Sep 8 2025
Google Publishes Exact Gemini Usage Limits Across All Tiers via @sejournal, @MattGSouthern

Google has published exact usage limits for Gemini Apps across the free tier and paid Google AI plans, replacing earlier vague language with concrete numbers marketers can plan around.

The Help Center update covers daily caps for prompts, images, Deep Research, video generation, and context windows, and notes that you’ll see in-product notices when you’re close to a limit.

What’s New

Until recently, Google’s documentation used general phrasing about “limited access” without specifying amounts.

The Help Center page now lists per-tier allowances for Gemini 2.5 Pro prompts, image generation, Deep Research, and more. It also clarifies that practical caps can vary with prompt complexity, file sizes, and conversation length, and that limits may change over time.

Google’s Help Center states:

“Gemini Apps has usage limits designed to ensure an optimal experience for everyone… we may at times have to cap the number of prompts, conversations, and generated assets that you can have within a specific timeframe.”

Free vs. Paid Tiers

On the free experience, you can use Gemini 2.5 Pro for up to five prompts per day.

The page lists general access to 2.5 Flash and includes:

  • 100 images per day
  • 20 Audio Overviews per day
  • Five Deep Research reports per month using 2.5 Flash).

Because overall app limits still apply, actual throughput depends on how long and complex your prompts are and how many files you attach.

Google AI Pro increases ceilings to:

  • 100 prompts per day on Gemini 2.5 Pro
  • 1,000 images per day
  • 20 Deep Research reports per day (using 2.5 Pro).

Google AI Ultra raises those to

  • 500 prompts per day
  • 200 Deep Research reports per day
  • Includes Deep Think with 10 prompts per day at a 192,000-token context window for more complex reasoning tasks.

Context Windows and Advanced Features

Context windows differ by tier. The free tier lists a 32,000-token context size, while Pro and Ultra show 1 million tokens, which is helpful when you need longer conversations or to process large documents in one go.

Ultra’s Deep Think is separate from the 1M context and is capped at 192k tokens for its 10 daily prompts.

Video generation is currently in preview with model-specific limits. Pro shows up to three videos per day with Veo 3 Fast (preview), while Ultra lists up to five videos per day with Veo 3 (preview).

Google indicates some features receive priority or early access on paid plans.

Availability and Requirements

The Gemini app in Google AI Pro and Ultra is available in 150+ countries and territories for users 18 or older.

Upgrades are tied to select Google One paid plans for personal accounts, which consolidate billing with other premium Google services.

Why This Matters

Clear ceilings make it easier to scope deliverables and budgets.

If you produce a steady stream of social or ad creative, the image caps and prompt totals are practical planning inputs.

Teams doing competitive analysis or longer-form research can evaluate whether the free tier’s five Deep Research reports per month cover occasional needs or if Pro’s daily allotment, Ultra’s higher limit, and Deep Think are a better fit for heavier workloads.

The documentation also emphasizes that caps can vary with usage patterns, so it’s worth watching the in-app limit warnings on busy days.

Looking Ahead

Google notes that limits may evolve. If your workflows depend on specific daily counts or large context windows, it’s sensible to review the Help Center page periodically and adjust plans as features move from preview to general availability.

Featured Image: Evolf/Shutterstock

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Sep 4 2025
AI Search Sends Users to 404 Pages Nearly 3X More Than Google via @sejournal, @MattGSouthern

New research examining 16 million URLs aligns with Google’s predictions that hallucinated links will become an issue across AI platforms.

An Ahrefs study shows that AI assistants send users to broken web pages nearly three times more often than Google Search.

The data arrives six months after Google’s John Mueller raised awareness about this issue.

ChatGPT Leads In URL Hallucination Rates

ChatGPT creates the most fake URLs among all AI assistants tested. The study found that 1% of URLs people clicked led to 404 pages. Google’s rate is just 0.15%.

The problem gets worse when looking at all URLs ChatGPT mentions, not just clicked ones. Here, 2.38% lead to error pages. Compare this to Google’s top search results, where only 0.84% are broken links.

Claude came in second with 0.58% broken links for clicked URLs. Copilot had 0.34%, Perplexity 0.31%, and Gemini 0.21%. Mistral had the best rate at 0.12%, but it also sends the least traffic to websites.

Why Does This Happen?

The research found two main reasons why AI creates fake links.

First, some URLs used to exist but don’t anymore. When AI relies on old information instead of searching the web in real-time, it might suggest pages that have been deleted or moved.

Second, AI sometimes invents URLs that sound right but never existed.

Ryan Law from Ahrefs shared examples from their own site. AI assistants created fake URLs like “/blog/internal-links/” and “/blog/newsletter/” because these sound like pages Ahrefs might have. But they don’t actually exist.

Limited Impact on Overall Traffic

The problem may seem significant, but most websites won’t notice much impact. AI assistants only bring in about 0.25% of website traffic. Google, by comparison, drives 39.35% of traffic.

This means fake URLs affect a tiny portion of an already small traffic source. Still, the issue might grow as more people use AI for research and information.

The study also found that 74% of new web pages contain AI-generated content. When this content includes fake links, web crawlers might index them, spreading the problem further.

Mueller’s Prediction Proves Accurate

These findings match what Google’s John Mueller predicted in March. He forecasted a “slight uptick of these hallucinated links being clicked” over the next 6-12 months.

Mueller suggested focusing on better 404 pages rather than chasing accidental traffic.

His advice to collect data before making big changes looks smart now, given the small traffic impact Ahrefs found.

Mueller also predicted the problem would fade as AI services improve how they handle URLs. Time will tell if he’s right about this, too.

Looking Forward

For now, most websites should focus on two things. Create helpful 404 pages for users who hit broken links. Then, set up redirects only for fake URLs that get meaningful traffic.

This allows you to handle the problem without overreacting to what remains a minor issue for most sites.

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Sep 4 2025
Let’s Look Inside An Answer Engine And See How GenAI Picks Winners via @sejournal, @DuaneForrester

Ask a question in ChatGPT, Perplexity, Gemini, or Copilot, and the answer appears in seconds. It feels effortless. But under the hood, there’s no magic. There’s a fight happening.

This is the part of the pipeline where your content is in a knife fight with every other candidate. Every passage in the index wants to be the one the model selects.

For SEOs, this is a new battleground. Traditional SEO was about ranking on a page of results. Now, the contest happens inside an answer selection system. And if you want visibility, you need to understand how that system works.

Let's Look Inside An Answer Engine and See How GenAI Picks WinnersImage Credit: Duane Forrester

The Answer Selection Stage

This isn’t crawling, indexing, or embedding in a vector database. That part is done before the query ever happens. Answer selection kicks in after a user asks a question. The system already has content chunked, embedded, and stored. What it needs to do is find candidate passages, score them, and decide which ones to pass into the model for generation.

