The competition involving AI in online reviews: How companies are combating fraudulent content.

      What was once a straightforward indicator of trust has evolved into a space where potential customers feel compelled to be vigilant. Reviews, encompassing star ratings and written feedback, have been overtaken by generative AI, automation, and more frequently commissioned reviews. As large language models (LLMs) reduce the cost of producing content at scale, online reputation poses a greater risk for consumers. Currently, online reputation management (ORM) involves addressing AI safety, governing platforms, and building a credible infrastructure.

      The increase in fake reviews

      Fake reviews are no longer merely penned by paid individuals, but have been fully industrialized. Some estimates suggest that billions of dollars in global consumer spending are swayed by fraudulent or manipulated reviews, with analyses indicating the total economic impact could reach hundreds of billions.

      The issue extends beyond negative assaults on businesses. A significant portion of insincere reviews consists of five-star ratings designed to enhance a product's visibility, manipulate ranking algorithms, and push aside legitimate competitors.

      Generative AI has exacerbated this situation. Newer LLMs can produce contextually aware, emotionally appealing reviews that reference specific product features, details, or subtleties derived from other online reviews. When bot networks access older accounts, these systems can generate entire campaigns of reviews that bypass traditional anomaly detection filters. For platforms, the balance between authentic and fake reviews is deteriorating more rapidly than the filtering systems can adapt.

      Why the review economy is inherently flawed

      The belief that an increase in reviews equates to greater trust has been shown to be misguided. In reality, artificially positive reviews skew consumer perceptions just as severely as low-rating attacks. Both scenarios compromise fair competition in the market and the long-term credibility of brands.

      Small and mid-sized businesses bear the brunt of this problem. Many operate in small or niche markets, where even a few reviews can greatly enhance their customer base. This situation creates fertile ground for fraudulent activities: unscrupulous individuals threaten to unleash waves of fake negative reviews unless businesses pay them to avoid reputational harm. Given that platforms often employ slow and manual dispute resolution processes, the advantage typically rests with the attackers.

      Once trust is compromised, the market shifts to prioritize those who best understand how to exploit the system rather than rewarding genuine quality. At that stage, reputation becomes less about customer experience and more about resilience in a different economic landscape.

      Platform vulnerabilities: The emergence of ORM as a technical field

      Leading review platforms utilize a combination of automated categorization, heuristics, and human moderation. While this approach is generally effective against low-effort spam bots, these systems falter when faced with more sophisticated challenges, such as reviews that appear factually plausible, sound human-written, and are statistically “normal” when viewed in isolation.

      The inadequacy of updated review technologies has given rise to a more technical approach to online reputation management. Modern ORM emphasizes reverse-engineering platform mechanics. Practitioners scrutinize the metadata of reviews, user account histories, posting frequencies, linguistic irregularities, and adherence to platform policies to establish whether content breaches guidelines.

      Reputation management firms operate as specialized compliance and diagnostics teams. They enforce platform-specific rules, identify infringements, and navigate formal dispute procedures with concrete evidence. This marks a significant shift from previous practices that often unwittingly allowed artificial reviews.

      A case study for the new ORM model

      Erase.com exemplifies this newer generation of ORM services. It functions within established platform and search engine frameworks. It does not merely eliminate negative reviews but also assesses whether content aligns with policy standards for authenticity, relevance, and user experience.

      The company performs extensive review analyses, applies platform-specific dispute processes, and remediates search results based on documented guidelines. The focus is on data-driven arguments, enabling swift defense against malicious attacks. While this is not the sole company employing this new ORM model, it illustrates how reputation management has become an essential layer for many businesses addressing systemic review weaknesses.

      Working towards an industry-wide response

      The future of trustworthy reviews appears grim if platforms persist with their current practices. Various new solutions are under exploration. Real-time AI-assisted verification tools could identify suspicious content prior to its impact on rankings, while a blockchain-based system may provide stronger authenticity assurances.

      Simultaneously, consumer awareness remains crucial. As AI-generated content proliferates, signs of trust may emerge from subtler factors, such as a reviewer's history, their choice of language, and verification on the platform. Ultimately, the battle against fake reviews cannot be fought in isolation. As automated content becomes more advanced, online reputation management will emerge as a vital discipline for sustaining trust.