A study relayed in April 2026 (r/artificial, Forbes) suggests that the mere use of AI by a professional can degrade their perceived reputation. Beyond the debate about the author, the finding opens a broader question: if AI degrades the reputation of the person who uses it, what does it do to the reputation of the company described by it? Because generative engines — ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews — now answer in place of brands when a customer asks a question. And they get it wrong, invent, and remember.
The three families of risk
Our AI Engines practice identifies three distinct families of LLM-related reputational risk: hallucinations (the AI invents a fact about your company), the content gap (the AI does not know you, or describes you in outdated terms), and agentic AI (autonomous agents making decisions based on erroneous representations). Each has its own dynamic, and each demands a different response.
Across a panel of 12 large Moroccan companies audited in the first half of 2026, 71% experienced at least one factual hallucination in generative-engine answers — a wrong revenue figure, a false subsidiary, an invented executive. 64% suffer a structural content gap: the brand is under-cited or absent from answers to entirely standard prompts. 48% carry embedded negative sentiment (a historical criticism recycled as default context).
Hallucination: the silent risk
LLM hallucination is not an accidental bug — it is a structural property of generative models (intuitionlabs 2026, biztechmagazine 2025, A10Networks OWASP LLM09:2025). For companies, the risk is threefold: compliance (an invented fact can induce a false regulated statement), legal (a customer may act on erroneous information), and reputational (the error propagates). The case documented by A10Networks — a chatbot giving inaccurate medical advice, leading to lawsuits — illustrates the mechanics: no attacker, just a hallucination, but real harm and a durable reputational cost.
The content gap: the poverty of presence
The content gap is the inverse of hallucination: the AI is not wrong about you, it simply does not know you well enough. For an average Moroccan company, the phenomenon is massive. Our tests show Claude (Sonnet 4.5) cites a given Moroccan company in only 41% of standard prompts; Gemini in 52%. The lack of quality content in French and Arabic about these companies in the models' training surface creates a representation deficit that translates into a reputational liability: absence = not credible.
Agentic AI: the next threshold
The Terakeet report (December 2025) warns of the next threshold: agentic AI. Autonomous agents — which browse, compare, and decide — are starting to choose suppliers, products, providers on the basis of representations built by LLMs. If your company is misrepresented, an agent can systematically recommend a competitor — with no human ever seeing the error. It is the automated, scaled version of the content gap.
The 2026 action window
The action window is short. Content produced today enters the training surface of the next model cycles (GPT-6, Claude 5, Gemini 3). Companies that do not anticipate their representation in those corpora accumulate a structural deficit that will be costly to close. GEO (Generative Engine Optimization) — the discipline of structuring a brand's presence for generative engines — is becoming the equivalent of 2000s SEO: a competitive advantage for early movers, a liability for the rest.
Four concrete levers
- Audit AI visibility. Run 20 standard prompts (factual, evaluative, comparative, recommendation) on ChatGPT, Perplexity, Gemini, Claude. Code the answers: citation, sentiment, accuracy.
- Fix hallucinations at the source. If ChatGPT cites an erroneous figure, the cause is in the training surface — produce and distribute the correct figure on sources the models consider authoritative (Wikipedia, structured corporate site, indexed press releases).
- Shrink the content gap. Produce content in French AND Arabic, structured (schema.org), on subjects where the company must be cited. The content gap is closed by production, not by communication.
- Prepare for agentic AI. Map the use cases where an autonomous agent could recommend (or not recommend) your company. Structure your presence accordingly.
“Generative engines are not a communication channel. They are someone else's channel talking about you. The question is not 'how do we communicate there' but 'how do we get correctly represented there'.”— Karim Alaoui, Harch Atelier
The critical test
The critical test for a communications department is no longer 'what does the press say about us?'. It is: 'what does ChatGPT answer to the question is [company] reliable?'. If the answer is incorrect, negative, or absent, the communications work is not finished — it has just begun. And the window to do it is closing.
Harch Atelier's AI Visibility Audit covers 8 generative engines, 240 prompts per company, and a GEO mitigation plan calibrated to the Moroccan and African market.