I recently discovered that HubSpot has decided to shake things up by rebranding their annual conference, taking it from ‘Inbound’ to the innovative ‘Unbound’. This change is certainly a nod to the evolving landscape of marketing and strategy.
If you’ve tucked away your inbound strategy tools over the past year, maybe it’s time to do the same with those ‘Inbound’ conference mugs and swag as well. It’s a fresh start.
This coming September, HubSpot’s annual gathering in Boston will reflect this transition. As noted on their event site, the reasoning behind this shift is clear:
“This evolution is our response to that reality. INBOUND is becoming UNBOUND because growth no longer fits within a single framework or function. Today, it covers marketing, sales, service, and operations across the full customer journey in an AI-driven environment. UNBOUND reflects that expanded reality and the mindset required to lead through it.”
It’s fascinating to consider how HubSpot, the pioneers of inbound marketing, are now expanding beyond what they once set in motion—using content and search rankings for attracting and converting visitors.
I’ve also noted that recent changes in Google’s algorithm seem to have affected the HubSpot blog, possibly as a result of content drifting away from core topics like CRM, sales, and marketing.
It’s clear that the traditional inbound strategy has lessened in impact as platforms like Google shift towards AI models such as ChatGPT, affecting website traffic and clicks.
Back in 2025, HubSpot introduced their Loop marketing strategy, aiming to educate consumers in this rapidly advancing AI world.
The move to ‘Unbound’ acknowledges that no singular approach is sufficient in today’s dynamic marketing environment. It’s a brave new shift, one that reflects a deeper understanding of the expansive realities we’re working within.
Recently, I read an eye-opening report stating that AI bot activity skyrocketed by 300% in 2025. As someone deeply interested in digital publishing, I couldn’t help but feel the strain it puts on media and publishing industries.
Why this matters to me. I’m increasingly aware of how AI bots are revolutionizing content discovery and consumption. They’ve shifted the dynamics by directing users from traditional search clicks to direct answers via chat interfaces. For publishers like us, this means fewer organic visits and a lack of attribution in AI-generated responses, which undermines revenue from ads and subscriptions.
The threat we face. In our publishing niche, we’re confronted with two significant AI bot threats:
– Training bots that are fed our content models.
– Fetcher bots that extract our real-time content to provide instant answers, posing a severe risk by capturing the value as soon as it’s created.
The impact I notice. It’s disheartening to see page views sink while operational costs escalate. Scraping bots consume our server and CDN resources without adding revenue, decreasing brand visibility.
– AI chatbot referrals result in about 96% less traffic compared to traditional search.
– Only about 1% of users click on sources cited in AI-generated answers.
Our solutions. As a proactive step, I see publishers like us leaning toward nuanced controls instead of outright banning AI bots. We adapt by:
– Monitoring and categorizing bot traffic efficiently.
– Selectively blocking malicious scrapers or slowing them down using techniques like tarpitting.
– Authorizing bots that are linked to licensing deals or partnerships.
In their words. As per Akamai’s insights:
– “These bots are more than just a security issue; they pose a profound business challenge that threatens the sustainability of quality journalism in a zero-click search and AI-generated content era.”
– “Publishing faces an existential crisis… Readers still appreciate genuine content, but they seek instant answers via AI-driven platforms like ChatGPT and Gemini rather than search results.”
What’s ahead? There’s talk about a “pay-per-crawl” model. Tools such as identity verification (Know Your Agent) and platforms like TollBit are aiming to authenticate bots and charge for real-time access.
– The aim is to convert scraping into a manageable and monetizable transaction.
About the data. The Akamai report scrutinized bot management data from July to December 2025, which included application-layer traffic across websites, apps, and APIs.
When I think about auditing an agency to find a genuine growth partner, I am often reminded of how many agencies sound the same at first glance. Yet, when we dig deeper, the real differences can be stark, particularly in their methods of optimization, measurement, and scaling.
