How Organisations Can Build Trust, Authority and Visibility in the Age of Artificial Intelligence
By Hezron Ochiel
Updated: 2 November 2026
Introduction: When an answer becomes the first impression
A communications director used to have a reasonable idea of how someone would check an institution’s credentials: search for its name, visit the official website, read a news report, and contact the relevant office. That route still exists. But more and more people now encounter an AI-generated summary before they see the underlying documents.
Consider a university with a valuable research programme. Its reports may be freely available, yet a search-generated answer about leading work in the field may omit them. In another case, an assistant may repeat outdated admissions information. Both situations affect reputation: people act on what they can find and what they believe is accurate.
This guide examines the practical implications for public relations in Africa. It draws on published guidance from search providers, original Hezron Insights research and examples of the decisions communication teams face when information is scattered, outdated or poorly attributed.
1. What AI Search Changes for Public Relations
A conventional search results page gives readers links to examine. AI-assisted search may also summarise information or assemble an answer from selected sources. That can save a reader time, but it can also make the sources harder to identify. A university, hospital, professional body or public agency therefore has reason to ask whether its official record is accessible and whether summaries represent it correctly.
Official websites remain essential. Search systems still need discoverable source material, and readers should be able to check important claims against primary documents. Google’s guidance on AI features explicitly says that the fundamentals of technical SEO and useful, reliable content still apply.
For communications teams, the change is practical. They must make authoritative information easy to locate, interpret and verify. They must also pay attention to what independent sources say about their organisation. A useful website, a well-documented research archive, clearly identified experts and responsible media relationships each serve readers directly. They can also improve the quality of the public information available to search and AI systems.
2. What We Know About AI Citations, and What We Do Not
No universal formula explains how every AI assistant finds, evaluates, and cites sources. Product design, retrieval methods, query wording and available content differ. A citation in one answer should not be treated as a permanent endorsement or evidence of the system’s internal reasoning.
Google advises site owners to keep important pages crawlable, indexable and useful to readers; its AI-search guidance does not require a special markup scheme. Microsoft’s Bing Webmaster Tools now provides AI citation reporting for supported experiences, but the company cautions that citation counts are not rankings or measures of authority.
An organisation should therefore distinguish three outcomes. Being findable means relevant public information can be located. Being represented accurately means the source material and resulting explanations reflect reality. Being cited or recommended means a particular tool visibly uses or suggests the organisation in a given context. These outcomes are related, but none guarantees the next.
A communication team should avoid claiming that adding schema, publishing a fixed number of articles or acquiring backlinks will automatically trigger recommendations. The better question is whether the underlying information is useful, accessible, original, up to date and corroborated where appropriate.
3. Africa’s Visibility Challenge: What the Evidence Shows
African organisations produce knowledge that deserves to be easy to find: university research, community reporting, public records, professional guidance and technical evidence. Availability on a website, however, does not guarantee that the material will appear in a generated answer.
The African Humanitarian AI Citation Index (AHACI) provides one example. It analysed 1,083 verifiable answer-linked citations across four AI platforms, covering questions about ten African countries. Only 5.5% of citations were to organisations based in the country under discussion; 68.8% were to UN or other multilateral organisations. These figures describe a defined sample of humanitarian-information answers, not all AI search or all African knowledge. They reveal whose sources were visibly credited in that exercise and justify further investigation into local discoverability.
The study does not establish the full supply of relevant African sources, nor does it prove why any particular platform preferred one source. It does show whose work appeared in the citations that could be recovered. That is a useful starting point for examining how African evidence travels online.
Related Hezron Insights studies provide further context. One audit examined 999,538 websites and found 423 in the highest AI-search readiness bands (scores 9–10), about 0.04% of the sample. A separate Kenya-focused review examined 199 websites and identified at least one high-confidence content risk in 37.2% of those reviewed. These studies use different sampling approaches and assessment criteria; neither provides a representative estimate of all African websites. They illustrate practical problems such as stale information, inconsistent names, template residue and unsupported figures. Organisations should correct these defects before investing heavily in promotion. A related analysis of hiring trends examines why SEO and GEO skills are appearing in communication roles.
