Government use cases: residents, planning, rules

01The use cases.

The use cases, Government
Case	What happened	The impact	Why it matters
03Planning decisions went wrong on a website the city had forgotten	Authorities merged into one and the old digital landscape came with them. A well-built, well-cited planning microsite from one original authority, forgotten, outranked the new portal, whose documents carried weak version, date and authority signals. The new authority’s own chatbots and agents read the old site too. In 2 instances blocking made it worse.	Incorrect advice, planning rejections that should not have happened, appeals, some litigation, poor citizen experience.	Multiple versions across the landscape, no map of the landscape, and weakly structured new information.
04The chatbot was replaced twice before anyone looked at what it was reading	Successive chatbots failed, the software vendor was blamed for the first, the digital team for the wrong answers that followed. The cause was the landscape the chatbot read.	Internal disruption, the cost of 3 deployments, and time spent chasing the wrong cause, with wrong answers reaching citizens throughout.	A widespread problem. The chatbot answers from the estate as it finds it, and the organisation answers for what it says (Air Canada, cited briefly).
11Regulator embarrassment at poor document control	20% of links inside the regulator’s legal and regulatory PDFs broken, including the core legal basis of some documents. AI reads them on behalf of the organisations the regulator oversees.	A working interpretation of the law in circulation that the regulator cannot stand behind.	A body holding others to a published standard seen unable to keep its own record in order. Embarrassment at best.
162 AI tools, 2 different answers, and consequences the organisation could not see in full	Search and Gemini present materially different pictures of the same organisation. Access, source selection and processing can contribute; no single cause is asserted without evidence.	Applicants told different courses and dates by different tools. Wrong mileage repayment details from government information.	Lost confidence, and the human cost of dealing with the challenges that follow.
17The organisation’s own AI compounds its defective documents	Poorly structured documents ingested automatically into internal AI and agents. Errors compound as information is retrieved, summarised and consolidated. By the time an answer reaches a decision its origin has disappeared.	Decisions taken on answers nobody can trace.	Digital Estate DEBT™ entering the organisation’s own systems. The 9 in 10 construction: MIT measured the failure rate, AAAnow observed a cause the study did not examine.
18Forgotten sites nobody was patching: AI finding and highlighting vulnerabilities	Forgotten subdomains, microsites, platforms, forms and scripts sit outside patching, access review, certificate renewal and monitoring. Unmaintained software is unpatched software.	An attack surface nobody owns. Takeovers documented across government and Fortune 500 sites.	Organisations that know their landscape and remove what no longer earns its place save money and time and run more efficiently. Coverage and cyber exposure move with it.
19At renewal, the firm could not show what it had online, and the terms showed it	Exposure becomes financially visible at underwriting and renewal. Organisations that can show levels of control and reasonable steps taken are better placed on risk quality, cost, limits and terms. No outcome is automatic.	Terms, exclusions and cover worsen where control cannot be evidenced.	Not knowing what is online is a risk in itself, with ever greater impact.
20Duplicates and untracked copies: the correction that never lands	19% of PDFs on organisational websites are duplicates. 37% become untracked once they leave the producer’s site. Correcting one copy leaves the others in place. Some are found, some are not, and the chase continues.	Old figures stay retrievable and the management burden never ends.	Governance in how documents are controlled, and solid foundations. The AI Accelerator’s internal education exists so governance across document control is understood.
21Careers material shapes what candidates are told	Old careers pages, past roles and benefits no longer offered are assembled into the employer AI describes.	Candidates told the wrong thing about the employer, and acting on it.	73% of talent acquisition leaders are concerned candidates receive outdated or inaccurate AI information; 33% are confident their employer brand is accurately represented.
23The documents that sank the internal AI project	A generic internal AI system was fed the conflicting safety documents from 5. Answers disagreed because the sources disagreed. The project failed, the spend was written off and filed as proof that AI projects fail. Nobody traced it to the documents.	Spend written off, and the wrong conclusion drawn.	MIT measured the rate. AAAnow observed a cause the study did not examine. Side by side, never merged.
