Responding to a Crisis

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

• How do you assess the severity of a reputation crisis?

  Assess severity across four signals: media reach (which outlets, what tier), social amplification rate (whether the story is gaining or losing reach), AI narrative formation (how many engines have absorbed it and how confidently), and stakeholder reach (whether investors, regulators, customers, or employees are calling). Read together, the four produce a far more decision-useful severity picture than any one alone.

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• What is a reputation crisis escalation framework?

  A reputation crisis escalation framework is a pre-agreed, one-page plan that maps specific triggers (event types, coverage thresholds, AI narrative signals) to tiered responses, each with a named owner, explicit decision rights, the tools that get activated, and a response-time commitment (SLA).

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• I have a common name and someone else’s scandal is hurting my search results. What can be done?

  This is a solvable technical problem called entity disambiguation: you give each same-named person distinct Person schema with unique identifiers and sameAs links to their own reference set (LinkedIn, professional bios, Wikidata, and Wikipedia where notable), so search and AI engines learn to treat them as two separate identities and stop attaching the other person's scandal to your name.

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• How does negative press affect Google search results long-term?

  Negative press can persist on page one for years if it is authoritative and unaddressed. Google has no built-in time-decay, so durable displacement requires sustained authoritative competing content plus source-level work on the underlying narrative.

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• How do you manage AI search results during a reputation crisis?

  Managing AI search results in a crisis is a daily, source-level loop: stand up monitoring on the specific narrative threads across the AI engines, work out which sources each engine appears to be drawing on, place authoritative counter-content on the properties the engines already trust (including Wikipedia through Talk-page edit requests backed by reliable secondary sources), then track whether the corrected picture is being absorbed and repeat where it is not.

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