Every modern AI search pipeline uses the same three stages (across four steps): retrieval, re-ranking, and clarity checks. Each stage matters. Each carries weight. And while every platform has its own recipe (the weighting assigned at each step/stage), the research gives us enough visibility to sketch a realistic starting point. To basically build our own model to at least partially replicate what’s going on.

The Builder’s Baseline

If you were building your own LLM-based search system, you’d have to tell it how much each stage counts. That means assigning normalized weights that sum to one.

A defensible, research-informed starting stack might look like this:

  • Lexical retrieval (keywords, BM25): 0.4.
  • Semantic retrieval (embeddings, meaning): 0.4.
  • Re-ranking (cross-encoder scoring): 0.15.
  • Clarity and structural boosts: 0.05.

Every major AI system has its own proprietary blend, but they’re all essentially brewing from the same core ingredients. What I’m showing you here is the average starting point for an enterprise search system, not exactly what ChatGPT, Perplexity, Claude, Copilot, or Gemini operate with. We’ll never know those weights.

Hybrid defaults across the industry back this up. Weaviate’s hybrid search alpha parameter defaults to 0.5, an equal balance between keyword matching and embeddings. Pinecone teaches the same default in its hybrid overview.

Re-ranking gets 0.15 because it only applies to the short list. Yet its impact is proven: “Passage Re-Ranking with BERT” showed major accuracy gains when BERT was layered on BM25 retrieval.

Clarity gets 0.05. It’s small, but real. A passage that leads with the answer, is dense with facts, and can be lifted whole, is more likely to win. That matches the findings from my own piece on semantic overlap vs. density.

At first glance, this might sound like “just SEO with different math.” It isn’t. Traditional SEO has always been guesswork inside a black box. We never really had access to the algorithms in a format that was close to their production versions. With LLM systems, we finally have something search never really gave us: access to all the research they’re built on. The dense retrieval papers, the hybrid fusion methods, the re-ranking models, they’re all public. That doesn’t mean we know exactly how ChatGPT or Gemini dials their knobs, or tunes their weights, but it does mean we can sketch a model of how they likely work much more easily.

From Weights To Visibility

So, what does this mean if you’re not building the machine but competing inside it?

Overlap gets you into the room, density makes you credible, lexical keeps you from being filtered out, and clarity makes you the winner.

That’s the logic of the answer selection stack.

Lexical retrieval is still 40% of the fight. If your content doesn’t contain the words people actually use, you don’t even enter the pool.

Semantic retrieval is another 40%. This is where embeddings capture meaning. A paragraph that ties related concepts together maps better than one that is thin and isolated. This is how your content gets picked up when users phrase queries in ways you didn’t anticipate.

Re-ranking is 15%. It’s where clarity and structure matter most. Passages that look like direct answers rise. Passages that bury the conclusion drop.

Clarity and structure are the tie-breaker. 5% might not sound like much, but in close fights, it decides who wins.

Two Examples

Zapier’s Help Content

Zapier’s documentation is famously clean and answer-first. A query like “How to connect Google Sheets to Slack” returns a ChatGPT answer that begins with the exact steps outlined because the content from Zapier provides the exact data needed. When you click through a ChatGPT resource link, the page you land on is not a blog post; it’s probably not even a help article. It’s the actual page that lets you accomplish the task you asked for.

  • Lexical? Strong. The words “Google Sheets” and “Slack” are right there.
  • Semantic? Strong. The passage clusters related terms like “integration,” “workflow,” and “trigger.”
  • Re-ranking? Strong. The steps lead with the answer.
  • Clarity? Very strong. Scannable, answer-first formatting.

In a 0.4 / 0.4 / 0.15 / 0.05 system, Zapier’s chunk scores across all dials. This is why their content often shows up in AI answers.

A Marketing Blog Post

Contrast that with a typical long marketing blog post about “team productivity hacks.” The post mentions Slack, Google Sheets, and integrations, but only after 700 words of story.

  • Lexical? Present, but buried.
  • Semantic? Decent, but scattered.
  • Re-ranking? Weak. The answer to “How do I connect Sheets to Slack?” is hidden in a paragraph halfway down.
  • Clarity? Weak. No liftable answer-first chunk.

Even though the content technically covers the topic, it struggles in this weighting model. The Zapier passage wins because it aligns with how the answer selection layer actually works.

Traditional search still guides the user to read, evaluate, and decide if the page they land on answers their need. AI answers are different. They don’t ask you to parse results. They map your intent directly to the task or answer and move you straight into “get it done” mode. You ask, “How to connect Google Sheets to Slack,” and you end up with a list of steps or a link to the page where the work is completed. You don’t really get a blog post explaining how someone did this during their lunch break, and it only took five minutes.

Volatility Across Platforms

There’s another major difference from traditional SEO. Search engines, despite algorithm changes, converged over time. Ask Google and Bing the same question, and you’ll often see similar results.

LLM platforms don’t converge, or at least, aren’t so far. Ask the same question in Perplexity, Gemini, and ChatGPT, and you’ll often get three different answers. That volatility reflects how each system weights its dials. Gemini may emphasize citations. Perplexity may reward breadth of retrieval. ChatGPT may compress aggressively for conversational style. And we have data that shows that between a traditional engine, and an LLM-powered answer platform, there is a wide gulf between answers. Brightedge’s data (62% disagreement on brand recommendations) and ProFound’s data (…AI modules and answer engines differ dramatically from search engines, with just 8 – 12% overlap in results) showcase this clearly.

For SEOs, this means optimization isn’t one-size-fits-all anymore. Your content might perform well in one system and poorly in another. That fragmentation is new, and you’ll need to find ways to address it as consumer behavior around using these platforms for answers shifts.

Why This Matters

In the old model, hundreds of ranking factors blurred together into a consensus “best effort.” In the new model, it’s like you’re dealing with four big dials, and every platform tunes them differently. In fairness, the complexity behind those dials is still pretty vast.

Ignore lexical overlap, and you lose part of that 40% of the vote. Write semantically thin content, and you can lose another 40. Ramble or bury your answer, and you won’t win re-ranking. Pad with fluff and you miss the clarity boost.

The knife fight doesn’t happen on a SERP anymore. It happens inside the answer selection pipeline. And it’s highly unlikely those dials are static. You can bet they move in relation to many other factors, including each other’s relative positioning.

The Next Layer: Verification

Today, answer selection is the last gate before generation. But the next stage is already in view: verification.

Research shows how models can critique themselves and raise factuality. Self-RAG demonstrates retrieval, generation, and critique loops. SelfCheckGPT runs consistency checks across multiple generations. OpenAI is reported to be building a Universal Verifier for GPT-5. And, I wrote about this whole topic in a recent Substack article.

When verification layers mature, retrievability will only get you into the room. Verification will decide if you stay there.

Closing

This really isn’t regular SEO in disguise. It’s a shift. We can now more clearly see the gears turning because more of the research is public. We also see volatility because each platform spins those gears differently.

