Enrolment is a sales funnel, and AI now sells at every step. EnrolIQ scores how AI-run your funnel is, how AI-enabled your paid media is, which tools your team uses, and the capability of the people running it, then names your single constraint.

Sample · Employee AIQ
720
/ 1000Capability Score
Proficient

Advanced capability across the AIQ rubric.

vs peer cohort74th percentile
Direction82
Discernment76
Impact71
Adaptability64
Search moved to AI. Your funnel did not.
68%↓
paid click-through when an AI answer appears above results
Seer Interactive, 25.1M impressions
68%
of Google searches now end with no click at all
SparkToro 2026
21x
more likely to qualify a lead answered in five minutes vs thirty
MIT / HBR studies
~50%
of prospective students use AI weekly while choosing where to study
UPCEA 2025

A prospect asks an AI for the best programme in your field and region, reads the answer, and decides, before a single click reaches your funnel.

The model

Eight steps, one motion

AEO and AI search are one step, not the whole story. Revenue leaks at every step still run by hand.

Step 1
Surface
Discover
Get found inside the AI answer, before any click.
Step 2
Answer
Engage
Answer in seconds, any hour, any channel.
Step 3
Score
Qualify
Predictive scoring and conversational qualification route hot leads to humans, warm to nurture, cold to retarget.
Step 4
Personalise
Nurture
Behaviour-triggered, personalised journeys keep the undecided warm and re-engage on real signals.
Step 5
Close
Convert
Real-time application support and nudges to complete, with a warm handoff to a human to close.
Step 6
Hold
Decide / melt
Proactive pre-enrolment nudges stop accepted students going cold before they arrive.
Step 7
Onboard
Enrol
Guided onboarding through the last mile so the yes becomes a student in a seat.
Step 8
Keep
Retain
Early-warning support and referral activation keep students and turn them into the next intake.
Four ways in

Measure, audit, then act

01The four-pass diagnostic

Growth engine assessment

Funnel, paid media, stack and team in 8 to 10 minutes. The synthesis names the one constraint holding back enrolment.

02For the numbers

Process audit

Turn your current state into a board-grade estimate of the students and revenue leaking from each process.

03For the people

Employee AIQ

Baseline the demonstrated AI capability of any team member on the 200 to 1,000 scale, with the priority to develop.

For schools & universities

An AIQ for educators, not just admissions

Admissions AIQ measures the funnel. Educators AIQ measures whether the people running the institution can teach, lead and operate with AI. One assessment, branched by role and segment.

01For the classroom

Teacher AIQ

Six dimensions of classroom AI fluency: lesson design, assessment & feedback, integrity, personalisation, classroom ops, professional growth.

02For the institution

Leadership AIQ

Six dimensions of institutional readiness: strategy, policy & ethics, faculty enablement, data & systems, risk, outcomes & ROI.

03Four contexts

Schools & universities

School teacher · School leader · University faculty · University leadership. Benchmarked against peers in your segment, not a generic average.

Take the Educators AIQ →Join the waitlist
Why trust the number

A transparent rubric, not a black box

AIQ scores demonstrated capability against a published rubric across six dimensions, on a portable 200 to 1,000 scale. The melt-cutting approach we recommend is the one Georgia State proved in a controlled trial: 21.4% less melt, 3.9% higher enrolment. Read the scoring rubric.

200
Passive
360
Exploring
520
Practising
680
Proficient
840
Multiplier
The AI sales engine

What AI does at every step

From discovery to retention, with your team at the judgement points. Each step has a job, and a place where revenue leaks when it is left manual.

1
AI sells by surfaceing
Discover

Get found inside the AI answer, before any click. AEO and GEO so models cite your programmes.

2
AI sells by answering
Engage

Answer in seconds, any hour, any channel. AI chat and voice agents capture the lead and book the call.

3
AI sells by scoreing
Qualify

Predictive scoring and conversational qualification route hot leads to humans, warm to nurture, cold to retarget.

4
AI sells by personaliseing
Nurture

Behaviour-triggered, personalised journeys keep the undecided warm and re-engage on real signals.

5
AI sells by closeing
Convert

Real-time application support and nudges to complete, with a warm handoff to a human to close.

6
AI sells by holding
Decide / melt

Proactive pre-enrolment nudges stop accepted students going cold before they arrive.

7
AI sells by onboarding
Enrol

Guided onboarding through the last mile so the yes becomes a student in a seat.

