Aisha Amaira is a distinguished MarTech expert whose career has been defined by a deep-seated passion for the intersection of technology and consumer behavior. With an extensive background in CRM systems and customer data platforms, she has spent years helping businesses translate complex data points into actionable marketing strategies. As the digital landscape shifts toward AI-driven search, her focus has evolved to address how these emerging technologies influence brand perception and long-term reputation. In this conversation, she breaks down the critical differences between content removal and suppression, the legal shifts impacting digital records, and why modern reputation management requires a rigorous, data-centric approach to handle the nuances of AI-generated answers.
AI tools now drive nearly half of local business recommendations, often highlighting negative reviews that traditional search used to keep hidden. How is this shift in consumer behavior changing the way businesses need to think about their online reputation?
The shift we are seeing is truly seismic, moving from a world where consumers browsed to one where they are simply given an answer. According to recent data from the 2026 Local Consumer Review Survey, 45% of consumers are now using AI tools like ChatGPT for local recommendations, a staggering jump from just 6% a year ago. This means the “invisible” results on page two of Google, which businesses used to ignore, are now being fed directly into large language models and presented as facts. When an AI summarizes your business, it doesn’t care if a negative review is buried; it pulls that sentiment into the spotlight, making it feel more urgent than ever. We are seeing that users only click through to a traditional search result about 8% of the time when an AI summary is present, which effectively cuts traffic to original sources in half. This forced transparency means businesses can no longer rely on burying their problems; they have to address the source material before the AI weaves it into a permanent narrative.
When a client sees a negative link, their first instinct is often to “get it taken down,” but you suggest there are actually three distinct outcomes. Could you explain the differences between removal, deindexing, and suppression, and why choosing the right one is so vital in the age of AI?
It is a common mistake to use the word “removal” as a catch-all term, but conflating these three strategies is where most of the wasted effort and budget occur in reputation management. True removal means the source page is completely gone—deleted by the publisher or taken down by a court order—and this is the only way to ensure the content stops feeding into AI training sets. Deindexing is more of a middle ground where the page exists but Google no longer shows it in search results, though it remains live for anyone with the link or for scrapers to find. Suppression is the most traditional method, where we build new, positive content to push the negative link below the visible fold, but this is increasingly the weakest defense against AI. Because an AI model retrieves information from a vast corpus regardless of search position, a negative review at position ten carries the same weight as one at position three. Our internal data shows that requests to address negative AI Overviews have spiked by 215% year-over-year, proving that simply pushing a link down is no longer enough to keep it out of the conversation.
Removing a news article from a legitimate outlet is notoriously difficult. What are the realistic paths to success here, and what role do editorial initiatives like the “Fresh Start” program play?
You have to understand that Google will almost never deindex a legitimate news article just because you ask; you have to go to the source. The most effective path is often a factual correction, where you provide names, dates, and documentation to prove a specific error that reframes the entire damaging claim. We are also seeing a heartening trend in journalism where outlets like The Boston Globe through their “Fresh Start” initiative and Cleveland.com’s “Right to Be Forgotten” program allow people to petition for the unpublishing or anonymizing of old stories. These programs are designed for non-public figures whose lives are being haunted by resolved legal matters or minor past mistakes. However, even if you win the battle with the original publisher, you must be hyper-aware of syndication cleanup. One article can be mirrored across dozens of scraper sites, and those syndicated copies are often exactly what AI systems cite, meaning the work isn’t done until every copy is addressed.
Legal records and mugshots can be devastating to a professional reputation. How has the legal landscape changed regarding these sites, and what steps should someone take if their charges were dismissed?
The tide has fortunately turned against predatory mugshot websites that used to charge exorbitant fees for removal. Many states have now enacted statutes making it illegal for these sites to charge for taking down photos, and major payment processors have cut off their ability to monetize this practice. If your charges were dropped, dismissed, or legally expunged, you have real leverage because these records should no longer be public. Before you send a single email, you must pull your official dismissal paperwork or expungement certificate, as a request backed by a legal document is much harder for a site to ignore. We have seen requests for these types of removals fall by more than half since 2023 at Erase.com, largely because Google’s crackdown has pushed these sites out of visibility. It is crucial to sequence your actions correctly: seal or expunge the record first through the court, then pursue the digital removal, otherwise, you’ll find yourself re-litigating the same issue with every site owner.
Data brokers seem to have a never-ending supply of personal information like home addresses and phone numbers. Is there a systematic way to manage this volume, or is it a losing battle?
Managing data brokers is less of an adversarial battle and more of a tedious, ongoing maintenance project. There are more than 500 data brokers registered in California alone, and they are constantly refreshing their datasets from public records, which is why a one-time opt-out often fails when the information reappears months later. The good news is that tools are becoming more centralized, such as California’s DROP platform, which will allow residents to send a single deletion request to all registered brokers starting August 1, 2026. In the meantime, Google’s “Results About You” tool is a powerful free resource that monitors your name and address and can even catch exposed government ID numbers like Social Security or driver’s license numbers. You have to treat this like digital hygiene—it’s something you check on a recurring basis rather than a problem you solve once. The volume is high, but because these sites generally have established opt-out processes, it is a very solvable problem if you stay disciplined.
Reddit and forum threads are becoming increasingly influential in AI summaries. Why are these so difficult to remove, and what is the best way to approach moderators or platform policies?
Reddit is a unique challenge because the platform will not remove a thread simply for being unflattering, and moderators are often protective of their community’s discourse. However, requests to address threads in Google’s “Discussions and forums” module have nearly tripled over the last 18 months because these threads are being disproportionately cited by AI. If a post violates subreddit rules or contains genuine harassment or personal information, your first step should be the in-app report tool, but you must be accurate about the category of the violation. If that fails, a direct, factual message to the moderators via modmail is the next step; you should cite the specific rule being broken rather than just complaining that the post is mean. You also have to realize that a thread with only a few dozen upvotes can be scraped and quoted in roundup posts, which means the original thread might not even be your main problem anymore. If the claim has been screenshotted or quoted elsewhere, you have to pivot your strategy to those downstream copies to actually move the needle.
Once a piece of content is purportedly removed, how can a business or individual verify that it’s actually gone from the digital ecosystem?
The old way of checking your reputation was a simple rank check on Google, but that is completely insufficient in the current environment. A page can be deindexed from search results while the claim it contains continues to live on in AI Overviews and assistant answers, often because the model absorbed the information before the takedown happened. You need to run specific branded queries against ChatGPT, Perplexity, and Google’s AI tools to see what citations they are providing in their summaries. This citation list is the most valuable document you can have because it gives you a finite list of URLs that are actually shaping the narrative. If you see a “clean” result once, don’t celebrate yet; outputs vary from run to run, so you need to repeat these checks weekly to ensure the narrative has truly shifted. Monitoring the sources the AI cites is the only way to know if your removal efforts have actually broken the feedback loop.
What is your forecast for the future of online reputation as AI becomes more integrated into our daily search habits?
I believe we are entering an era where “reputation” and “data integrity” will become synonymous. As AI summaries become the primary way people consume information—already appearing in about one in five Google searches and up to 60% of question-style queries—businesses will have to be much more proactive about the data they leave behind. We will see a shift away from “content volume” strategies toward “source authority” strategies, where the goal is to ensure that the facts feeding these models are accurate and verified at the root. The era of just burying bad news is ending; the future belongs to those who can manage their digital footprint with the same precision they use for their financial records. If you can’t control the source, you can’t control the answer, and in an AI-first world, the answer is all that matters to the consumer.