For SEOs, I think the takeaway is clear. Keep lexical overlap strong. Build semantic density into clusters. Lead with the answer. Make passages concise and liftable. And I do understand how much that sounds like traditional SEO guidance. I also understand how the platforms using the information differ so much from regular search engines. Those differences matter.

This is how you survive the knife fight inside AI. And soon, how you pass the verifier’s test once you’re there.

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: tete_escape/Shutterstock

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Aug 28 2025
Research Shows How To Optimize For Google AIO And ChatGPT via @sejournal, @martinibuster

New research from BrightEdge shows that Google AI Overviews, AI Mode, and ChatGPT recommend different brands nearly 62% of the time. BrightEdge concludes that each AI search platform is interpreting the data in different ways, suggesting different ways of thinking about each AI platform.

Methodology And Results

BrightEdge’s analysis was conducted with its AI Catalyst tool, using tens of thousands of the same queries across ChatGPT, Google AI Overviews (AIO), and Google AI Mode. The research documented a 61.9% overall disagreement rate, with only 33.5% of queries showing the exact same brands in all three AI platforms.

Google AI Overviews averaged 6.02 brand mentions per query, compared to ChatGPT’s 2.37. Commercial intent search queries containing phrases like “buy,” “where,” or “deals” generated brand mentions 65% of the time across all platforms, suggesting that these kinds of high-intent keyword phrases continue to be reliable for ecommerce, just like in traditional search engines. Understandably, e-commerce and finance verticals achieved 40% or more brand-mention coverage across all three AI platforms.

Three Platforms Diverge

Not all was agreement between the three AI platforms in the study. Many identical queries led to very different brand recommendations depending on the AI platform.

BrightEdge shares that:

  • ChatGPT cites trusted brands even when it’s not grounding on search data, indicating that it’s relying on LLM training data.
  • Google AI Overviews cites brands 2.5 times more than ChatGPT.
  • Google AI Mode cites brands less often than both ChatGPT and AIO.

The research indicates that ChatGPT favors trusted brands, Google AIO emphasizes breadth of coverage with more brand mentions per query, and Google AI Mode selectively recommends brands.

Next we untangle why these patterns exist.

Differences Exist

BrightEdge asserts that this split across the three platforms is not random. I agree that there are differences, but I disagree that “authority” has anything to do with it and offer an alternate explanation later on.

These are the conclusions that they draw from the data:

  • “The Brand Authority Play:
    ChatGPT’s reliance on training data means established brands with strong historical presence can capture mentions without needing fresh citations. This creates an “authority dividend” that many brands don’t realize they’re already earning—or could be earning with the right positioning.
  • The Volume Opportunity:
    Google AI Overview’s hunger for brand mentions means there are 6+ available slots per relevant query, with clear citation paths showing exactly how to earn visibility. While competitors focus on traditional SEO, innovative brands are reverse-engineering these citation networks.
  • The Quality Threshold:
    Google AI Mode’s selectivity means fewer brands make the cut, but those that do benefit from heavy citation backing that reinforces their authority across the web.”

Not Authority – It’s About Training Data

BrightEdge refers to “authority signals” within ChatGPT’s underlying LLM. My opinion differs in regard to an LLM’s generated output, not retrieval-augmented responses that pull in live citations. I don’t think there are any signals in the sense of ranking-related signals. In my opinion, the LLM is simply reaching for the entity (brand) related to a topic.

What looks like “authority” to someone with their SEO glasses on is more likely about frequency, prominence, and contextual embedding strength.

  • Frequency:
    How often the brand appears in the training data.
  • Prominence:
    How central the brand is in those contexts (headline vs. footnote).
  • Contextual Embedding Strength:
    How tightly the brand is associated with certain topics based on the model’s training data.

If a brand appears widely in appropriate contexts within the training data, then, in my opinion, it is more likely to be generated as a brand mention by the LLM, because this reflects patterns in the training data and not authority.

That said, I agree with BrightEdge that being authoritative is important, and that quality shouldn’t be minimized.

Patterns Emerge

The research data suggests that there are unique patterns across all three platforms that can behave as brand citation triggers. One pattern all three share is that keyword phrases with a high commercial intent generate brand mentions in nearly two-thirds of cases. Industries like e-commerce and finance achieve higher brand coverage, which, in my opinion, reflects the ability of all three platforms to accurately understand the strong commercial intents for keywords inherent to those two verticals.

A little sunshine in a partly cloudy publishing environment is the finding that comparison queries for “best” products generate 43% brand citations across all three AI platforms, again reflecting the ability of those platforms to understand user query contexts.

Citation Network Effect

BrightEdge has an interesting insight about creating presence in all three platforms that it calls a citation network effect. BrightEdge asserts that earning citations in one platform could influence visibility in the others.

They share:

“A well-crafted piece… could:
Earn authority mentions on ChatGPT through brand recognition

Generate 6+ competitive mentions on Google AI Overview through comprehensive coverage

Secure selective, heavily-cited placement on Google AI Mode through third-party validation

The citation network effect means that earning mentions on one platform often creates the validation needed for another. “

Optimizing For Traditional Search Remains

Nevertheless, I agree with BrightEdge that there’s a strategic opportunity in creating content that works across all three environments, and I would make it explicit that SEO, optimizing for traditional search, is the keystone upon which the entire strategy is crafted.

Traditional SEO is still the way to build visibility in AI search. BrightEdge’s data indicates that this is directly effective for AIO and has a more indirect effect for AI Mode and ChatGPT.

ChatGPT can cite brand names directly from training data and from live data. It also cites brands directly from the LLM, which suggests that generating strong brand visibility tied to specific products and services may be helpful, as that is what eventually makes it into the AI training data.

BrightEdge’s conclusion about the data leans heavily into the idea that AI is creating opportunities for businesses that build brand awareness in the topics they want to be surfaced in.
They share:

“We’re witnessing the emergence of AI-native brand discovery. With this fundamental shift, brand visibility is determined not by search rankings but by AI recommendation algorithms with distinct personalities and preferences.

The brands winning this transition aren’t necessarily the ones with the biggest SEO budgets or the most content. They’re the ones recognizing that AI disagreement creates more paths to visibility, not fewer.

As AI becomes the primary discovery mechanism across industries, understanding these platform-specific triggers isn’t optional—it’s the difference between capturing comprehensive brand visibility and watching competitors claim the opportunities you didn’t know existed.

The 62% disagreement gap isn’t breaking the system. It’s creating one—and smart brands are already learning to work it.”

BrightEdge’s report:

ChatGPT vs Google AI: 62% Brand Recommendation Disagreement

Featured Image by Shutterstock/MMD Creative

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Aug 27 2025
Perplexity’s Discover Pages Offer A Surprising SEO Insight via @sejournal, @martinibuster

A post on LinkedIn called attention to Perplexity’s content discovery feed called Discover, which generates content on trending news topics. It praised the feed as a positive example of programmatic SEO, although some said that its days in Google’s search results are numbered. Everyone in that discussion believes those pages are one thing. In fact, they are something else entirely.

