The Evolution of Reputation Management — How AI is Shaping the Future of Branding Success


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In recent years, the field of Public Relations has undergone a significant transformation. It now emphasizes building reputation (most saliently, online) as an overarching goal. PR stresses reputation because it goes beyond what a brand sells or how it came to be; it’s who the brand is.

The changing nature of reputation

Before mass media, reputation was primarily shaped by word-of-mouth and direct experiences. However, as the world became more interconnected and complex, we became interested in companies, brands, and people we couldn’t personally interact with or judge for ourselves. This change led to developing new, more sophisticated ways to assess reputation.

For decades, traditional media played a crucial role in shaping public opinion about brands and individuals. This meant that companies could influence their reputation by smartly influencing or buying media coverage — a practice that continues today, albeit in different forms.

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The internet revolution and the role of search engines

The advent of the internet brought about a seismic shift in how reputations are formed and managed. It created many potential sources for people to draw upon when seeking to understand what a brand or individual is really about.

This digital landscape meant that many different actors could impact reputation. It also offered brands and individuals many opportunities to influence what people read and think about them.

Search engines quickly played a pivotal role in this new digital reputation ecosystem. Early search engines like Yahoo were primarily focused on directing users to specific websites. Over time, especially as Google came on the scene, they evolved to help users find specific pieces of information or answers to queries.

In recent years, search engines have become increasingly sophisticated. They’ve gotten better at understanding user intent and providing more relevant results.

Key developments in search evolution:

  1. Traditional Search Engines (1990s): Keyword-based, limited understanding of context.
  2. Google Dominance (2000s): Advanced algorithms, PageRank and more relevant results.
  3. Semantic Search (2010s): Better understanding of user intent and context.
  4. AI-powered Search (2020s): Conversational, context-aware, synthesizing information from multiple sources.

Google: The accidental reputation snapshot machine

In an effort to satisfy searchers with a variety of sources, Google has inadvertently created a powerful tool for assessing digital reputation. Understanding an entity’s reputation requires bringing together various perspectives — what the entity says about itself, what the media reports, what experts think, what peers say, what consumers experience, and what the general public believes. These are largely the same inputs that Google uses to populate its search results.

As a result, Google search results have become perhaps the most efficient way to get a representative snapshot of an entity’s reputation.

The Wikipedia factor

Alongside search engines, Wikipedia has emerged as a significant source of reputation information. Wikipedia aims to include topics of consequence in their live, crowd-sourced encyclopedia, with facts determined and agreed upon by a community of editors following a set of guidelines. Earned media from reliable sources tends to be the most accepted sources for populating information on Wikipedia.

Wikipedia’s influence extends beyond its own platform. Search engines like Google and Bing rely heavily on Wikipedia — having a Wikipedia page indicates notability. Wikipedia is often featured prominently in Google and Bing search results, including in knowledge panels for branded searches.

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The AI revolution

The emergence of AI models like ChatGPT, Copilot, Gemini and others has ushered in a new era in reputation management. These AI models can do things that Google alone cannot:

  • Understand more complex or nuanced queries
  • Read the content of web pages and extract answers directly
  • Synthesize information gleaned from various sources
  • Formulate responses that directly address queries
  • Provide results in specific requested formats

In an AI-powered world, we expect people to ask more complex questions to get directly to their desired outcomes. While Google-type searching is likely to persist alongside smart AI-powered searches, brands and executives will soon find that their reputation lives both in Google and in the “brains” of a small number of very influential AIs.

As AIs gain more influence, their perceptions about a brand or executive may become the most influential voice impacting decisions on topics ranging from how good a singer someone is, to who should be the president.

Shaping AI perceptions

To help shape how AIs see you or your brand, your best bet is to ensure they see content beneficial to your brand. To do this, you first need to know what they are currently saying. This includes understanding the facts the AI models present, the topics they mention in relation to your entity, and the sources they return in support of their answers. You’ll also want to track any unfavorable content that is being returned.

It will be important to see how this changes over time. Are they the same as before, getting better or getting worse? It will be instructive to compare with peers – do they get the same treatment? Perhaps the AIs are unaware of some of your content or facts, or maybe they are outdated or have confused the entity with another similarly named one.

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Where do we go from here?

As the world of search continues to evolve with the rise of AI, brands and individuals should stay ahead of the curve. By understanding how they’re perceived not just in traditional search engines like Google but also by AI models, clients can take proactive steps to shape their digital reputation and maintain control over their narrative in this new frontier of information, just as they did with traditional media and search.



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