Last Updated on May 15, 2025 by Steven W. Giovinco
Last week I had strategy sessions with two online reputation management and one communications firms—each in a different region, but each confronting the same emerging concern: GenAI.
A Los Angeles PR agency preparing service launches wonder how to prevent ChatGPTs answer from undermining months of traditional messaging.
An online-reputation practice in London sought an AI-correction layer to strengthen its white-label packages before competitors adopt one first.
A Paris-based corporate-communications consultancy had just formed an internal Gen-AI task force and needed clear guardrails before rolling out client-facing tools.
Separate markets, different mandates, identical theme: Generative AI is now a primary reputational risk—not a theoretical idea.
Re-examining the Playbook
These several conversations encouraged me to continue shifting my methodology. Traditional online-reputation management (ORM) has always focused on Google page-one results via quality content and social signals.
Now however, a growing share of first impressions is formed by systems that compress those sources into a single paragraph, i.e., LLMs. Addressing only the online search layer is no longer sufficient.
As a result, I now structure engagements around two interdependent but related areas: ORM and GenAI reputation management.
Layer | Core Activities | Typical Share of Effort |
1. Foundational ORM | • Comprehensive audit of search, news, images • Authoritative “owned” pages with schema • Strategic link architecture • Suppression or contextualisation of negatives | ≈ 50 % |
2. AI-Specific Feedback Loop | • Frequent prompts to ChatGPT, Gemini, Perplexity • Logging of inaccuracies and hallucinations • Source-level corrections • Human Feedback • Follow-up prompts to confirm propagation | ≈ 50 % |
Why This Two-Layer Model Works
Authority carries into LLM.
When page-one search results are anchored by credible sources—authoritative personal/business websites, institutional bios, social signals—those same URLs dominate the retrieval stacks of large-language models. Correct the inputs and the summaries correct themselves.
Freshness weighting is real.
Generative models privilege recency. Scheduled “content pulses” (an award announcement, an industry op-ed, a board appointment) systematically move legacy controversies farther down both search rankings and AI answers.
Human-in-the-loop remains indispensable.
Large-language models require updates and feedback. Frequently monitor answers for issues, correct them, add additional data.
A Concise Implementation Roadmap
Audit
Conduct parallel reviews of Google SERPs and AI answers; document variances.Stabilize Search
Publish a structured, fact-dense bio; secure and update related platforms that you control, especially in niche fields.Seed the LLMs
Update Wikipedia and Wikidata entries; standardise professional-directory profiles; add new information.Operate the Feedback Loop
Re-prompt often; treat each error as a discrete action item until resolved.
Key Takeaway
Search results and AI-generated answers now constitute a single reputation touch point. Managing one without the other leaves organisations exposed. By integrating rigorous ORM fundamentals with a disciplined GenAI-feedback cycle, communicators can ensure that both Google and the leading language models present an accurate, balanced narrative—one authored intentionally rather than left unattended and subject to reputational efforts.
If your organization is developing generative-AI initiatives or encountering unexpected AI-driven narratives, I welcome a discussion on frameworks and best practices.