Context: Perplexity Discover

Perplexity publishes a Discover feed of trending topics. The page is like a portal to the news of the day, featuring short summaries and links to web pages containing the full summary plus links to the original news reporting.

SEOs have noticed that some of those pages are ranking in Google Search, spurring a viral discussion on LinkedIn.

Perplexity Discover And Programmatic SEO

Programmatic SEO is the use of automation to optimize web content and could also apply to scaled content creation. It can be tricky to pull off well and can result in a poor outcome if not.

A LinkedIn post calling attention to the Perplexity AI-generated Discover feed cited it as an example of programmatic SEO “on steroids.”

They wrote:

“For every trending news topic, it automatically creates a public webpage.

These pages are now showing up in Google Search results.

When clicked, users land on a summary + can ask follow-up questions in the chatbot.

…This is such a good Programmatic SEO tactic put on steroids!”

One of the comments in that discussion hailed the Perplexity pages as an example of good programmatic SEO:

“This is a very bold move by Perplexity. Programmatic SEO at scale, backed by trending topics, is a smart way to capture attention and traffic. The key challenge will be sustainability – Google may see this as thin content or adjust algorithms against it. Still, it shows how AI + SEO is evolving faster than expected.”

Another person agreed:

“SEO has been part of their growth strategy since last year, and it works for them quite well”

The rest of the comments praised Perplexity’s SEO as “bold” and “clever” as well as providing “genuine user value.”

But there were also some that predicted that “Google won’t allow this trend…” and that “Google will nerf it in a few weeks…”

The overall sentiment of Perplexity’s implementation of programmatic SEO was positive.

Except that there is no SEO.

 Perplexity Discover Is Not Programmatic SEO

Contrary to what was said in the LinkedIn discussion, Perplexity is not engaging in “programmatic SEO,” nor are they trying to rank in Google.

A peek at the source code of any of the Discover pages shows that the title elements and the meta descriptions are not optimized to rank in search engines.

Screenshot Of A Perplexity Discover Web Page

Every single page created by Perplexity appears to have the exact same title and meta description elements:

Perplexity

Every page contains the same canonical tag:

https://www.perplexity.ai” />

It’s clear that Perplexity’s Discover pages are not optimized for Google Search and that the pages are not created for search engines.

The pages are created for humans.

Given how the Discover pages are not optimized, it’s not a surprise that:

  • Every page I tested failed to rank in Google Search.
  • It’s clear that Perplexity is engaged in programmatic SEO.
  • Perplexity’s Discover pages are not created to rank in Google Search.
  • Perplexity’s Discover pages are created specifically for humans.
  • If any pages rank in Google, that’s entirely an accident and not by design.

What Is Perplexity Actually Doing?

Perplexity’s Discover pages are examples of something bigger than SEO. They are web pages created for the benefit of users. The fact that no SEO is applied shows that Perplexity is focused on making the Discover pages destinations that users turn to in order to keep in touch with the events of the day.

Perplexity Discover is a user-first web destination created with zero SEO, likely because the goals are more ambitious than depending on Google for traffic.

The Surprising SEO Insight?

It may well be that a good starting point for creating a website and forming a strategy for promoting it lies outside the SEO sandbox. In my experience, I’ve had success creating and promoting outside the standard SEO framework, because SEO strategies are inherently limited: they have one goal, ranking, and miss out on activities that create popularity.

SEO limits how you can promote a site with arbitrary rules such as: 

  • Don’t obtain links from sites that nofollow their links.
  • Don’t get links from sites that have low popularity.
  • Offline promotion doesn’t help your site rank.

And here’s the thing: promoting a site with strategies focused on building brand name recognition with an audience tends to create the kinds of user behavior signals that we know Google is looking for.

Check out Perplexity’s Discover at perplexity.ai/discover.

Featured Image by Shutterstock/Cast Of Thousands

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Aug 26 2025
Perplexity Launches Comet Plus, Shares Revenue With Publishers via @sejournal, @MattGSouthern

Perplexity announced Comet Plus, a monthly subscription that pays participating publishers when people read their work and when AI systems use it to answer questions.

The company says subscriber payments go to partners, with a small portion retained to cover compute costs.

How Comet Plus Works

Comet Plus will be available for $5 per month. Existing Perplexity Pro and Max subscribers will have Comet Plus included.

Subscribers get direct access to participating publisher sites, answers informed by those sources, and agent workflows that can complete tasks on those sites. The offering is tied to the Comet browser and assistant.

About Revenue Sharing

Perplexity positions Comet Plus as a compensation model for an AI-centric web.

Publishers are paid for three interaction types:

  1. Human visits
  2. Search citations
  3. Agent actions.

Perplexity’s example of “agent traffic” is Comet Assistant scanning a calendar and suggesting relevant reading from publisher sites.

The idea is to reflect how people now consume information across browsing, AI answers, and agent workflows.

Perplexity wrote:

“Comet Plus is the first compensation model… based on three types of internet traffic: human visits, search citations, and agent actions.”

Availability

Interested publishers can email publishers@perplexity.ai to request to join the program.

Why It Matters

For publishers and marketers, the model expands monetization and measurement beyond traditional clicks.

Websites are testing a range of responses to AI usage of their content, from blocking crawlers to signing licenses.

Comet Plus differs from flat-fee deals by tying payouts to actual user and assistant activity, which could align compensation more closely with real demand.

Looking Ahead

Perplexity says it will announce an initial roster of publishing partners when the Comet browser becomes available to all users for free.

Early adoption, reporting transparency, and real revenue for partners will determine whether this model becomes a viable framework or stays a niche experiment.

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Aug 26 2025
Consumer Trust And Perception Of AI In Marketing

This edited excerpt is from Ethical AI in Marketing by Nicole Alexander ©2025 and is reproduced and adapted with permission from Kogan Page Ltd.

Recent research highlights intriguing paradoxes in consumer attitudes toward AI-driven marketing. Consumers encounter AI-powered marketing interactions frequently, often without realizing it.

According to a 2022 Pew Research Center survey, 27% of Americans reported interacting with AI at least several times a day, while another 28% said they interact with AI about once a day or several times a week (Pew Research Center, 2023).

As AI adoption continues to expand across industries, marketing applications – from personalized recommendations to chatbots – are increasingly shaping consumer experiences.

According to McKinsey & Company (2023), AI-powered personalization can deliver five to eight times the ROI on marketing spend and significantly boost customer engagement.

In this rapidly evolving landscape, trust in AI has become a crucial factor for successful adoption and long-term engagement.

The World Economic Forum under­scores that “trust is the foundation for AI’s widespread acceptance,” and emphasizes the necessity for companies to adopt self-governance frameworks that prioritize transparency, accountability, and fairness (World Economic Forum, 2025).

The Psychology Of AI Trust

Consumer trust in AI marketing systems operates fundamentally differently from traditional marketing trust mechanisms.