Take a health-training institution with programme requirements spread across PDFs, old news reports and an admissions page. When the documents disagree, a reader may struggle to identify the current rule. An AI summary can reproduce that confusion. The immediate remedy is an authoritative, dated admissions page with a clear owner and links from older material.
A closer look at three African evidence sets
AHACI examines citation localisation within a defined set of humanitarian questions across ten countries. Its 1,083 recoverable citations are evidence about that sample, not a census of all AI answers or all available African expertise.
The almost one-million-website readiness audit answers a different question. It assessed a broad sample of websites against an author-defined readiness rubric; only 423 attained scores of 9 or 10. That result falls within the study’s framework. It is not a measure of the percentage of all websites that can or cannot appear in AI answers. The practical lesson is to inspect technical accessibility, transparent authorship and structured, verifiable content together. A website can load quickly yet offer no credible explanation of its expertise. Another can hold substantial research but bury it in hard-to-discover documents.
The smaller Kenya content review examined 199 websites and found that 37.2% showed at least one high-confidence risk. Those risks included conflicting numerical information, ambiguous organisational identities, template residue and outdated copy. The study was not a national random sample, and its findings should not be projected to every Kenyan institution. Nevertheless, the risk categories make an excellent starting checklist for a university, public agency or professional association reviewing its online record.
An example: resolving contradictory programme information
Suppose the current admissions page for a Kenyan health-training programme specifies one minimum requirement while a downloadable brochure lists another. The team should verify the rule with academic administration, correct the primary page, update or retire the brochure, and display the effective date. Then link a single approved answer from the FAQs and related articles. That helps applicants first; any benefit to AI summaries is an outcome to observe, not a promise.
4. The AI Visibility Framework: Six Practical Stages
The six-stage AI Visibility Framework below is a practical editorial and governance tool. It is not a validated scoring system and does not claim to reproduce a search platform’s ranking process.
Stage 1: Establish credibility
Start with the organisation’s actual performance. Verify services, qualifications, affiliations, locations, contact information and claims of impact. Review who approves changes to public information and how it records corrections. An impressive website cannot compensate for inaccurate statements or poor service. Credibility must be earned in the work and reflected faithfully in the published record.
Stage 2: Document knowledge
Identify expertise that exists but is not accessible outside the organisation. Useful material may include research findings, technical explanations, case studies, policy briefs, frequently asked questions, programme information and expert commentary. Assign an owner, date and supporting evidence to each important resource. Write for the question a reader actually needs answered, not merely for a keyword.
Stage 3: Make the record discoverable
Maintain clear navigation, descriptive page titles, accessible text, logical internal links and canonical URLs. Ensure important pages can be crawled and that obsolete URLs redirect correctly. Where structured data is appropriate, it must match the information visible to visitors. A PDF can support a report, but an accompanying web page with a useful summary, authorship, and stable citation details often makes the work easier to find and assess.
Stage 4: Earn independent recognition
Share evidence with journalists, professional bodies, researchers and relevant communities. A credible external article or citation gives readers another route to the source material. Not every backlink is an endorsement, and artificial link-building campaigns can harm credibility. The aim is meaningful coverage, correction when necessary and accurate attribution, rather than collecting mentions for their own sake.
Stage 5: Maintain authority
Check whether the same organisation is described consistently across its website, publications, biographies, research profiles and trusted third-party sources. Correct factual contradictions promptly. Preserve older studies with clear publication dates and update notices. Where evidence changes, explain the change. This stage is ongoing editorial governance, not a one-off campaign.