As a seasoned performance marketing head at an agency, I frequently encounter agencies offering account audits during their sales pitch. Their goal is usually twofold: to deliver immediate value and to showcase their expertise.
But, in my experience, brand marketers seldom reverse roles to audit these agencies during the Request for Proposal (RFP) process. Over the years, I’ve noticed many brands settling for mediocrity simply because they aren’t equipped with the right questions to unearth the weaknesses in a potential partner’s strategy.
If I were a brand, eager to secure a true growth partner, these are the questions I’d make sure to ask.
1. What are your key services, and what percentage of your clients utilize each? I’ve seen many agencies claim they offer ‘full service,’ but true execution excellence is rare. I’d scrutinize where they truly focus their time and efforts. This not only includes channel proficiency but how their strengths align with our brand’s needs.
2. How are you approaching AI-driven account optimization and platform automation? Gone are the days when manual controls set us apart as high-performing marketers. Understanding how an agency balances AI automation without over-reliance is crucial.
3. What is your reporting process, and what KPIs do you focus on for the majority of your clients? A mere sample report won’t do. I need to comprehend their data philosophy, especially if it centers around revenue and ROAS metrics.
4. What’s the average industry tenure of the team on my account? A common query, yet crucial for understanding their ability to retain experienced professionals who leverage AI tools adeptly.
5. How is your team using AI on client accounts? Striking a balance in AI usage is essential. I prefer teams that use AI wisely for operational efficiency without sacrificing strategic insights and creativity.
6. When you take over an account, what are the first things you do to save budget without affecting growth? This is a litmus test of their technical proficiency, focusing on identifying and eliminating budget waste efficiently.
Ultimately, to distinguish a true growth partner from others, I focus on their service utilization rates, tactical AI applications, and budget efficiency approaches. These considerations help identify a partner ready to deliver genuine performance rather than just manage our budget.
I’ve been intrigued by how Google Search is set to transform. Sundar Pichai, the CEO of Alphabet, recently shared on the Cheeky Pint podcast that search is moving away from just providing information and answers. Instead, it’s evolving into a dynamic system that can complete tasks for us.
Why this matters to us: This shift marks Google’s transition from being a tool for information retrieval to becoming an assistant in task execution, which I’m sure will enhance our web interactions significantly.
Search’s agentic evolution: Sundar Pichai illustrates that our traditional way of searching is already seeing changes, and it’s only going to continue evolving.
He mentioned, “If I fast-forward, a lot of what are just information-seeking queries will be agentic in Search. You’ll be completing tasks. You’ll have many threads running.”
Pichai envisions a future where Google Search serves more as an agent manager, coordinating various actions for us. It’s like having multiple agents accomplishing different tasks, allowing us to get so much more done efficiently.
The CEO notes, “Search would be an agent manager in which you’re doing a lot of things. I think in some ways, I use Antigravity today, and you have a bunch of agents doing stuff. I can see search doing versions of those things, and you’re getting a bunch of stuff done.”
AI Mode’s impact: Pichai highlights that users are adapting their search behavior with Google’s AI functionalities. Even now, people perform deep research queries that redefine traditional search activities, implying that we’re already on a path to using search for more complex, long-running tasks.
He explains, “But today in AI Mode in Search, people do deep research queries. That doesn’t quite fit the definition of what you’re saying. But people adapted to that. I think people will do long-running tasks.”
Search and Gemini coexistence: Despite the introduction of Gemini, Sundar assures us that Google Search isn’t going anywhere. Instead, both will coexist and evolve together, balancing between some areas of overlap and profound divergence. This dual strategy aims to enhance how we utilize these technologies daily.
“We are doing both Search and Gemini. They will overlap in certain ways. They will profoundly diverge in certain ways. I think it’s good to have both and embrace it,” he shared.