Where traditional marketing trust builds through brand familiarity and consistent experiences, AI trust involves additional psychological dimensions related to automation, decision-making autonomy, and perceived control.

Understanding these differences is crucial for organizations seek­ing to build and maintain consumer trust in their AI marketing initiatives.

Cognitive Dimensions

Neurological studies offer intriguing insights into how our brains react to AI. Research from Stanford University reveals that we process information differently when interacting with AI-powered systems.

For example, when evaluating AI-generated product recommendations, our brains activate distinct neural path­ways compared to those triggered by recommendations from a human salesperson.

This crucial difference highlights the need for marketers to understand how consum­ers cognitively process AI-driven interactions.

There are three key cognitive factors that have emerged as critical influences on AI trust, including perceived control, understanding of mechanisms, and value recognition.

Emotional Dimensions

Consumer trust in AI marketing is deeply influenced by emotional factors, which often override logical evaluations. These emotional responses shape trust in several key ways:

  • Anxiety and privacy concerns: Despite AI’s convenience, 67% of consumers express anxiety about how their data is used, reflecting persistent privacy concerns (Pew Research Center, 2023). This tension creates a paradoxical relationship where consumers benefit from AI-driven marketing while simultaneously fearing its potential misuse.
  • Trust through repeated interactions: Emotional trust in AI systems develops iteratively through repeated, successful interactions, particularly when systems demonstrate high accuracy, consistent performance, and empathetic behavior. Experimental studies show that emotional and behavioral trust accumulate over time, with early experiences strongly shaping later perceptions. In repeated legal decision-making tasks, users exhibited growing trust toward high-performing AI, with initial interactions significantly influencing long-term reliance (Kahr et al., 2023). Emotional trust can follow nonlinear pathways – dipping after failures but recovering through empathetic interventions or improved system performance (Tsumura and Yamada, 2023).
  • Honesty and transparency in AI content: Consumers increasingly value transpar­ency regarding AI-generated content. Companies that openly disclose when AI has been used – for instance, in creating product descriptions – can empower customers by helping them feel more informed and in control of their choices. Such openness often strengthens customer trust and fosters positive perceptions of brands actively embracing transparency in their marketing practices.

Cultural Variations In AI Trust

The global nature of modern marketing requires a nuanced understanding of cultural differences in AI trust. These variations arise from deeply ingrained societal values, historical relationships with technology, and norms around privacy, automation, and decision-making.

For marketers leveraging AI in customer engagement, recognizing these cultural distinctions is crucial for developing trustworthy AI-driven campaigns, personalized experiences, and region-specific data strategies.

Diverging Cultural Trust In AI

Research reveals significant disparities in AI trust across global markets. A KPMG (2023) global survey found that 72% of Chinese consumers express trust in AI-driven services, while in the U.S., trust levels plummet to just 32%.

This stark difference reflects broader societal attitudes toward government-led AI innovation, data privacy concerns, and varying historical experiences with technology.

Another study found that AI-related job displacement fears vary greatly by region. In countries like the U.S., India, and Saudi Arabia, consumers express significant concerns about AI replacing human roles in professional sectors such as medicine, finance, and law.

In contrast, consumers in Japan, China, and Turkey exhibit lower levels of concern, signaling a higher acceptance of AI in professional settings (Quantum Zeitgeist, 2025).

The Quantum Zeitgeist study shows that regions like Japan, China, and Turkey exhibit lower levels of concern about AI replacing human jobs compared to regions like the U.S., India, and Saudi Arabia, where such fears are more pronounced.

This insight is invaluable for marketers crafting AI-driven customer service, finan­cial tools, and healthcare applications, as perceptions of AI reliability and utility vary significantly by region.

As trust in AI diverges globally, understanding the role of cultural privacy norms becomes essential for marketers aiming to build trust through AI-driven services.

Cultural Privacy Targeting In AI Marketing

As AI-driven marketing becomes more integrated globally, the concept of cultural privacy targeting – the practice of aligning data collection, privacy messaging, and AI transparency with cultural values – has gained increasing importance. Consumer attitudes toward AI adoption and data privacy are highly regional, requiring market­ers to adapt their strategies accordingly.

In more collectivist societies like Japan, AI applications that prioritize societal or community well-being are generally more accepted than those centered on individual convenience.

This is evident in Japan’s Society 5.0 initiative – a national vision intro­duced in 2016 that seeks to build a “super-smart” society by integrating AI, IoT, robotics, and big data to solve social challenges such as an aging population and strains on healthcare systems.

Businesses are central to this transformation, with government and industry collaboration encouraging companies to adopt digital technologies not just for efficiency, but to contribute to public welfare.

Across sectors – from manufac­turing and healthcare to urban planning – firms are reimagining business models to align with societal needs, creating innovations that are both economically viable and socially beneficial.

In this context, AI is viewed more favorably when positioned as a tool to enhance collective well-being and address structural challenges. For instance, AI-powered health monitoring technologies in Japan have seen increased adoption when positioned as tools that contribute to broader public health outcomes.

Conversely, Germany, as an individualistic society with strong privacy norms and high uncertainty avoidance, places significant emphasis on consumer control over personal data. The EU’s GDPR and Germany’s support for the proposed Artificial Intelligence Act reinforce expectations for robust transparency, fairness, and user autonomy in AI systems.

According to the OECD (2024), campaigns in Germany that clearly communicate data usage, safeguard individual rights, and provide opt-in consent mechanisms experience higher levels of public trust and adoption.

These contrasting cultural orientations illustrate the strategic need for contextual­ized AI marketing – ensuring that data transparency and privacy are not treated as one-size-fits-all, but rather as culture-aware dimensions that shape trust and acceptance.

Hofstede’s (2011) cultural dimensions theory offers further insights into AI trust variations:

  • High individualism + high uncertainty avoidance (e.g., Germany, U.S.) → Consum­ers demand transparency, data protection, and human oversight in AI marketing.
  • Collectivist cultures with lower uncertainty avoidance (e.g., Japan, China, South Korea) → AI is seen as a tool that enhances societal progress, and data-sharing concerns are often lower when the societal benefits are clear (Gupta et al., 2021).

For marketers deploying AI in different regions, these insights help determine which features to emphasize:

  • Control and explainability in Western markets (focused on privacy and auton­omy).
  • Seamless automation and societal progress in East Asian markets (focused on communal benefits and technological enhancement).

Understanding the cultural dimensions of AI trust is key for marketers crafting successful AI-powered campaigns.

By aligning AI personalization efforts with local cultural expectations and privacy norms, marketers can improve consumer trust and adoption in both individualistic and collectivist societies.

This culturally informed approach helps brands tailor privacy messaging and AI transparency to the unique preferences of consumers in various regions, building stronger relationships and enhancing overall engagement.

Avoiding Overgeneralization In AI Trust Strategies

While cultural differences are clear, overgeneralizing consumer attitudes can lead to marketing missteps.