24Funding stopped on submissions that looked fine	Applicants submitted PDFs with no structure defined. 64% carried faults in how tables and data were laid out. The system misread them and funding for key projects stopped. The system was blamed, restarted on the same documents, and failed again.	Projects unfunded, applicants lost out, hours of review, a second failure.	The anchor case. Faults invisible to people decided outcomes by machine.
25£350,000 to be seen by AI routes the estate was turning away	£350,000 committed to AI visibility while AI routes were turned away or left without a reply at the digital doorway. The website looked fully operational to people throughout.	Spend amplifying foundations that could not be read.	Evidence base: 5 anonymised doorway assessments, 4 to 14 of 15 AI routes turned away, people served.
26The external repository that became a dumping ground	A contracted third-party document store holding annual reports and safety manuals. Drafts uploaded beside finals, versions multiplying, no management over time. AI selects from the lot.	Draft and superseded material served as the record.	The landscape extends beyond the organisation’s own sites.
28Regulator's questioned governance where the compliance register stopped	Held to what is published, and AI restates it without supervision. The gap between the compliance team’s list and the real estate is where inquiries begin.	Demonstrating control after the fact is slow and expensive because the record was never assembled.	The widest case. Regulatory attention from the other cases routes here.
31Intellectual property disputes	Claims, names and assets across regional sites, campaigns, partner material, historic records and third-party repositories. AI blends them, attributes claims to the wrong product, revives abandoned campaigns, repeats third-party wording as the brand’s own, and makes things up.	In a dispute, material published under the organisation’s name reads as admission. Contested wording spreads faster than it can be withdrawn.	Developed in depth. An accurate position of the landscape first, the AI Accelerator establishing it, then monitored with the technology overseeing at scale. Nothing worse than AI inventing under your name.
32Announcements buried in the page	Big, heavy pages carrying little information. The announcement is lost in the page furniture, so the systems that should carry it widely miss it and the superseded figure keeps being repeated. Companies that bought AI visibility to fix low reach without finding the root cause amplified the problem.	Important news, product and investor announcements not heard.	Under most people’s radar.
3320 years of documents were still answering for the company as if each were current	Decades of documents, much superseded, most still reachable, read with the same confidence as the current label. Retired material stays online, mirrored or cached. More is out there than the organisation’s records show.	Superseded material repeated as current.	Route removal after the landscape stage. AI discovery an option.
34Residents get different answers to the same question, and the cost lands on the council	Services, districts and arms-length bodies each publish their own version. A person asking AI gets one, served with confidence. Nobody is wrong; the estate as a whole is.	Conflicting information to the public, then repeat contact, complaints, rework and remediation.	The cost consequence, missing from the page today, is the point.
35The estate the digital team had warned about for years	The digital team warned of the sprawl for years; the first step in correcting AI was the footprint. Higher education instance: a university replatformed to a single platform, single point of governance and control, and removed the old sites. A 9-month project the team had wanted for 3 years, brought forward by what AI was surfacing.	Ongoing savings: operating cost, and beyond it better information, more confidence, better-informed applicants and control of the message.	The positive case. The team was right, and the return is measurable.
44An old annual report answered as this year's after the page that dated it was removed	The web page that framed a report as historic has been removed. The PDF lives on, carries more authority than the page did, and answers as current.	A historic report treated as the present.	PDF authority in AI results.
45New York City’s business chatbot told companies to break the law	The city’s business chatbot misstated policies and advised businesses to break the law. Identical questions got different answers at different times. The chatbot was taken down.	Citizen frustration, wrong information acted on, legal action against the municipality, withdrawal.	Public-record confirmation of 4 and 39. // Table B. What stands behind each case // Evidence status, figure with its locked scope, directors’ and officers’ liability, insurance and commercial exposure, links.

02The other sectors.

The same library, by sector: Higher education, Listed companies, Financial services, Life sciences, Chemicals and Consumer brands. The full list of 45 is on the use cases page.

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Cases marked Illustrative or Scenario are written from the mechanism; the others are observed or documented. No organisation is named.

Government, read back to the public.

01The use cases.

02The other sectors.