Stage 6: Test how the organisation is represented
Ask a defined set of relevant questions across selected search and AI tools. Record the wording, platform, date, answer and supporting links. Check for factual accuracy, omitted sources and misleading claims. Repeat the exercise on a consistent schedule, recognising that one answer does not represent an entire platform. Combine these observations with search analytics, web referrals, media citations and direct stakeholder feedback. Report change carefully: an additional citation is an observation, not automatic proof that a particular intervention caused it.
5. Where Reputation and Public Relations Fit
Reputation is formed through experience, relationships and public information. AI-generated summaries create another occasion for people to encounter an institution’s claims without hearing directly from its staff. PR teams therefore need reliable source pages, sound media relationships and a clear process for correcting errors.
Media relations deserves particular attention. Independent journalism can test organisational claims and provide context that official channels cannot. A well-researched news article should not be treated as a purchased credibility signal; it has value precisely because editorial judgement is independent. Communication teams can facilitate that judgement by offering documents, interviews and prompt corrections. They should not expect coverage to translate into a predictable AI recommendation. The wider measurement implications are explored in What PR Teams Should Measure in 2026.
A credible conference presentation should contribute a finding, a carefully argued interpretation or a practical lesson worth checking. When a talk draws on research, publish a source page and link to the methods. That makes it easier for organisers, journalists and readers to assess the contribution. The communication-skills study based on 948 job advertisements provides another view of what employers still value.
Professional communities can also reveal the questions that formal institutional websites overlook. A discussion among practitioners may expose confusion about a service or policy. Use those questions to improve official explanations, while treating social posts and anonymous comments as leads to verify, not automatic evidence.
When inaccurate information circulates, speed matters, but accuracy matters more. The communication response should begin with an approved factual record and a realistic assessment of who could be affected.
How a response to false AI-generated claims should work
Consider an AI-generated summary that wrongly states that an institution has withdrawn accreditation or closed an academic programme. A public rebuttal alone may not solve the underlying problem. First, determine the exact assertion, when it appeared, which prompt produced it, whether the tool supplied a citation, and what evidence contradicts it. Capture a screenshot, timestamp, platform version if available, question wording and any relevant URLs. Keep this record internally; do not amplify a potentially harmful falsehood by repeating it as a sensational headline.
Next, establish the authoritative fact. Ask the relevant office to approve the current status in writing and publish a concise, dated statement on a stable institutional page. If the system cited an obsolete institutional PDF, repair the institutional source. If the error originated with an independent publisher, send a courteous documented correction. Use the platform’s reporting or feedback channel where one exists, but do not imply that the provider must remove or update the answer immediately. Search systems may take time to reprocess a correction, and generative responses can continue to vary.
Finally, decide who needs a direct message. Applicants facing an admissions deadline may need an email, SMS or website notice; research partners may need a formal clarification; journalists may require a named spokesperson and a link to the source record. Track whether the disputed statement reappears across a fixed prompt sample. If it does, record the unresolved evidence and escalate proportionately through the organisation’s normal communication and legal processes. The goal is accuracy for affected people, not a cosmetic improvement to a dashboard score.
6. A Simple Measurement Scorecard
A measurement plan should say exactly what is being observed. Search clicks, citations within a defined test set and enquiries from stakeholders measure different things. The following scorecard keeps those distinctions visible.
- Technical availability: index status, crawl errors, redirects, broken links and accessibility of priority pages.
- Content reliability: percentage of priority pages reviewed, correction time, named owners, dates and evidence links.
- Search discovery: impressions, clicks and queries from Search Console or equivalent tools; segment by content topic.
- AI-answer observations: proportion of tested prompts with an accurate answer, visible citation or erroneous claim, always reporting the test sample.
- Independent recognition: quality and relevance of citations, editorial coverage and legitimate referrals, not raw backlink totals alone. For a fuller account of measuring trust, discovery and action, see What PR Teams Should Measure in 2026.
- Stakeholder outcomes: enquiries, applications, publication downloads, invitations or other meaningful actions, interpreted alongside offline evidence.