A 2024 ISACA report warns against rigid AI segmentation, emphasizing that trust attitudes evolve with:

  • Media influence (e.g., growing fears of AI misinformation).
  • Regulatory changes (e.g., the EU AI Act’s impact on European consumer confidence).
  • Generational shifts (younger, digitally native consumers are often more AI-trusting, regardless of cultural background).

For AI marketing, this highlights the need for flexible, real-time AI trust monitoring rather than static cultural assumptions.

Marketers should adapt AI trust-building strategies based on region-specific consumer expectations:

  • North America and Europe: AI explainability, data transparency, and ethical AI labels increase trust.
  • East Asia: AI-driven personalization and seamless automation work best when framed as benefiting society.
  • Islamic-majority nations and ethical consumer segments: AI must be clearly aligned with fairness and ethical governance.
  • Global emerging markets: AI trust is rapidly increasing, making these markets prime opportunities for AI-driven financial inclusion and digital transformation.

The data, drawn from the 2023 KPMG International survey, underscores how cultural values such as collectivism, uncertainty avoidance, and openness to innovation, shape public attitudes toward AI.

For example, trust levels in Germany and Japan remain low, reflecting high uncertainty avoidance and strong privacy expectations, while countries like India and Brazil exhibit notably higher trust, driven by optimism around AI’s role in societal and economic progress.

Measuring Trust In AI Marketing Systems

As AI becomes central to how brands engage customers – from personalization engines to chatbots – measuring consumer trust in these systems is no longer optional. It’s essential.

And yet, many marketing teams still rely on outdated metrics like Net Promoter Score (NPS) or basic satisfaction surveys to evaluate the impact of AI. These tools are helpful for broad feedback but miss the nuance and dynamics of trust in AI-powered experiences.

Recent research, including work from MIT Media Lab (n.d.) and leading behavioral scientists, makes one thing clear: Trust in AI is multi-dimensional, and it’s shaped by how people feel, think, and behave in real-time when interacting with automated systems.

Traditional metrics like NPS and CSAT (Customer Satisfaction Score) tell you if a customer is satisfied – but not why they trust (or don’t trust) your AI systems.

They don’t account for how transparent your algorithm is, how well it explains itself, or how emotionally resonant the interaction feels. In AI-driven environments, you need a smarter way to understand trust.

A Modern Framework For Trust: What CMOs Should Know

MIT Media Lab’s work on trust in human-AI interaction offers a powerful lens for marketers. It breaks trust into three key dimensions:

Behavioral Trust

This is about what customers do, not what they say. When customers engage frequently, opt in to data sharing, or return to your AI tools repeatedly, that’s a sign of behavioral trust. How to track it:

  • Repeat engagement with AI-driven tools (e.g., product recommenders, chatbots).
  • Opt-in rates for personalization features.
  • Drop-off points in AI-led journeys.

Emotional Trust

Trust is not just rational, it’s emotional. The tone of a voice assistant, the empathy in a chatbot’s reply, or how “human” a recommendation feels all play into emotional trust. How to track it:

  • Sentiment analysis from chat transcripts and reviews.
  • Customer frustration or delight signals from support tickets.
  • Tone and emotional language in user feedback.

Cognitive Trust

This is where understanding meets confidence. When your AI explains itself clearly – or when customers understand what it can and can’t do –they’re more likely to trust the output. How to track it:

  • Feedback on explainability (“I understood why I got this recommendation”).
  • Click-through or acceptance rates of AI-generated content or decisions.
  • Post-interaction surveys that assess clarity.

Today’s marketers are moving toward real-time trust dashboards – tools that moni­tor how users interact with AI systems across channels. These dashboards track behavior, sentiment, and comprehension all at once.

According to MIT Media Lab researchers, combining these signals provides a richer picture of trust than any single survey can. It also gives teams the agility to address trust breakdowns as they happen – like confusion over AI-generated content or friction in AI-powered customer journeys.

Customers don’t expect AI to be perfect. But they do expect it to be honest and understandable. That’s why brands should:

  • Label AI-generated content clearly.
  • Explain how decisions like pricing, recommendations, or targeting are made.
  • Give customers control over data and personalization.

Building trust is less about tech perfection and more about perceived fairness, clarity, and respect.

Measuring that trust means going deeper than satisfaction. Use behav­ioral, emotional, and cognitive signals to track trust in real-time – and design AI systems that earn it.

To read the full book, SEJ readers have an exclusive 25% discount code and free shipping to the US and UK. Use promo code ‘SEJ25’ at koganpage.com here.

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References

  • Hofstede, G (2011) Dimensionalizing Cultures: The Hofstede Model in Context, Online Readings in Psychology and Culture, 2 (1), scholarworks.gvsu.edu/cgi/viewcontent. cgi?article=1014&context=orpc (archived at https://perma.cc/B7EP-94CQ)
  • ISACA (2024) AI Ethics: Navigating Different Cultural Contexts, December 6, www.isaca. org/resources/news-and-trends/isaca-now-blog/2024/ai-ethics-navigating-different-cultural-contexts (archived at https://perma.cc/3XLA-MRDE)
  • Kahr, P K, Meijer, S A, Willemsen, M C, and Snijders, C C P (2023) It Seems Smart, But It Acts Stupid: Development of Trust in AI Advice in a Repeated Legal Decision-Making Task, Proceedings of the 28th International Conference on Intelligent User Interfaces. doi.org/10.1145/3581641.3584058 (archived at https://perma.cc/SZF8-TSK2)
  • KPMG International and The University of Queensland (2023) Trust in Artificial Intelligence: A Global Study, assets.kpmg.com/content/dam/kpmg/au/pdf/2023/ trust-in-ai-global-insights-2023.pdf (archived at https://perma.cc/MPZ2-UWJY)
  • McKinsey & Company (2023) The State of AI in 2023: Generative AI’s Breakout Year, www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023- generative-ais-breakout-year (archived at https://perma.cc/V29V-QU6R)
  • MIT Media Lab (n.d.) Research Projects, accessed April 8, 2025
  • OECD (2024) OECD Artificial Intelligence Review of Germany, www.oecd.org/en/ publications/2024/06/oecd-artificial-intelligence-review-of-germany_c1c35ccf.html (archived at https://perma.cc/5DBS-LVLV)
  • Pew Research Center (2023) Public Awareness of Artificial Intelligence in Everyday Activities, February, www.pewresearch.org/wp-content/uploads/sites/20/2023/02/ PS_2023.02.15_AI-awareness_REPORT.pdf (archived at https://perma.cc/V3SE-L2BM)
  • Quantum Zeitgeist (2025) How Cultural Differences Shape Fear of AI in the Workplace, Quantum News, February 22, quantumzeitgeist.com/how-cultural-differences-shape-fear-of-ai-in-the-workplace-a-global-study-across-20-countries/ (archived at https://perma.cc/3EFL-LTKM)
  • Tsumura, T and Yamada, S (2023) Making an Agent’s Trust Stable in a Series of Success and Failure Tasks Through Empathy, arXiv. arxiv.org/abs/2306.09447 (archived at https://perma.cc/L7HN-B3ZC)
  • World Economic Forum (2025) How AI Can Move from Hype to Global Solutions, www. weforum.org/stories/2025/01/ai-transformation-industries-responsible-innovation/ (archived at https://perma.cc/5ALX-MDXB)

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Aug 26 2025
Perplexity Comet Browser Vulnerable To Prompt Injection Exploit via @sejournal, @martinibuster

Brave published details about a security issue with Comet, Perplexity’s AI browser, that enables an attacker to inject a prompt into the browser and gain access to data in other open browser tabs.