Have you ever wondered why AI often misunderstands your content? It all comes down to how AI systems label and score your content before ranking it. This process, known as annotation, determines how you’re perceived and whether you’ll succeed online.
Imagine my surprise when Google once attributed two of Barry Schwartz’s articles from Search Engine Land to me. This misclassification briefly altered authorship in Google’s systems, inaccurately listing me as the author.
For those few days, if you searched for specific articles written by Schwartz, Google misidentified me as the author, connecting these articles to my Knowledge Panel. This mishap highlights a critical aspect often overlooked in the SEO industry: annotation, not the content itself, is key to visibility and success.
How Google Misannotated and Got the Author Wrong
When Googlebot crawled those pages, it prominently noted my name below the article—my author bio appeared as the first recognized entity. The annotation algorithms then wrongly classified me as the author with high confidence.
This highlights the importance of annotation as a defining gate that influences everything downstream, from recruitment to ranking. Although this was simply an authorship error, imagine if it involved a product, price, or crucial attribute—that would severely impact your competitive standing.
Annotation serves as a vital gate in taking your brand from being discovered to winning, for whatever search intent or engine you’re optimizing for.
While indexing breaks your content into chunks and stores it, annotation labels these chunks with classifications based on confidence. It’s a pragmatic labeler, describing what the chunk contains, when it could be useful, and its trustworthiness.
Annotation remains largely impartial, tagging content without bias. Microsoft’s Fabrice Canel notes that filtering occurs later at query time, meaning annotation is neutral at the crawl stage, classifying without knowing its future retrieval context.
This insight transformed my approach to “crawl and index.” The real action happens with annotation: an indexed page with poor annotation is invisible to algorithms across search engines, language models, and knowledge graphs.
Annotation analyzes each chunk in the context of the whole page, using multiple language models, the web index, and a knowledge graph to determine context and confidence. Poor page-level understanding affects every chunk’s annotation.
Algorithmic systems use annotation to absorb content during recruitment, influenced by different criteria. A low-confidence or misclassified chunk results in a weaker competitive standing.
Annotation is a critical midpoint in the content pipeline, where strategy shifts from infrastructure to competition.
The Five Levels of Annotation
Annotation has five functional categories, each essential in the classification process. Here’s the taxonomy I’ve identified:
Level 1: Gatekeepers
Temporal scope, geographic scope, language, and entity resolution, determining pass or fail.
Failures here instantly remove content from competitiveness.
Level 2: Core Identity
Entities, attributes, relationships, and sentiment are defined.
Without a strong identity, chunks lack significance.
Level 3: Selection Filters
Intent, expertise, claim structure, and actionability determine competition pools.
Mismatched pools mean competing against better-suited content.
Level 4: Confidence Multipliers
Factors like verifiability and corroboration scale rankings.
Confidence impacts all other signals profoundly.
Level 5: Extraction Quality
Determines content’s sufficiency and context need.
Impacts how content appears in outputs.
Annotation Is Where the Game is Won
Annotation scores in each level reflect confidence in various aspects of content. Misclassified or low-confidence annotations can doom content before it truly competes.
Annotation fundamentally shapes the understanding algorithms have of your content, making it a crucial aspect of content strategy.
How to Optimize for Annotation Quality
The key to success is optimizing for annotation, not just indexing. Follow these principles:
Ensure category clarity early in content.
Write for subject, entity, and concept clarity.
Get annotation right on initial publish.
Invest in a solid entity foundation.
Eliminate contradictory signals promptly.
Audit for annotation accuracy.
Why Annotation Matters
Annotation is your last solo run before entering the competitive fray. Once classified correctly, you’re better positioned to win at recruitment and beyond. Fix it here, or face persistent issues downstream.
I’ve been exploring some fantastic new features on Google Maps, and I’m excited to share how they’ve transformed my experience. With recent updates, sharing photos, reviews, and local insights has become more intuitive, thanks to the introduction of AI-generated captions powered by Gemini.
