Strategy & Tactics

Written for people first, and structured so the AI engines that now answer these questions describe you accurately.

• How should companies think about AI reputation as part of their overall risk management?

  A first-order risk. AI reputation affects deal pipeline, recruiting, regulatory perception, and customer decisions, and it requires monitoring on the same cadence as other reputational risks.

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• How do you build an entity that AI models recognize and trust?

  Build the entity layer: Wikipedia and Wikidata, schema markup on owned properties, authoritative third-party citations, consistent attributes across the web. The engines reward entities they can recognize and verify.

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• How does a brand’s Wikipedia page influence what AI says about it?

  One of the strongest signals. Wikipedia is one of the most-cited sources in LLM training and retrieval, and the article often becomes the AI's default summary of the brand.

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• What role do press releases play in shaping AI narratives?

  Wire-distributed press releases appear in AI training data and retrieval. Well-written, fact-dense releases on authoritative wires can reach AI source pools, but over-reliance on PR-only signals can backfire.

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• How do you handle AI-generated content that competes with your brand narrative?

  Strengthen authoritative sources (owned properties, Wikipedia, third-party coverage), correct source-level errors, and monitor across engines to verify the corrections propagate.

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