By Ronn Torossian

The first time I asked ChatGPT what it knew about me, the answer wasn't wrong. It was incomplete in a way that mattered.

The book was cited. The firm was cited. The Harvard lecturing was missing. One old controversy got two sentences. Two decades of work got half a sentence. It was accurate in the technical sense and completely misleading in the practical one.

That was the moment I understood AI reputation management as a distinct discipline. Not brand reputation. Not search-engine reputation. The specific work of shaping what the machine says when someone asks about you.

Here is the five-step framework I now use — on myself, on the executives I advise, and in every serious reputation engagement.

Step 1: Audit

You cannot fix what you have not looked at. Run 30–50 queries about yourself, your brand, your firm, and your category across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.

Log four things per query:

  • The full response text (or a summary of it)

  • The sources cited

  • The tone — positive, neutral, negative, mixed

  • The factual accuracy — right, wrong, incomplete

Include adversarial queries. "Controversies involving [name]." "Criticism of [brand]." "Problems with [firm]." The engines will answer whether you look or not.

Step 2: Diagnose

Every audit produces four categories of finding. Sort your data into them.

  • Wrong. Factual errors the engines are stating as fact. Wrong company, wrong role, wrong date, wrong credit.

  • Incomplete. True but missing significant context. Book cited, career missing. One title mentioned, current title absent.

  • Overweighted negatives. Old controversies, resolved matters, or minor incidents given more prominence than they warrant.

  • Silent. Categories where you don't appear at all — but should, given your work.

Each category calls for a different intervention. Sorting matters.

Step 3: Correct the Founding Facts

The engines are only as accurate as the sources they cite. Fix the sources.

  • Wikipedia. The most-cited source across every major engine. Accurate, well-cited, actively maintained. If yours is out of date, that is priority one.

  • Wikidata. The underlying knowledge graph. Entity accuracy here shapes how engines resolve who you are.

  • LinkedIn. Complete title, current role, accurate history.

  • Company sites. Bio pages, About pages, press pages all up to date and consistent.

  • Executive bio hosted on your own domain. Canonical, structured, machine-readable.

Do this before anything else. Every subsequent intervention lands better on top of accurate foundational sources.

Step 4: Reweight the Narrative

You cannot delete what the engines already know. You can reweight what they emphasize.

The mechanism is volume of substantive, forward-looking work. Consistent bylines, speaking engagements, new coverage, new milestones, published research, ongoing contribution to the field. The engines summarize the totality of what they see. Over time, adding significant new signal shifts the summary.

This is slow. Three to six months to see meaningful movement. Twelve months to see structural change. There are no shortcuts here. Attempts to accelerate — through paid content, low-quality volume, or manipulation — either fail or reverse the progress.

Step 5: Monitor and Recalibrate

Reputation is a leading indicator, not a set point. Set a cadence. Re-run the audit monthly. Watch the trend line, not the individual answer.

Every material change — a new hire, a launch, a book, an award, a coverage moment, or a crisis — should trigger a recheck. The engines integrate new information on their own schedules. Knowing when they have caught up matters.

What This Framework Is Not

  • Not takedowns. You can't take down what the engines already trained on. Chasing takedowns burns time that would be better spent on the substrate.

  • Not paid boosts. Every promise I have seen in this category has failed to deliver durable results.

  • Not prompt engineering. You cannot ask the engine to remember your preferred narrative. It doesn't work that way.

  • Not brand campaigns. Advertising has essentially zero direct impact on AI-engine responses.

The Bottom Line

AI reputation management is disciplined, patient work on the sources the engines already cite. Audit. Diagnose. Correct the founding facts. Reweight the narrative through sustained new signal. Monitor the trend line.

The executives and brands doing this deliberately will define how they are described in the answer layer for years. The ones ignoring it will discover — often the hard way — that the paragraph the machine writes about them was authored by whoever was paying attention.

FAQ

What is AI reputation management?

The discipline of shaping how AI engines describe a person, brand, or firm when asked. It combines source correction, Wikipedia and Wikidata accuracy, consistent owned and earned publishing, and ongoing monitoring across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.

How is AI reputation management different from ORM?

Online reputation management focuses on search-engine results. AI reputation management focuses on the answers generated by AI engines. The mechanisms overlap but are not the same — an outcome that improves your Google results may or may not improve your ChatGPT results.

Can negative AI-engine results be removed?

Not directly. The engines cannot be forced to forget what they have trained on. But sustained corrective work — accurate Wikipedia, sustained new coverage, substantive owned publishing — can reweight how negatives are summarized within broader answers.

—

About the author

Ronn Torossian is the founder and chairman of 5W AI Communications, the AI Communications Firm. He is the publisher of Everything-PR and the author of two best-selling editions of For Immediate Release.