Local Guides Redesign. If you’re like me, who enjoys contributing to Google Maps, you’ll appreciate the revamped Local Guides profiles. Now, our total points and levels are prominently displayed, and the badges have received a fresh new look!
Top contributors like us can enjoy greater visibility in reviews, thanks to new gold profile indicators that help us stand out.
AI Caption Drafts. Another noteworthy addition is the AI-generated caption drafts. Gemini is there to assist us by analyzing selected images and suggesting text we can either edit or discard, offering a smoother captioning experience.
Currently, these caption suggestions are available in English on iOS in the U.S., with plans for broader availability on Android and globally.
Media Sharing. Sharing photos and videos has never been easier. Recent uploads are now showcased directly in the Contribute tab, speeding up the sharing process.
By allowing media access, Google Maps helps us by suggesting images from our camera roll that are ready for sharing with just a tap. This feature is live on iOS and Android across the globe.
Why We Care. These updates not only enhance content creation but also potentially boost our local content visibility and search rankings. This could influence which reviews we trust and which businesses receive more attention.
In my extensive three-decade career, I’ve witnessed keywords dominate the landscape of paid search. However, in today’s world, they have become just a part of a larger puzzle. What truly drives performance now is strategy.
I remember spending weeks meticulously researching keywords, crafting strategies around them, and managing every aspect, from bid adjustments to audience targeting. It was the foundation of success in this industry.
We used to focus heavily on precise placements, structured URLs, and audience targeting, primarily with Google’s influence leading the charge. Our profession thrived on the tactical control this model offered.
We enjoyed the ability to identify which queries triggered ads and make informed decisions to optimize budgets accordingly. Sometimes we would even segment ad groups intricately to maximize returns.
What Changed Across Platforms
Now, advertising has embraced a significant shift: automation, driven by AI, has taken over critical tasks like bidding and creative assembly. While keywords remain relevant, they serve as just one of many signals that AI systems use.
With tools like AI Max for Search, Google has transformed keywords from being the focal point to just signals in guiding ad delivery. It’s fascinating how AI now uses elements like existing keywords and landing page content to enhance performance.
Advertisers employing AI Max often experience notable gains, with some campaigns seeing up to 27% more conversions. Integrating it with other tools like Performance Max can further amplify reach across various platforms.
When I mention strategy as the new keyword, I mean focusing on specific inputs shaping ad performance. These include conversion data quality, a critical factor for systems like Google’s Smart Bidding, which relies on quality data to optimize campaigns.
We now prioritize which conversions hold the most value. It’s a shift from purely manual adjustments to strategic evaluations that highlight what truly matters for campaign success.
First-party data, enriched and well-structured, is paramount. It’s akin to the foundational keyword research of the past, vital for driving performance on today’s platforms.
Creative assets have evolved beyond mere deliverables; they’re now strategic signals that AI uses to target effectively. These visuals and messages have become an integral part of how we engage audiences.
The quality of landing pages and websites has also taken on new importance. AI determines relevance based on post-click experiences, emphasizing the need for seamless user journeys.
Our roles have adapted to these changes. It’s less about managing keywords or bids manually and more about creating strategic frameworks that guide AI systems effectively.
Subject-matter experts like us now focus on ensuring data quality, defining creative strategies, and identifying when human intervention is necessary.
We guide AI through a careful mix of conversion architecture, audience signal quality, and creative frameworks rather than traditional methods of keyword lists and bidding.
It’s crucial to understand how these advanced systems and platforms operate, as well as to emphasize the signals that matter most. Building strong first-party data and strategic frameworks will enhance AI capabilities and redefine the future.
Embracing this evolution, practitioners focusing on strategy over technical execution positions will find themselves best equipped to thrive in this changing landscape.
The keyword list remains, but our primary focus now is on strategy.
I remember the days when a Google search was akin to embarking on a quest for information. It was an adventure of navigating various links and forming my own opinions.