8
AI sells by keeping
Retain

Early-warning support and referral activation keep students and turn them into the next intake.

AI Growth Engine Assessment

Your funnel, your spend, your team

Four passes. How AI-run your enrolment funnel is, how AI-enabled your paid media is, which tools you actually use, then the team capability that caps all three. The synthesis names your constraint.

For Heads of Marketing & CMOs Takes 8 to 10 minutes Nothing stored or sent

How AI-run is your enrolment funnel today?

For each stage, pick the option that is true right now. The leaks live in the steps left manual or to chance.

Discover
Engage
Qualify
Nurture
Convert
Decide
Enrol
Retain
Step 1Getting found inside AI answers · ChatGPT, Gemini, Perplexity (AEO/GEO)
Not happening
Done manually
Partly with AI
AI-run, team decides
Step 2Responding to a new enquiry instantly, any hour, any channel
Not happening
Done manually
Partly with AI
AI-run, team decides
Step 3Qualifying and scoring leads by fit, intent and readiness
Not happening
Done manually
Partly with AI
AI-run, team decides
Step 4Nurturing undecided prospects with personalised, behaviour-triggered journeys
Not happening
Done manually
Partly with AI
AI-run, team decides
Step 5Helping applicants complete, submit and make the decision
Not happening
Done manually
Partly with AI
AI-run, team decides
Step 6Stopping accepted students from going cold before arrival (melt)
Not happening
Done manually
Partly with AI
AI-run, team decides
Step 7Guiding confirmed students through to day one
Not happening
Done manually
Partly with AI
AI-run, team decides
Step 8Retaining students and activating referrals at scale
Not happening
Done manually
Partly with AI
AI-run, team decides

How AI-enabled is your paid media and reporting?

This is the layer most teams leave manual. For each capability, pick what is true across your Google, Meta, and any other active channel.

AI-optimised campaign bidding and targeting
·Bidding strategy across Google and Meta
Manual CPC / CPM only
Target CPA on at least one campaign
AI-optimised bidding tied to enrolments across channels
Agentic bid optimisation with MCP or custom rules
·Audience targeting and expansion
Manual audiences built by the team
Lookalike and interest layers added
Advantage+ or Performance Max AI audiences active
AI continuously tests and refines audiences against enrolment outcomes
·Budget allocation across channels
Fixed split set at the start of cycle
Manually adjusted once a month or less
Reallocated weekly based on performance signals
AI reallocates budget automatically across channels in near real time
·Ad creative generation and testing
Team writes and designs all creatives manually
AI helps draft copy, team decides versions
AI generates variants and the platform tests them
AI-led dynamic creative optimisation across all channels
AI-enabled ads dashboard and reporting
·Reporting dashboard for paid media
Manual exports assembled by the team
Unified dashboard updated manually
Live cross-channel dashboard with automated feeds
AI-narrated dashboard that surfaces and explains shifts
·Cost per enrolment visibility
We track cost per click or lead, not per enrolment
We estimate cost per enrolment manually
Cost per enrolment is calculated automatically per channel
Cost per enrolment feeds back into bidding automatically
·Anomaly detection and alerts
The team notices when reviewing reports
Manual thresholds set in the ad platforms
Automated alerts fire on sharp moves
AI identifies root cause and recommends action
·Attribution across the full funnel
Last-click attribution only
Multi-touch attribution for leads
Attribution tracks to application or enrolment
AI reconciles paid data with CRM enrolment outcomes
AI speed in paid media operations
·Speed of acting on a performance insight
Days: scheduled review
Hours: reviewed daily, changed same day
Under an hour: automated rules or agents
Real time: AI acts and flags for approval
·Landing page matching to ad intent
All paid traffic lands on the same page
Different pages per campaign, set manually
Dynamic pages adapt to programme or audience
AI personalises the page per ad and intent in real time

What your team is using, and what it is not

Tick every tool or practice actually in use today. Unticked items are your gap list, the moves available to you right now.