Comet AI Browser Vulnerability

Brave described a vulnerability that can be activated when a user asks the Comet AI browser to summarize a web page. The LLM will read the web page, including any embedded prompts that command the LLM to take action on any open tabs

According to Brave:

“The vulnerability we’re discussing in this post lies in how Comet processes webpage content: when users ask it to “Summarize this webpage,” Comet feeds a part of the webpage directly to its LLM without distinguishing between the user’s instructions and untrusted content from the webpage. This allows attackers to embed indirect prompt injection payloads that the AI will execute as commands. For instance, an attacker could gain access to a user’s emails from a prepared piece of text in a page in another tab.”

A post on Simon Willison’s Weblog shared that Perplexity tried to patch the vulnerability but the fix does not work.

A developer posted the following on X:

“Why is no one talking about this?

This is why I don’t use an AI browser

You can literally get prompt injected and your bank account drained by doomscrolling on reddit:”

Things aren’t looking good for Comet Browser at this time.

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Aug 25 2025
How To Leverage AI To Modernize B2B Go-To-Market via @sejournal, @alexanderkesler

In a post “growth-at-all-costs” era, B2B go-to-market (GTM) teams face a dual mandate: operate with greater efficiency while driving measurable business outcomes.

Many organizations see AI as the definitive means of achieving this efficiency.

The reality is that AI is no longer a speculative investment. It has emerged as a strategic enabler to unify data, align siloed teams, and adapt to complex buyer behaviors in real time.

According to an SAP study, 48% of executives use generative AI tools daily, while 15% use AI multiple times per day.

The opportunity for modern Go-to-Market (GTM) leaders is not just to accelerate legacy tactics with AI, but to reimagine the architecture of their GTM strategy altogether.

This shift represents an inflection point. AI has the potential to power seamless and adaptive GTM systems: measurable, scalable, and deeply aligned with buyer needs.

In this article, I will share a practical framework to modernize B2B GTM using AI, from aligning internal teams and architecting modular workflows to measuring what truly drives revenue.

The Role Of AI In Modern GTM Strategies

For GTM leaders and practitioners, AI represents an opportunity to achieve efficiency without compromising performance.

Many organizations leverage new technology to automate repetitive, time-intensive tasks, such as prospect scoring and routing, sales forecasting, content personalization, and account prioritization.

But its true impact lies in transforming how GTM systems operate: consolidating data, coordinating actions, extracting insights, and enabling intelligent engagement across every stage of the buyer’s journey.

Where previous technologies offered automation, AI introduces sophisticated real-time orchestration.

Rather than layering AI onto existing workflows, AI can be used to enable previously unscalable capabilities such as:

  • Surfacing and aligning intent signals from disconnected platforms.
  • Predicting buyer stage and engagement timing.
  • Providing full pipeline visibility across sales, marketing, client success, and operations.
  • Standardizing inputs across teams and systems.
  • Enabling cross-functional collaboration in real time.
  • Forecasting potential revenue from campaigns.

With AI-powered data orchestration, GTM teams can align on what matters, act faster, and deliver more revenue with fewer resources.

AI is not merely an efficiency lever. It is a path to capabilities that were previously out of reach.

Framework: Building An AI-Native GTM Engine

Creating a modern GTM engine powered by AI demands a re-architecture of how teams align, how data is managed, and how decisions are executed at every level.

Below is a five-part framework that explains how to centralize data, build modular workflows, and train your model:

1. Develop Centralized, Clean Data

AI performance is only as strong as the data it receives. Yet, in many organizations, data lives in disconnected silos.

Centralizing structured, validated, and accessible data across all departments at your organization is foundational.

AI needs clean, labeled, and timely inputs to make precise micro-decisions. These decisions, when chained together, power reliable macro-actions such as intelligent routing, content sequencing, and revenue forecasting.

In short, better data enables smarter orchestration and more consistent outcomes.

Luckily, AI can be used to break down these silos across marketing, sales, client success, and operations by leveraging a customer data platform (CDP), which integrates data from your customer relationship management (CRM), marketing automation (MAP), and customer success (CS) platforms.

The steps are as follows:

  • Appoint a data steward who owns data hygiene and access policies.
  • Select a CDP that pulls records from your CRM, MAP, and other tools with client data.
  • Configure deduplication and enrichment routines, and tag fields consistently.
  • Establish a shared, organization-wide dashboard so every team works from the same definitions.

Recommended starting point: Schedule a workshop with operations, analytics, and IT to map current data sources and choose one system of record for account identifiers.

2. Build An AI-Native Operating Model

Instead of layering AI onto legacy systems, organizations will be better suited to architect their GTM strategies from the ground up to be AI-native.

This requires designing adaptive workflows that rely on machine input and positioning AI as the operating core, not just a support layer.

AI can deliver the most value when it unifies previously fragmented processes.

Rather than simply accelerating isolated tasks like prospect scoring or email generation, AI should orchestrate entire GTM motions, seamlessly adapting messaging, channels, and timing based on buyer intent and journey stage.

Achieving this transformation demands new roles within the GTM organization, such as AI strategists, workflow architects, and data stewards.

In other words, experts focused on building and maintaining intelligent systems rather than executing manual processes.

AI-enabled GTM is not about automation alone; it’s about synchronization, intelligence, and scalability at every touchpoint.

Once you have committed to building an AI-native GTM model, the next step is to implement it through modular, data-driven workflows.

Recommended starting point: Assemble a cross-functional strike team and map one buyer journey end-to-end, highlighting every manual hand-off that could be streamlined by AI.

3. Break Down GTM Into Modular AI Workflows

A major reason AI initiatives fail is when organizations do too much at once. This is why large, monolithic projects often stall.

Success comes from deconstructing large GTM tasks into a series of focused, modular AI workflows.

Each workflow should perform a specific, deterministic task, such as:

  • Assessing prospect quality on certain clear, predefined inputs.
  • Prioritizing outreach.
  • Forecasting revenue contribution.

If we take the first workflow, which assesses prospect quality, this would entail integrating or implementing a lead scoring AI tool with your model and then feeding in data such as website activity, engagement, and CRM data. You can then instruct your model to automatically route top-scoring prospects to sales representatives, for example.