Nowadays, tools like AI Overviews, ChatGPT, and Perplexity condense all that information into a single, simplified answer. This transformation often strips away the finer details while amplifying certain perspectives.
This shift has redefined online reputation management. Now, search engines not only present information but shape the underlying narratives. This raises the stakes for brands, as even a top-ranking status doesn’t guarantee influence if AI stories tell a different tale.
For brands, the game has changed. Being number one doesn’t ensure visibility and influence anymore. The underlying narrative holds far greater power.
AI Narrative Formation: Crafting User Answers
AI platforms now utilize what I like to call ‘AI narrative formation.’ This process crafts the responses we receive from various search engines. Let me walk you through how this system works.
Source Pooling
These systems pull content from numerous sources. Contrary to expected reliance on peer-reviewed articles, they gather data from Reddit, YouTube, and social platforms like Instagram and TikTok.
Signal Weighting
Not all sources are equal. Often, a popular yet low-quality source can outweigh a singular, credible entry. A bustling Reddit thread with negative feedback might overshadow a well-researched Wikipedia page.
Narrative Compression
The summarization process compresses diverse inputs, often losing nuance along the way. Complex reputations are simplified into general statements like, ‘Users find this company untrustworthy.’
Continued Reinforcement
These summaries transcend their original context, getting shared and re-shared across social media. As these echoes return as new data, they further entrench the narratives in AI responses.
Unraveling a Finance Company’s Reputation in AI Search
To illustrate AI narrative formation, consider a recent case I worked on involving a financial company, which we’ll call Company X.
Company X’s reputation remained strong on traditional SERPs. High Trustpilot ratings and reputable endorsements were the norm until Google AI Overview threads surfaced a forgotten Reddit forum rife with grievances against them.
The AI Overview skewed the narrative, suggesting Company X had unresolved customer service issues, even though these concerns had been addressed years prior. This created a skewed perception that was hard to counteract.
The Amplified Risk from AI Searches
AI dramatically increases reputational risk through several mechanisms:
The Spread of Negative Narratives: Negative content surfaces faster and more prominently than before.
AI Hallucinations: Despite growing awareness, AI inaccuracies continue to deceive.
The Snowball Effect: Repeated narratives gain momentum, complicating reputation management efforts.
It has become evident that in ORM, repetition often overrides accuracy.
Auditing AI-Generated Narratives: A Step-by-Step Approach
Let’s consider a situation involving an AI-generated narrative challenge faced by CEO X of a well-known SaaS company.
After an out-of-context quote from CEO X’s podcast appearance went viral, AI summarized him unfavorably. Quickly, his reputation transformed negatively across major platforms.
Step 1: Mapping Queries
I initiated a process to understand what queries AI outputs were generating about CEO X. This helped identify the underlying issues.
Step 2: Capturing Outputs
Identifying repeated claims revealed how CEO X was perceived. Narratives from Google AI and ChatGPT were consistently portraying him negatively.
Step 3: Delving Through Sources
The next step involved examining the quality of sources contributing to these narratives, often outdated or lacking accuracy.
Step 4: Analyzing the Narrative Gap
This involved assessing discrepancies between AI narratives and his actual reputation, contextualizing the initial quote, and examining the long-standing perception of CEO X.
Step 5: Correcting and Replacing Sources
Finally, I focused on directly addressing, correcting, and replacing those negative narratives. This involved engaging directly with platforms that contributed to the misinformation and reinforcing positive content elsewhere.
A New Perspective: From SEO to Narrative Management
The focus has shifted from merely achieving top SEO rankings to understanding and adapting to narrative shifts. We must rethink our strategy from content engagement to managing the narratives AI disseminates.
To succeed, it’s important to reinforce AI systems with quality inputs, including crafting high-quality content, pursuing credible mentions, disseminating structured data, and managing misinformation directly.
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.
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.