AI discoverability (AEO / GEO)
AEO / GEO content optimisation for AI answer enginesStructuring content so ChatGPT, Gemini, Perplexity cite you
AI-answer citation trackingMonitoring share of voice in AI answers vs competitors
Paid media AI tools
Google Performance Max with AI asset generation
Meta Advantage+ campaign automation
AI ad creative generation (AdCreative.ai, Pencil, platform tools)
MCP or API-connected agentic ad spend optimisationBudget decisions made with an AI agent, not manually
AI-powered A/B and multivariate creative testing
Programmatic AI bidding (DV360, The Trade Desk)
Reporting and analytics AI tools
Unified cross-channel paid dashboard
AI-narrated or anomaly-detecting reporting layer
Enrolment-level attribution model (CRM connected to ad platforms)
Predictive budget forecasting using AI
Automated reporting with AI-generated commentary
Lead capture and response
AI chat agent live on site or WhatsApp, 24/7
AI voice agent for out-of-hours calls
Sub-60-second automated lead acknowledgement
AI lead qualification and routing to the right counsellor
Nurture and conversion
Behaviour-triggered nurture journeys (not batch emails)
AI-personalised content by programme, country, year group
Predictive lead scoring to prioritise counsellor effort
AI-assisted application flow with instant help on the page
Melt, retention and referrals
Proactive melt nudges for accepted students
Early-warning AI for at-risk enrolled students
Referral and alumni activation with AI personalisation
Foundation
Single clean CRM as source of truth
GA4 plus conversion tracking set up correctly
Named owner of the AI enrolment engine
Regular team capability assessment (AIQ baseline)

Can your team run it

The engine is only as good as the judgement directing it. Answer for your marketing and admissions team. This returns an AIQ on the 200 to 1,000 scale.

DirectionWhen the team uses AI on a real paid media or enrolment task, it mostly
Takes whatever the first output gives
Adds some context and tidies the result
Steers with clear briefs and iterates until it is right
Designs the AI approach and knows when to override it
WorkflowAI in paid media and reporting is
An occasional one-off helper
Used for a few recurring tasks
Built into several standard workflows
Orchestrated across the full workflow with documented process
DiscernmentWhen the AI produces an output, the team
Trusts it if it reads plausibly
Skims for obvious errors
Verifies key numbers before acting
Routinely catches fluent-but-wrong output
Data & ethicsOn data, attribution, and what AI should decide, the team
Is unsure what is safe to share with AI
Avoids obvious sensitive data like student PII
Follows clear rules on data and AI-decision scope
Judges when AI optimisation is biased or crossing a line, and acts
ImpactThe team's use of AI in paid media has
Saved a little time, unmeasured
Sped up some tasks noticeably
Improved a measurable outcome: lower CPL, better ROAS
Produced results not feasible before, like real-time attribution
AdaptabilityWhen platforms change their AI tools or policies, the team
Waits to be told what to do
Adapts slowly with help
Picks up changes and resets within days
Tests new capabilities before competitors notice the shift
Your position

Complete all four passes to see where to act first

Funnel AI-run
Paid media AI-enabled
Team AIQ

Each pass stands alone, but the four together show the real constraint. A well-optimised paid stack run by a team that cannot interpret the output underperforms. The lowest signal is where the next move is.

Internal scoring uses the AIQ model. Nothing here is stored or sent.
Bands: Passive (200–359) · Exploring (360–519) · Practising (520–679) · Proficient (680–839) · Multiplier (840–1,000).

Process audit

The students and revenue you are losing at each step

Give seven figures and your state at each step. Seats are fixed. The audit estimates the lead spend you waste today against an AI-run funnel that fills the same seats from fewer leads.

Used only on your report. Nothing is stored or sent.
Total enquiries across all channels in a typical year, not applications.
Applications received in a typical year, between enquiries and enrolments.
Students who actually enrolled in a typical year. This anchors the whole estimate.
Average marketing cost to generate one enquiry, in the currency below.
Average across programmes if you run several. Use the currency below.
A 1-year master's is 1, a 3-year degree is 3. Allowed range 1 to 8.
Formats every figure in the report.
Your current state at each step
DiscoverWhen a student asks ChatGPT, Gemini or Perplexity for the best programme in your field and region, do you appear in the answer?
Not happening
Done manually
Partly with AI
AI-run
Visibility inside AI answers, not just classic search results.
EngageWhen a prospective student enquires, how fast and how consistently do you respond, any hour, any channel?
Not happening
Done manually
Partly with AI
AI-run
Speed and consistency of first response across every channel.
QualifyDo you separate serious applicants from casual enquiries before counsellors spend time on them?
Not happening
Done manually
Partly with AI
AI-run
Triage by fit and intent before human time is committed.
NurtureHow do you stay in front of prospects who are interested but not yet applying?
Not happening
Done manually
Partly with AI
AI-run
Behaviour-triggered contact, not generic broadcast.
ConvertWhen someone starts an application, do you help them finish, or do they drop off?
Not happening
Done manually
Partly with AI
AI-run
Real-time support through the application form.
DecideBetween offer accepted and term start, how do you stop confirmed students drifting away (melt)?
Not happening
Done manually
Partly with AI
AI-run
Pre-arrival melt prevention.
EnrolFrom confirmation to the first day, do students get guided through onboarding, or left to navigate it?
Not happening
Done manually
Partly with AI
AI-run
Guided onboarding from confirmation to day one.
RetainOnce enrolled, do you catch at-risk students early and turn happy ones into your next intake?
Not happening
Done manually
Partly with AI
AI-run
Early-warning retention and referral activation.
Complete all fields to generate your report.
Wasted lead spend recovered
AED 0
per year, at AI benchmark conversion
Lead to enrolment conversion
Employee assessment

