The New Marketing and Public Relations Ethics Challenge: Artificial Intelligence (AI)

Even though almost all AI platforms warn users that “AI can make mistakes,” ChatGPT remains the fifth-most-visited website in the world, according to data from  Meltwater’s Digital 2026 Global Overview. And, 50% of Google searches now generate AI summaries, according to a recent McKinsey & Company report. These statistics mean consumers and decision-makers are relying on AI-generated search summaries and LLM (large language model) outputs as primary sources of information and truth. And that’s the root of some formidable marketing and public relations challenges.  

Eight Marketing and Public Relations AI Challenges in 2026 and Beyond

1. The Decline in Content Quality

An over-reliance on generative AI has caused a decline in content quality and effectiveness. While AI excels at boosting productivity— speeding up tasks by 15–26% and generating content in a fraction of the time it takes humans to craft it—Generative AI (Gen AI) often produces average work that mimics average human intelligence. 

Not only that, but leading firms like Gartner and Deloitte have found that AI often "hallucinates" (delivers incorrect or completely fabricated information). Deloitte fell victim to that phenomenon when it used Gen AI to produce a report for the Australian government. The report was laced with serious errors caused by Gen AI’s misinterpretation of data and “hallucination” of fake data (to fill in gaps), including a fabricated quote from a federal court judgment and references to nonexistent academic research papers. These errors and fabrications rendered the report useless and potentially damaging. While Deloitte corrected the errors, they were nonetheless required to pay 97,000 Australian dollars ($63,000 USD) in damages. 

2. Difficulty Getting Content to Cut Through

The decline in content quality and effectiveness is not the only challenge AI presents. An even more daunting challenge is the increasing difficulty in making authentic, human-driven narratives cut through and stand out in a crowded landscape of AI-generated content. More than 50% of new online articles are generated by AI, with AI-generated content overtaking human-written content in volume as of late 2024. And, more than 74% of web pages contain AI-generated material. Marketers share the blame, though, with 82% now incorporating AI tools to create blogs, emails, and social posts.

Because I create a great deal of content for clients every day and have never used AI beyond conceptualization, I decided to experiment by prompting ChatGPT to generate an eBook. It took only about 20 seconds for it to render a chapter-by-chapter “manuscript.” The output wasn’t impressive and wasn’t exactly a manuscript. It was something akin to an annotated outline that was laced with generalities and overused business clichés. By no means was it what any serious content writer or editor would consider a publication-ready eBook. To refine the output to that point would have taken a bit of time, effort, and more than one refined prompt. 

Findings from Melbourne researchers echo what I was thinking as I reviewed the output of my Gen AI experiment: when accuracy is essential, AIs (even cutting-edge ones) make errors so frequently that the extensive human oversight required to catch them makes the entire effort less productive than not using AI at all. 

That said, Gen AI does have its place in marketing and PR. I use it as a force multiplier when I’m repurposing content. For example, if I’m repurposing long-form content (eBook, white paper, etc.), transforming it into multiple shorter pieces of content like social posts, email nurtures, paid media, infographics, and blog posts, AI’s speed and agility in recrafting excerpts of it to fit those formats accelerates production and publication. The faster those derivative pieces of content are produced, the faster the message reaches audiences that may never access or read the content in its original long-form format. Since AI isn’t prompted to create any new content, there is no opportunity for it to hallucinate or misinterpret information. In this application, AI’s sole task (and therefore the prompt I give it) is to pull information verbatim from the longer piece of content and then quickly and efficiently repurpose it into the short-form content I prompt it to create. 

Of course, I have to check AI’s output to ensure that it has precisely followed my instructions, but AI can produce those repurposed pieces of content in seconds rather than the minutes it would take me. 

3. Context Blindness and Cultural Incoherence

 AI can’t “read the room.” It simply lacks the human wisdom and intuition to do so. As a result, AI models struggle to grasp human intent, regional sensitivities, and the velocity of narrative momentum, which can lead to "context blindness" (content that may be technically accurate but is contextually misleading or culturally inappropriate). 

4. The Fake and Manipulated Content Crisis

 AI tools have lowered the barrier for creating convincing, fake, and manipulated content. Also referred to as “deepfakes,” these inauthentic pieces of content fuel a constant frenzy of disinformation that has already caused serious damage to the reputations of individuals, brands, and companies. This has left marketing and PR teams scrambling to transition from tracking mentions and customer sentiment to gathering what’s referred to as " narrative intelligence,” leveraging tools like  PeakMetrics to monitor online narratives for reputation threats so they can proactively manage crises.

5. AI-Driven "Dark Visibility" and Reputational Risk 

As consumers move from searching on Google to asking AI platforms for information, brands face "dark visibility"—a marketing and SEO concept that refers to a brand's influence on AI-generated answers from platforms like ChatGPT, Copilot, Claude, Gemini, and Perplexity. This influence occurs without generating traceable, click-through web traffic. 

The problem is that click-through traffic is a major conversion driver (a trigger that compels website visitors to move from passive browsing to taking a desired, measurable call to action—in this case, clicking through to the company’s website). These drivers increase revenue and conversion rates by enhancing user experience and building trust. No click-through traffic makes conversions a much heavier lift for marketers. Moreover, the lack of click-throughs affects search engine ranking. 

6. The Brand Preference “Black Box”

The increasing use of AI for search and research means that brand preference is now being formed within a "black box" of LLMs (Large Language Models), where, to build a brand profile, AI may have pulled information from old, outdated, or inaccurate sources like old Reddit threads or outdated customer complaints. This is making it harder for marketers and PR practitioners to correct the narrative. 

7. Proving Value in a "Zero-Click" World

With roughly 60% of Google searches—and over 75% of mobile searches—ending without a click, the digital marketing landscape has shifted from a "click economy" to a "zero-click" environment. According to an April 2026 article published by Search Engine Land, users are increasingly satisfied with answers provided directly on the Search Engine Results Page (SERP) through:

• AI overviews (AI-generated summaries at the top of search results, using Gemini to provide quick, comprehensive answers with linked sources)

• Featured snippets (selected search results, displayed in a box at the top of Google’s search results, known as "Position Zero,” designed to answer user queries immediately)

• Knowledge panels (automated information boxes appearing on search results for entities like people, places, organizations, and things)

Proving value in this "zero-click” search landscape necessitates shifting from measuring volume (clicks, sessions) to measuring influence—visibility, brand authority, and on-SERP actions (user interactions that occur directly on the Search Engine Results Page (SERP) without clicking through to a website). 

8. The New Optimizations for Search Visibility

Because consumers are searching via AI, it’s no longer enough to master SEO best practices to elevate page ranking and brand visibility in search engines. Visibility in today’s AI-assisted search is achieved through Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO)—two relatively new yet essential skills.  

While GEO builds on traditional SEO, it shifts the focus to aligning with how AI-enabled search platforms rank and showcase content. SEO has long been about getting your website to the top of search results, but GEO is about ensuring your content is the Gen AI chooses to generate. Here’s the shift: whereas SEO emphasizes text-based pages and backlinks, GEO prioritizes adaptable, multimodal content, including images, videos, and interactive media. And whereas SEO may reward keyword density, GEO prioritizes context, clarity, and conversational tone.

AEO is all about being the answer AI search delivers, not just a link in the results. This new currency of digital visibility focuses on providing concise, direct answers to specific user questions to improve visibility in the conversational, "position zero" era (position zero being the featured snippet displayed at the top of Google search results, appearing above the first organic link to directly answer a user's query). It prioritizes making your content the answer that engines deliver to users, whether through featured snippets, voice assistant responses, or AI-powered chat results.

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