The rapid integration of generative artificial intelligence into search engines and digital discovery platforms has prompted a significant re-evaluation of traditional communication frameworks. Central to this discussion is the PESO Model—an acronym representing Paid, Earned, Shared, and Owned media—originally developed by Gini Dietrich in 2014 to help communications professionals integrate various media types into a cohesive strategy. Recent industry research has ignited a debate over whether the emergence of AI-driven discovery, characterized by large language models (LLMs) such as ChatGPT, Claude, and Google’s Gemini, is disrupting this established model or merely reinforcing its core principles.
Two distinct surveys published in mid-2024 have highlighted a growing divergence in how public relations (PR) professionals and marketers perceive the importance of different media types in the context of AI visibility. The first, conducted by D S Simon, surveyed 168 PR executives and resulted in the report "Earned Media in the AI Era: Disrupting the PESO Model." The second, conducted by Sword and the Script Media, surveyed 200 marketing and communications professionals, revealing a starkly different prioritization of resources. These findings suggest that while the industry agrees on the importance of AI discoverability, there remains a significant lack of consensus on the most effective tactical approach and a concerning gap in how success is measured between agencies and their brand clients.
The Evolution of the PESO Model: From Integration to AI Visibility
To understand the current debate, it is necessary to examine the chronology of the PESO Model. Launched over a decade ago, the model was designed to move PR beyond a singular focus on earned media (media relations) and into a more holistic role that includes paid advertising, social media (shared), and brand-created content (owned). For years, the model served as a blueprint for integrated communications, emphasizing that no single media type should operate in a vacuum.
With the advent of generative AI in late 2022, the landscape of "discovery" shifted from traditional search engine result pages (SERPs) to AI-generated summaries. In this new environment, the goal of communications shifted from simply ranking for keywords to ensuring that a brand’s information is ingested, cited, and recommended by LLMs. This transition led to the conceptualization of "Visibility Engineering," a strategy where owned and earned media serve as the primary signals for AI models, while shared and paid media function as amplifiers for those signals.
The D S Simon report, released in the summer of 2024, argued that this shift constitutes a "disruption" of the PESO Model. However, proponents of the original framework argue that the model was always intended to be a flexible operating system rather than a rigid hierarchy. They contend that the prioritization of earned media by PR executives is not a disruption but a validation of the model’s design: using third-party credibility to fuel broader visibility.
Comparative Data: PR Perspectives vs. Marketing Realities
The data emerging from recent studies reveals a "siloed" view of AI strategy. In the D S Simon survey of 168 brand and agency PR executives, 44% of respondents identified earned media as the most important media type for improving AI discoverability. Paid media followed in second place, with owned media in third. Notably, only 5% of PR professionals selected shared media as the primary driver for AI visibility.
The report further highlighted a near-unanimous agreement on the strategic importance of this shift. Approximately 98% of PR executives stated that optimizing earned media for AI discoverability is "important," and 99% expressed a need to increase the number of earned media campaigns specifically tailored for AI impact. Furthermore, 52% of respondents indicated they are already allocating more than half of their AI-related discovery budgets toward earned media initiatives.
Conversely, the survey conducted by Sword and the Script Media presented a different hierarchy. Surveying 200 U.S. marketing and communications professionals—76% of whom were in-house staff with a marketing lean—the study found that paid media was considered the most influential factor for AI visibility by 35% of respondents. Shared media followed at 28%, earned media at 24%, and owned media was ranked last at 14%.
This discrepancy highlights a fundamental industry trend: professionals tend to prioritize the media types they are most accustomed to purchasing or managing. PR professionals, whose core competency is earned media, view it as the primary lever for AI success. Marketers, who often manage larger budgets for advertising and social media, lean toward paid and shared strategies. This "buy-what-you-know" mentality suggests that organizations may be making strategic decisions based on departmental bias rather than empirical evidence of what influences AI model training and output.
The Technical Mechanism of AI Discovery
The preference for earned and owned media among some strategists is rooted in the technical way LLMs operate. AI models are trained on massive datasets that include web crawls, books, and articles. When an AI generates an answer, it relies on high-authority, third-party sources to validate information. A mention in a reputable, high-traffic news outlet (earned media) acts as a high-quality signal of credibility that an AI model is likely to prioritize.
However, earned media cannot stand alone. For an AI to provide a comprehensive answer about a brand, it needs to link that third-party validation to deep, informative content hosted by the brand itself (owned media). This includes white papers, technical documentation, and detailed "About" pages. Without the depth of owned media, the credibility of earned media has no foundation to land on.
Industry analysts have noted that the marketing-led preference for paid media in AI visibility lacks empirical support. To date, there is little to no evidence suggesting that traditional paid advertising directly influences the organic training sets or the real-time retrieval-augmented generation (RAG) processes of major AI models. While paid media can drive traffic to owned properties, it does not inherently create the "authority signal" that LLMs crave.
The Measurement Crisis and the Confidence Gap
Perhaps the most critical finding in the recent data is the "confidence gap" between agencies and brands. According to the D S Simon report, 87% of agency respondents believe they are successfully optimizing earned media for AI discovery. In contrast, only 59% of brand-side respondents agree with that assessment. This 28-point gap points to a significant disconnect in expectations and reporting.
The root of this disconnect appears to be a lack of standardized measurement. When asked about barriers to success:
- Brands identified a "lack of clear measurement" as their top obstacle (44%).
- Agencies ranked measurement as a much lower priority, placing it fifth on their list of concerns. Instead, agencies cited the "agency/client relationship" and "internal alignment" as their primary hurdles.
This suggests a cycle of frustration: agencies believe they are performing well but cannot provide the data-driven proof that brand managers require. Brands, unable to see a clear link between PR activities and AI search results, remain skeptical of agency claims. This measurement gap is exacerbated by the fact that many traditional PR metrics, such as impressions or ad value equivalency (AVE), are entirely irrelevant in the context of AI discoverability.
Impact and Implications for Strategic Communications
The implications of these findings are twofold. First, they suggest that the PESO Model is evolving into an "OESP" sequence (Owned, Earned, Shared, Paid) for the AI era. In this sequence, owned content provides the substance, earned media provides the validation, and shared and paid media provide the distribution and amplification.
Second, the data underscores an urgent need for new Key Performance Indicators (KPIs). For a PESO strategy to be effective in 2024 and beyond, organizations must move toward a measurement framework that tracks:
- Brand Presence in AI Summaries: How often is the brand mentioned in "AI Overviews" or chatbot responses for relevant industry queries?
- Citation Quality: Which third-party sources are AI models citing when they mention the brand?
- Owned Content Depth: Are the brand’s owned properties structured in a way that AI "crawlers" can easily parse and understand?
For agencies, the path forward involves closing the confidence gap by adopting these AI-specific metrics. For brands, the challenge lies in breaking down silos between marketing and PR departments to ensure that paid budgets are not being wasted on strategies that do not move the needle on AI visibility.
Ultimately, the D S Simon report and the Sword and the Script survey reveal that while the "P" in PESO might stand for Paid, it does not necessarily stand for "Priority" in the age of artificial intelligence. The most successful organizations will be those that treat the PESO Model not as a list of independent tasks, but as an integrated operating system where owned and earned media create the essential signals that allow a brand to be discovered, trusted, and recommended by the next generation of search technology. Rather than disrupting the model, AI has provided the ultimate stress test, proving that integration and third-party validation remain the most valuable currencies in the communication landscape.