Score the demonstrated AI capability of a person

Six questions, answered honestly for the individual. Returns an AIQ on the 200 to 1,000 scale, the dimension breakdown, and the one thing to develop next. Capability, not confidence.

DirectionWhen they use AI on a real task, they mostly
Takes whatever the first output gives
Adds some context and tidies the result
Steers with clear briefs and iterates until it is right
Designs the AI approach and knows when to override it
WorkflowIn their day-to-day work, AI is
An occasional one-off helper
Used for a few recurring tasks
Built into several standard workflows
Orchestrated across the full workflow with documented process
DiscernmentWhen the AI produces an output, they
Trusts it if it reads plausibly
Skims for obvious errors
Verifies key numbers before acting
Routinely catches fluent-but-wrong output
Data & ethicsOn data, attribution and what AI should decide, they
Is unsure what is safe to share with AI
Avoids obvious sensitive data like student PII
Follows clear rules on data and AI-decision scope
Judges when AI optimisation is biased or crossing a line, and acts
ImpactTheir use of AI has
Saved a little time, unmeasured
Sped up some tasks noticeably
Improved a measurable outcome: lower CPL, better ROAS
Produced results not feasible before, like real-time attribution
AdaptabilityWhen platforms change their AI tools or policies, they
Waits to be told what to do
Adapts slowly with help
Picks up changes and resets within days
Tests new capabilities before competitors notice the shift
Employee AIQ
0
/ 1000 capability score
Assessed Jun 2026 · capability, not confidence

Bands: Passive · Exploring · Practising · Proficient · Multiplier. Nothing stored or sent. How the score is built.

Scoring rubric

How the score is built

AIQ is meant to be read, challenged and trusted. This is the rubric in plain words: what is measured, the scale, the bands, and how a score is reached.

What AIQ measures

AIQ scores demonstrated AI capability, what a person or team actually does with AI, not what they say they can do. It is employer-neutral and portable: the same score means the same thing wherever it travels, like a credit score for AI capability.

Read the full rubric →

The scale, 200 to 1,000

Every score sits on one fixed scale, divided into five bands describing how AI changes the work, from no real change to multiplying output.

200–359
Passive
360–519
Exploring
520–679
Practising
680–839
Proficient
840–1000
Multiplier

Passive: AI has not changed the work. Exploring: faster, but the same work. Practising: directed with intent, output verified. Proficient: produces work hard to reach without AI. Multiplier: does the previously infeasible, and lifts others.

The six dimensions

A score is a weighted composite across six dimensions, each scored from what the person demonstrates on the same four levels used in the assessment.

DimensionWhat it rewardsWeakStrong
DirectionSteering AI with clear intent toward a specific outcomeTakes the first outputDesigns the approach, knows when to override
WorkflowBuilding AI into repeatable process, so it compoundsOne-off useOrchestrated with documented process
DiscernmentTelling good output from plausible-but-wrongTrusts fluent outputCatches fluent-but-wrong reliably
Data & ethicsJudging data, bias, attribution and the decision lineUnsure what is safeJudges bias and where AI should not decide
ImpactTurning AI use into a measurable, attributable outcomeVague time savedResults not feasible before
AdaptabilityAbsorbing platform change and resetting fastWaits to be toldTests and resets before competitors

The dimensions are weighted, not averaged: Direction, Discernment and Impact carry the most weight, because directing well, judging output and producing real impact separate capability more than the rest. The exact weighting is held in the scoring engine so scores stay comparable over time; what is public is the dimensions, the levels and the bands.

What we will not do

Scores are owned by the person assessed, not the employer who pays to see them. We do not inflate, we do not sell a higher number, and we publish the rubric so the score can be challenged.