Emerging Scenarios

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

• How should law firms manage what AI says about their practice areas and cases?

  Law firms should monitor AI engines at three levels, the firm overall, each major practice area against peer firms, and named partners individually. The authoritative sources AI engines draw on for legal queries are concentrated: Chambers and Legal 500 directories, the ALM trade press, the firm's own website, and Wikipedia articles for the most senior partners. Keeping those sources accurate is the primary lever.

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• How should private equity firms manage their AI reputation during fundraising?

  Run an AIQ-style audit on the firm and the named principals well before the formal fundraise, monitor the allocator-style prompts LPs would ask, and fix the gaps in the underlying sources so the engines return materially different answers by the time LP diligence begins.

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• How do you prepare for AI search engines that can browse the web in real time?

  Real-time browsing AI engines, including Perplexity, ChatGPT Search, Gemini, and Google AI Overviews, retrieve and respond from newly published sources within minutes to hours, not on a training cycle. Preparing for them means running earned media, structured data, and AI monitoring at that same speed, so you shape the live narrative while it forms rather than explain it after the fact.

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• How will multimodal AI search affect reputation management?

  Multimodal AI search treats images, video, and audio as first-class inputs and outputs, not just text. Reputation work expands accordingly, image SEO (alt text, structured data, captioning), video transcripts, and audio content, all anchored by strong entity signals the engines can use to disambiguate.

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• How should healthcare companies manage AI-generated health information that mentions them?

  Healthcare companies need rigorous AI monitoring because incorrect medical claims tied to the brand can cause patient harm and compliance exposure, not just reputational damage. A 2025 peer-reviewed study found AI engine consistency on clinical questions ranged from 'unacceptable' to 'questionable', which confirms this is a documented, measurable risk. Remediation has to be grounded in authoritative medical sources and clear corrective content.

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