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The use cases · Higher education

Prospectus inaccuracies.

Universities publish more than almost any other organisation, across faculties, campuses and decades. AI answers applicants, funders and regulators from whatever it finds first.

Course pages, fees, entry requirements and prospectuses from previous cycles remain reachable. An applicant asking AI about a course may be answered from any of them.

ReferenceUC/2026/04
Updated12 August 2026
SectorHigher education
ExposureMisinformation, misrepresentation
Where it startsFaculty sites and archived prospectuses

01Where it begins

Superseded fees, entry requirements and course descriptions stay published across faculty sites, departmental pages and archived prospectuses.

02How it compounds

Applicants make decisions on the answers. Where published information and reality diverge, complaints and consumer-protection questions follow, cycle after cycle.

03What AI Readiness changes

AI Readiness gives the institution one governed answer per course, and evidence that AI carries it.

Discovery

The estate is discovered across faculties, campuses and years, including the archives.

A governed position

1 current position per course stands: fees, requirements, content. Superseded cycles are controlled.

Representation monitored

What AI systems tell applicants is monitored against the governed record, cycle after cycle.

Next step

Talk it through, in your terms.

The briefing sets this exposure in your sector’s terms, and the discussion follows your questions.

Your briefing invite

An illustrative scenario. No client is referenced, and no organisation has been assessed.

Questions

The questions this scenario raises.

Asked by boards in this position, answered directly.

An old prospectus is being read as current. How does that happen?

Offering documents are dense, dated and widely mirrored, which makes them exactly what AI systems retrieve when asked about the company. If the estate still serves them without context, they compete with your current filings, and sometimes win.

Which documents are most likely to surface?

The ones with the most structure and the longest history: prospectuses, circulars, annual reports on retired subdomains, PDF libraries from past advisers. The landscape stage inventories these before grading them.

Is the answer to delete the archive?

No. Regulatory and listing obligations mean much of it must remain. The answer is that what remains is framed, dated and positioned so a machine reading it knows what superseded it, and what has no obligation to remain is retired.

Who typically owns this problem?

It falls between company secretariat, IR and digital, which is why it persists. Grading the documents against the AI Readiness maturity model gives all 3 one evidence base to act from.