Keep dated records of corrections and observations. A page updated in June and cited in July does not by itself show that the edit caused the citation. Trends can move because the questions, systems and underlying indexes change.
A measurement example, with limitations
A medium-sized research institution might select 20 questions spanning admissions, research expertise, partnerships and leadership. It could test each question on three AI-enabled services at the beginning of a month, producing 60 observations. For each observation, it records whether the answer identifies the organisation correctly, links to an authoritative page, uses an outdated source, omits a relevant source or contains a material error. If 18 of 60 responses visibly cite the institution, the observed citation frequency is 30% in that deliberately chosen sample. It is not a 30% probability of being cited in all real-world questions, and it should not be described as market share.
The team could repeat the identical 60 tests after a set of corrections and publish an internal before-and-after table. It should also document when prompts were tested, country and language settings, whether the user was signed in, and any changes to the products themselves. A rise from 18 to 24 cited responses would be an observation worth examining, not proof that a particular web edit caused the increase. Answer accuracy, citation presence and user outcomes must remain separate columns because a highly cited but factually wrong answer is not a success.
Where the institution has access to publisher dashboards, it should compare these observations with product-specific aggregate data. Bing Webmaster Tools’ AI Performance report, for example, provides information on cited pages, citation counts and grounding-query groups across supported Microsoft experiences. Microsoft explicitly cautions that these measures do not establish rank, authority or placement within an individual answer. Google’s AI Search guidance similarly stresses that established SEO practice remains important and that meeting technical requirements never guarantees inclusion. Cross-platform numbers should not be combined into a single unexplained “AI trust score”.
7. The Digital Trust Cycle: A Supporting Idea
The Digital Trust Cycle reminds us that evidence can lead to scrutiny, recognition, and further discovery. It is a useful management idea, not an automatic sequence. Good work may remain obscure; high visibility does not necessarily mean that a claim is reliable.
The management discipline behind the cycle
The cycle needs ownership. Communications may lead the editorial audit, but the admissions office should approve admissions requirements; researchers should check scientific claims; IT should resolve redirects and indexing; and legal or compliance staff may need to review sensitive public corrections. Establishing those responsibilities prevents a common failure: a communications team repairs a page only for another department to upload an obsolete document a week later. One shared source register, with the current page URL, responsible owner, approval date and next review date, is often more useful than an expensive visibility dashboard.
8. A 30-Day Plan for African Organisations
Week 1: Establish what the public can currently find
List ten to twenty questions that stakeholders genuinely ask. Include programme requirements, research expertise, services, contact details and common reputational concerns. Test them on standard search and two or three AI-enabled tools. Save answers and links with timestamps. Check your most important pages for errors and inaccessible links. Identify information that is missing, inconsistent or outdated.
Week 2: Repair and publish authoritative source material
Choose five priority gaps. Update the main service or programme pages, reconcile conflicting figures, ensure contact details are current and write summaries for reports hidden in downloads. Give each page a named owner and review date. Restore broken article URLs where possible, especially those already cited by other publications. Check that redirects lead to relevant material.
Week 3: Connect the evidence to appropriate audiences
Publish one substantial evidence-led explanation and one case study or research brief. Offer relevant information to journalists and professional communities without requiring coverage. Update expert biographies, link related resources, and make attribution clear. If speaking at an event, provide organisers with a verifiable topic description and links to the underlying work.
Week 4: Evaluate the record and assign responsibility
Repeat the same question set and compare observed answers. Review crawl and index status, publication downloads, relevant referrals and enquiries. Note improvements, failures and unanswered questions. Assign owners to unresolved issues and agree on a monthly review. Do not promise dramatic results from a four-week programme; the purpose is to establish a reliable process.
What each week should produce
At the end of the month, the team should have an approved question bank, an audit of priority pages, a correction log, a small portfolio of improved evidence pages and a comparison of observations collected before and after the changes. Named staff should own updates and review dates.
