As someone deeply invested in the world of public relations, I’ve witnessed remarkable changes in how AI is reshaping our industry. It’s not just about innovation; it’s about staying ahead in a rapidly evolving landscape. Let me guide you through how AI PR is transforming the way we do business.
One crucial aspect of this transformation is the importance of citations in AI-generated answers. It’s vital that the information we use is both credible and traceable, ensuring that our strategies remain effective and trustworthy.
Additionally, understanding LLM (Large Language Model) visibility is key to making the most of AI capabilities. The visibility of these models determines how well they integrate into our PR strategies, impacting overall success.
For PR teams like mine, adapting our strategies in response to these changes is more important than ever. Staying agile and informed allows us to navigate this new era with confidence and creativity.
Have you ever wondered how different types of social content can influence AI visibility? Well, I’ve delved into this fascinating topic to uncover the ways platforms like YouTube and Reddit, along with long-form content, enhance AI citation.
Understanding the mechanics of how social platforms shape AI visibility is crucial in today’s digital landscape. In my exploration, I discovered that YouTube and Reddit are particularly powerful in driving AI citations, thanks to their unique content structures and engagement models.
Long-form content, known for its depth and comprehensive nature, is another player in this arena. Its ability to provide detailed insights makes it a preferred format for AI learning and referencing.
As I delve into the ongoing data battles, I’m struck by how they’re reshaping the AI landscape and the answers we rely on. It’s fascinating to observe the pivotal deals, restrictions, and lawsuits that are creating a fragmented visibility landscape in AI.
This journey through 2023 to 2026 reveals how platform shifts are altering the way data access impacts AI answers. Each step is integral to understanding the changing dynamics of this tech-driven era.
As someone deeply engaged in the world of AI, I’m excited to share how leading agencies are empowering brands to achieve AI visibility, optimize LLM citations, and maintain discoverability across tools like ChatGPT, Gemini, and Perplexity.
These expert agencies are paving the way for businesses to thrive in an AI-driven landscape, ensuring brands don’t just survive but excel in AI search environments.
As I delve into the world of social media, I’ve realized the game-changing potential of choosing the right agency. A social media agency that specializes in AEO, or Answer Engine Optimization, can make a world of difference for brands like mine. By turning ordinary social content into AI-enhanced visibility, citations, and growth, these agencies help elevate our online presence to new heights.
Picture this: content I share on social media isn’t just engaging with my current audience. With AEO strategies, it’s also becoming more discoverable to a broader, more relevant audience through artificial intelligence systems. That’s the kind of growth I aspire to achieve, don’t you?
The journey of understanding an AEO-centered strategy is more enriching when I collaborate with experts in the field. It’s not just about boosting AI visibility; it’s about crafting a roadmap that ensures my brand remains ahead of the curve. This is more than mere social interaction—it’s about creating a meaningful impact and fostering genuine engagement numbers that reflect in my brand’s growth.
See how Goodie’s Social Optimization Suite helps brands track social AI citations, identify what drives visibility, and act on platform-specific insights.
I’m thrilled to introduce you to Goodie’s Social Optimization Suite. It’s designed to empower brands by tracking social AI mentions and uncovering what truly enhances visibility. With our suite, you can easily harness platform-specific insights to make informed decisions.
I’ve always been fascinated by the evolving landscape of AI and its impact on search optimization. Recently, I’ve been diving deep into platform updates, proprietary research, and the latest optimization strategies emerging from the AEO category.
One article that caught my eye is “9 Top ChatGPT Optimization Tools for Better Visibility” by Emily Axelsen, which was published on October 10, 2025. It offers incredible insights into boosting visibility using ChatGPT.
Julia Olivas also provides a deep dive into crafting an LLM-friendly content strategy, which she explores in “AEO & AI Content Marketing,” released on December 19, 2025. Her insights are invaluable for anyone looking to align with AI advancements.
Understanding the differences in optimization strategies with the article “AEO & GEO vs SEO” by Daria Erzakova, published on August 20, 2025, also expanded my perspective significantly.