Similarly, for your forecasting workflow, connect forecasting tools to your model and train it on historical win/loss data, pipeline stages, and buyer activity logs.

To sum up:

  • Integrate only the data required.
  • Define clear success criteria.
  • Establish a feedback loop that compares model output with real outcomes.
  • Once the first workflow proves reliable, replicate the pattern for additional use cases.

When AI is trained on historical data with clearly defined criteria, its decisions become predictable, explainable, and scalable.

Recommended starting point: Draft a simple flow diagram with seven or fewer steps, identify one automation platform to orchestrate them, and assign service-level targets for speed and accuracy.

4. Continuously Test And Train AI Models

An AI-powered GTM engine is not static. It must be monitored, tested, and retrained continuously.

As markets, products, and buyer behaviors shift, these changing realities affect the accuracy and efficiency of your model.

Plus, according to OpenAI itself, one of the latest iterations of its large language model (LLM) can hallucinate up to 48% of the time, emphasizing the importance of embedding rigorous validation processes, first-party data inputs, and ongoing human oversight to safeguard decision-making and maintain trust in predictive outputs.

Maintaining AI model efficiency requires three steps:

  1. Set clear validation checkpoints and build feedback loops that surface errors or inefficiencies.
  2. Establish thresholds for when AI should hand off to human teams and ensure that every automated decision is verified. Ongoing iteration is key to performance and trust.
  3. Set a regular cadence for evaluation. At a minimum, conduct performance audits monthly and retrain models quarterly based on new data or shifting GTM priorities.

During these maintenance cycles, use the following criteria to test the AI model:

  • Ensure accuracy: Regularly validate AI outputs against real-world outcomes to confirm predictions are reliable.
  • Maintain relevance: Continuously update models with fresh data to reflect changes in buyer behavior, market trends, and messaging strategies
  • Optimize for efficiency: Monitor key performance indicators (KPIs) like time-to-action, conversion rates, and resource utilization to ensure AI is driving measurable gains.
  • Prioritize explainability: Choose models and workflows that offer transparent decision logic so GTM teams can interpret results, trust outputs, and make manual adjustments as needed.

By combining cadence, accountability, and testing rigor, you create an AI engine for GTM that not only scales but improves continuously.

Recommended starting point: Put a recurring calendar invite on the books titled “AI Model Health Review” and attach an agenda covering validation metrics and required updates.

5. Focus On Outcomes, Not Features

Success is not defined by AI adoption, but by outcomes.

Benchmark AI performance against real business metrics such as:

  • Pipeline velocity.
  • Conversion rates.
  • Client acquisition cost (CAC).
  • Marketing-influenced revenue.

Focus on use cases that unlock new insights, streamline decision-making, or drive action that was previously impossible.

When a workflow stops improving its target metric, refine or retire it.

Recommended starting point: Demonstrate value to stakeholders in the AI model by exhibiting its impact on pipeline opportunity or revenue generation.

Common Pitfalls To Avoid

1. Over-Reliance On Vanity Metrics

Too often, GTM teams focus AI efforts on optimizing for surface-level KPIs, like marketing qualified lead (MQL) volume or click-through rates, without tying them to revenue outcomes.

AI that increases prospect quantity without improving prospect quality only accelerates inefficiency.

The true test of value is pipeline contribution: Is AI helping to identify, engage, and convert buying groups that close and drive revenue? If not, it is time to rethink how you measure its efficiency.

2. Treating AI As A Tool, Not A Transformation

Many teams introduce AI as a plug-in to existing workflows rather than as a catalyst for reinventing them. This results in fragmented implementations that underdeliver and confuse stakeholders.

AI is not just another tool in the tech stack or a silver bullet. It is a strategic enabler that requires changes in roles, processes, and even how success is defined.

Organizations that treat AI as a transformation initiative will gain exponential advantages over those who treat it as a checkbox.

A recommended approach for testing workflows is to build a lightweight AI system with APIs to connect fragmented systems without needing complicated development.

3. Ignoring Internal Alignment

AI cannot solve misalignment; it amplifies it.

When sales, marketing, and operations are not working from the same data, definitions, or goals, AI will surface inconsistencies rather than fix them.

A successful AI-driven GTM engine depends on tight internal alignment. This includes unified data sources, shared KPIs, and collaborative workflows.

Without this foundation, AI can easily become another point of friction rather than a force multiplier.

A Framework For The C-Level

AI is redefining what high-performance GTM leadership looks like.

For C-level executives, the mandate is clear: Lead with a vision that embraces transformation, executes with precision, and measures what drives value.

Below is a framework grounded in the core pillars modern GTM leaders must uphold:

Vision: Shift From Transactional Tactics To Value-Centric Growth

The future of GTM belongs to those who see beyond prospect quotas and focus on building lasting value across the entire buyer journey.

When narratives resonate with how decisions are really made (complex, collaborative, and cautious), they unlock deeper engagement.

GTM teams thrive when positioned as strategic allies. The power of AI lies not in volume, but in relevance: enhancing personalization, strengthening trust, and earning buyer attention.

This is a moment to lean into meaningful progress, not just for pipeline, but for the people behind every buying decision.

Execution: Invest In Buyer Intelligence, Not Just Outreach Volume

AI makes it easier than ever to scale outreach, but quantity alone no longer wins.

Today’s B2B buyers are defensive, independent, and value-driven.

Leadership teams that prioritize technology and strategic market imperative will enable their organizations to better understand buying signals, account context, and journey stage.

This intelligence-driven execution ensures resources are spent on the right accounts, at the right time, with the right message.

Measurement: Focus On Impact Metrics

Surface-level metrics no longer tell the full story.

Modern GTM demands a deeper, outcome-based lens – one that tracks what truly moves the business, such as pipeline velocity, deal conversion, CAC efficiency, and the impact of marketing across the entire revenue journey.

But the real promise of AI is meaningful connection. When early intent signals are tied to late-stage outcomes, GTM leaders gain the clarity to steer strategy with precision.

Executive dashboards should reflect the full funnel because that is where real growth and real accountability live.

Enablement: Equip Teams With Tools, Training, And Clarity

Transformation does not succeed without people. Leaders must ensure their teams are not only equipped with AI-powered tools but also trained to use them effectively.

Equally important is clarity around strategy, data definitions, and success criteria.

AI will not replace talent, but it will dramatically increase the gap between enabled teams and everyone else.

Key Takeaways

  • Redefine success metrics: Move beyond vanity KPIs like MQLs and focus on impact metrics: pipeline velocity, deal conversion, and CAC efficiency.
  • Build AI-native workflows: Treat AI as a foundational layer in your GTM architecture, not a bolt-on feature to existing processes.
  • Align around the buyer: Use AI to unify siloed data and teams, delivering synchronized, context-rich engagement throughout the buyer journey.
  • Lead with purposeful change: C-level executives must shift from transactional growth to value-led transformation by investing in buyer intelligence, team enablement, and outcome-driven execution.

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