9. Questions Communication Leaders Ask
Is search engine optimisation still relevant?
Yes. Google explicitly states that core SEO practices continue to apply to its AI search features. Make valuable content technically accessible and easy for visitors to use. There is no published universal shortcut that guarantees AI citation.
What is generative engine optimisation?
GEO is a term used for improving how information is available, understood and potentially cited in AI-generated answers. It can be useful as a planning label, but it should not be confused with a standardised certification or a transparent ranking mechanism shared by all AI products.
Will publishing more articles increase our AI visibility?
Not necessarily. A smaller collection of accurate, distinctive and well-maintained resources may be more useful than a large library of repetitive pages. Choose topics your audience needs answers to and your organisation can support with evidence.
Should we purchase backlinks or online mentions?
No. Pursue legitimate editorial recognition and partnerships. Paid placement or manufactured links can create misleading signals, weaken reputation and violate platform policies. Clearly label sponsored material.
How do we measure whether the organisation is represented accurately?
Develop repeatable questions, capture dated answers and inspect citations against primary sources. Record both correct and incorrect information. Combine this exercise with stakeholder enquiries and website analytics. Treat results as a sample of observable behaviour rather than an exhaustive account of the platform.
What should we do when AI gives incorrect information about us?
First, correct the authoritative public record and make the correction easy to locate. Use the platform’s feedback mechanisms where available, contact publishers of inaccurate third-party material and communicate directly with affected audiences. Keep an evidence trail and verify whether the issue persists; do not assume the change will be immediate.
Do communications teams need a separate GEO budget?
Start by fixing work that the organisation already has a reason to do: accurate service information, accessible research outputs, good site hygiene, dated source pages and credible public relations. A separate budget may be warranted when the organisation has a large or complex digital estate, a multilingual audience, or a demonstrable pattern of consequential errors in AI answers. The budget should buy verifiable research, accessibility, testing and useful content. It should not be justified by a vendor’s promise to make a brand appear in every chatbot.
Can a media mention substitute for an original source?
No single kind of evidence serves every purpose. Independent reporting can provide scrutiny and context, while primary documentation establishes what the institution formally says or did. The best information journey often lets a reader inspect both. A peer-reviewed study, a transparent dataset, a public correction log and a well-reported independent article can each play different roles. Communications practitioners should help people trace important statements back to appropriate evidence.
Conclusion: Make the official record worth finding
For African institutions, the practical question is whether a person can locate the right evidence, understand who stands behind it and check whether it is current. Strong communication work makes that possible across websites, reports, journalism and public conversations.
Begin with the facts closest to the people you serve. Correct conflicting information, publish research with clear methods, link related pages and monitor what search tools and stakeholders actually report. Better visibility is worth pursuing, but it should always serve better understanding.
Sources and further reading
Google Search Central, AI features and your website — https://developers.google.com/search/docs/appearance/ai-features
Google Search Central, Creating helpful, reliable, people-first content — https://developers.google.com/search/docs/fundamentals/creating-helpful-content
Google Search Central, Optimising your website for generative AI features — https://developers.google.com/search/docs/fundamentals/ai-optimization-guide
Bing Webmaster Tools, AI Performance — https://www.bing.com/webmasters/help/ai-performance-9f8e7d6c
Microsoft Bing Webmaster Blog, Introducing AI Performance (10 February 2026) — https://blogs.bing.com/webmaster/2026/2/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview/
Hezron Insights, African Humanitarian AI Citation Index — https://www.hezroninsights.com/when-ai-explains-african-humanitarian-crises-whose-knowledge-becomes-visible/
About the author
Hezron Ochiel, MPRSK, is a strategic communication practitioner, researcher and founder of Hezron Insights. His work focuses on AI visibility, digital authority, research communication and the representation of African knowledge. He has more than 15 years of experience in journalism, public relations and institutional communication.