In addition to these, various other posts delve into AEO research frameworks, technical foundations, and social optimization. I personally found the analysis in Michael Saltz’s “Social Optimization Suite” from March 17, 2026, to be enriching, emphasizing the importance of owning conversations that truly matter.
Even more, on March 16, 2026, Julia Olivas published about the necessity of having a social media agency adept in AEO, adding depth to my understanding of agency capabilities in today’s digital world.
The timeline of “LLM Data Wars: Deals, Restrictions & Platform Power Plays (2023-2026)” by Julia Olivas, published on March 9, 2026, reveals intriguing narratives about the competitive landscape of AI platforms.
Mostafa Elbermawy’s study on March 5, 2026, explores the power of social platforms and content types in shaping AI visibility, adding more context to these discussions.
For those interested in AI PR, Michael Saltz’s “From Mentions to Citations” on March 4, 2026, provides a fresh perspective on how PR strategies are evolving in the AI era.
The guide on schema markup by Ollie Martin, published March 2, 2026, is comprehensive for anyone looking to enhance AI search. It’s a must-read if you’re diving into AI search optimization.
Lastly, Daria Erzakova’s work on aligning social, SEO, PR, and content for AI search dominance, from February 20, 2026, encapsulates a forward-thinking strategy for today’s digital landscape.
I recently discovered that Google Ads now includes an auto-apply setting for its experiments feature, which is activated by default. This means that once an experiment determines a winning variant, it can automatically implement that change without waiting for manual review. A real time-saver, but there’s more to consider.
Here’s how it works: as advertisers, we can select between two modes when evaluating results – directional outcomes or statistical significance with varying confidence levels of 80%, 85%, or 95%. However, it’s reassuring to know there’s a safety net; if any chosen success metric performs significantly worse during testing, the system won’t proceed with automatic changes.
Why it matters to me. Experiments are incredibly powerful within a Google Ads account, allowing us to test ideas without risking the existing campaign’s performance. While automating the application of results could streamline testing phases, this process eliminates a crucial checkpoint where we often catch unintended outcomes that might impact active campaigns.
The potential pitfall. One limitation is that experiments currently accommodate only two success metrics. This might mean that a third, important metric could suffer unnoticed if it’s not one of the chosen ones, as the system’s guardrails only protect what we’ve explicitly instructed Google to watch, not every significant factor.
The takeaway. While the auto-apply feature serves as a helpful shortcut for straightforward tests, when conducting significant experiments, it’s worth going the extra mile for manual review. It’s best to let the experiment play out fully, ensure accuracy and thoroughness, and examine all data before making a final call.
First observed by professionals. This update did not go unnoticed; it was first picked up by Google Ads specialist Bob Meijer, who shared his insights on LinkedIn.
I recently came across some fascinating insights into the world of SEO tools and how they’re evolving. It turns out, marketers are swapping SEO platforms less frequently now, mostly due to AI advancements, tightening budgets, and shifting search dynamics.
In 2025, SEO tools emerged as the most commonly replaced martech application. You might think this indicates a problem, but there’s more to it. According to the 2025 MarTech Replacement Survey, for the first time, SEO platforms surpassed marketing automation platforms in replacements, a leader for five years.
At first, this replacement trend could appear as instability within SEO. With the arrival of large language models, AI-generated answers, and zero-click search experiences, traditional keyword tracking and ranking-based methods face challenges.
However, the survey data reveals a more complex narrative.
SEO Tools: Most Replaced, Yet Stabilizing
Despite being the most replaced category in 2025, the rate of SEO tool replacements actually slowed down compared to previous years. This indicates that while I’m seeing changes, there’s also increased stability.
This shift points to maturation. It seems we’re consolidating, upgrading, or refining our SEO toolkits as search methods evolve rather than causing widespread churn.
Meanwhile, other significant martech categories experienced sharper annual decreases in replacements:
CRM replacements dropped over 12% from 2024 to 2025, hitting an all-time survey low.
MAPs, email platforms, and CMS tools also saw declines compared to 2024.
Why SEO Tools Are Being Replaced
With stability not being the primary driver, you might wonder what’s fueling the change in SEO tool replacements. The survey highlights three main reasons:
1. AI Capabilities
The survey incorporated questions about AI’s role in replacement decisions for the first time, revealing its substantial impact.
37.1% of respondents considered AI capabilities crucial.
33.9% desired AI features in new tools.
This shift reflects the growing trend of SEO platforms rapidly adopting AI for tasks like content generation, SERP analysis, and workflow automation.
In many cases, swapping an SEO tool isn’t about leaving SEO behind; it’s about upgrading to incorporate AI capabilities.
2. Cost Pressures
Cost considerations significantly influence martech tool replacements, including SEO tools:
In 2025, 43.8% of marketers cited cost reduction as their reason for replacing applications, a sharp increase from 23% in 2024 and 22% in 2023.
This indicates growing pressure to evaluate overlapping tool functionalities and optimize the SEO tech stack effectively.
3. Changing Needs in a Shifting Search Landscape
As search trends evolve, so do the expectations for SEO platforms. Traditional rank tracking and keyword monitoring aren’t adequate anymore. Many teams are now looking for tools that can:
Provide insights across AI-driven SERPs
Track visibility beyond just clicks
Integrate more seamlessly with wider marketing and data systems
This evolution partially drives the ongoing replacements, even as the overall landscape becomes more stable.
AI Is Reviving Custom-Built SEO Tools
A remarkable trend from the 2025 survey is the comeback of custom-built solutions for SEO processes.
Homegrown applications made up:
8.1% of replacements in 2025, increasing from 3.4% in 2024 and 5% in 2023.
This marks a shift after years of depending almost entirely on commercial platforms.
“AI-assisted coding is changing the calculus of build versus buy,” explained martech analyst Scott Brinker. “Building is now faster and easier. Companies should still purchase applications where they lack a competitive edge. However, where they can differentiate through tailored solutions, custom-built software is gaining appeal.”
For SEO teams, this trend could see more organizations developing:
Custom data pipelines
Unique SERP tracking systems
AI-driven analysis tools customized for specific requirements
Other Martech Categories Show Even Greater Stability
While SEO tools led in replacements, the broader martech field is stabilizing.
Several key categories recorded reduced replacement rates in 2025:
CRM platforms (down over 12% year-over-year)
Marketing automation platforms
Email distribution tools
Content management systems
This trend suggests that many organizations are sticking with core systems while selectively updating rapidly changing areas like SEO.
Methodology
The survey invitations were sent out via email, website, and social media throughout Q4 2025. Out of 207 respondents, findings are drawn from the 154 marketers (60%) who had replaced a martech application in the preceding 12 months.
I’ve noticed that Bing is testing a double-rowed sponsored product carousel in its shopping results. As someone who keeps an eye on these updates, this change could offer substantial visibility boosts for Microsoft Shopping advertisers.
The test, first spotted by Digital Marketer Sachin Patel, caught my attention when he noticed the broader layout while searching for cushions on Bing. This new format combines a significant double-rowed sponsored carousel, prominently paired with organic results below.
Why this matters to me: If Bing decides to roll out this format broadly, I foresee a significant increase in screen space dedicated to sponsored products. This extra visibility typically translates to higher click-through rates, especially for those running Microsoft Shopping campaigns. The visually appealing double-row carousel puts Bing’s shopping ads on par with similar offerings by Google Shopping.
Here’s the catch: The test seems to be in its early stages, as not all users, including seasoned industry experts like Mordy Oberstein, are seeing this expanded format. When I checked myself, I noticed a more compact layout, hinting at Bing’s ongoing experimentation.
The takeaway: Bing often experiments with its search engine results pages without officially rolling them out. As a retailer using Microsoft Shopping, it’s crucial for me to stay alert for any increase in product impressions if the format becomes more widespread.
Initially discovered. This testing phase was initially spotted by Sachin Paten, who shared his insights and a screenshot on X.
