I’ve always been intrigued by how technology transforms the way we engage with the world. Recently, OpenAI has taken a significant step by introducing ads in select markets. This move opens up exciting opportunities for brands to connect with users through AI-driven experiences.
OpenAI’s latest initiative to incorporate ads signals a strategic push into monetizing their platforms while keeping the premium tiers ad-free. This careful balancing act allows OpenAI to expand their ad reach without compromising the user experience of their paid plans.
Driving the news. Ads are being rolled out for users on Free and Go plans in Australia, New Zealand, and Canada. This is a fascinating development for those of us observing how AI interfaces evolve.
These changes currently apply solely to lower-tier plans.
The Pro, Business, Enterprise, and Education tiers will continue to offer an ad-free experience.
Why I care. As someone interested in AI and marketing, this presents an incredible opportunity to explore new channels for reaching users. The expansion into more markets means we can experiment and learn how ads can be effectively integrated into conversational interfaces, potentially reshaping the future of search and discovery.
The big picture. Most AI platforms have traditionally steered clear of conventional advertising, relying more on subscription models and enterprise partnerships. But this move by OpenAI might just be the tipping point for change.
It seems that OpenAI is:
investigating new revenue opportunities,
understanding the role of ads in conversational platforms,
and finding that sweet spot between monetization and a seamless user experience.
Yes, but: It’s clear that OpenAI wants to maintain a distinction between their free and premium offerings, ensuring that an ad-free experience remains a coveted advantage.
The bottom line: In cautious steps, OpenAI is exploring the world of ads within AI-driven products, starting with limited markets and tiers. This calculated approach allows them to understand the impact of advertising on their platforms.
I’ve been following Google’s strides in ad safety, and their recent updates with Gemini have caught my eye. Gemini’s AI-driven enforcement is not only faster but more accurate, eliminating more than 99% of bad ads even before they appear in 2025. This means we’re seeing fewer false suspensions and stricter adherence to ad policies.
Diving into Google’s 2025 Ads Safety Report, I’m amazed at the scale: 8.3 billion ads were blocked or removed globally, and 24.9 million advertiser accounts got suspended last year. It’s impressive to think that over 99% of these policy-violating ads never saw the light of day, thanks to the power of AI.
Google also pointed out how Gemini’s capabilities significantly improved ad safety:
Gemini slashed incorrect advertiser suspensions by 80%.
The system processed four times more user reports compared to the previous year.
It enhanced the detection of scams by better understanding ad intent.
Looking at the numbers, we see a staggering impact:
602 million scam-related ads removed
4 million scam-linked accounts suspended
4.8 billion ads restricted
480 million web pages blocked or restricted
245,000+ publisher sites actioned
35 policy updates made in 2025
In the United States alone, 1.7 billion ads were removed, and 3.3 million advertiser accounts were suspended in 2025. The main reasons included:
Abusing the ad network
Misrepresentation
Sexual content
Personalization violations
Dating and companionship ads
Why do I care about this? Because stronger AI-driven ad enforcement impacts the way ads run or get flagged. Google claims Gemini enhances precision and reduces unwarranted suspensions, which might prevent unexpected interruptions for genuine brands. However, as AI reviews tighten, we advertisers must ensure complete policy compliance.
Some UK and US advertisers experienced waves of unexplained disapprovals, citing no discernible issues, highlighting the intricacies of automated oversight.
Gemini’s approach to ad enforcement is exciting. By evaluating billions of signals—like account age and user patterns—it’s capable of identifying malicious activity quicker than previous systems. By the end of 2025, most Responsive Search Ads were assessed instantly, blocking harmful material before it could launch. Google aims to apply this capability across more ad formats soon.
Yet, there’s a balance to maintain. Aggressive automation may disrupt campaigns, but Google’s emphasis on nuanced understanding is crucial for reducing incorrect suspensions, which is essential for brands relying on continuous ad visibility.
In conclusion, Google is banking on Gemini to enhance ad safety, aiming to curtail sophisticated scams while assuring advertisers that legitimate activities won’t be hindered by stricter controls.
Hey there! Have you ever wondered how to make your content stand out in today’s digital world? I sure have. Let me share with you some amazing strategies I’ve discovered for optimizing content specifically for Gemini, Google’s innovative AI-driven platform. It’s all about enhancing visibility in AI Overviews and answer engines.
By focusing on Answer Engine Optimization (AEO), I’ve learned from top experts how to ensure my content gets the attention it deserves. Let’s dive into some actionable tactics that can really make a difference.
The great thing about mastering Gemini optimization is that it helps boost my content’s visibility across various digital landscapes, especially in areas like AI Overviews. These strategies have really opened new doors for me and my digital presence.
I keep hearing about AI search as if it’s become the norm for everyone—an inevitable shift in how we discover information. But in reality, it’s not so simple.
AI search is indeed on the rise, but it’s not being adopted equally. The real divide comes down to something rarely discussed: household income.
My agency started closely monitoring search behaviors back in early 2025. In our latest study, we took a closer look through the lens of household income.
The results? A significant divide emerged. While a general 27% of users claim to regularly use ChatGPT, income-specific data paints a different picture.
In essence, higher-income households are significantly more likely to use generative AI tools.
This major variation challenges the common assumption that AI adoption progresses uniformly across demographics.
We’re seeing a new layer of digital inequality in accessing information. This divide, visible across the UK, is adding to an existing digital skills gap.
AI adoption relies on more than just having the right tools. It’s also influenced by:
If you work in certain sectors like digital or corporate, you’re more likely to be encouraged to incorporate AI into your daily routines.
Capability plays a role, too. For some, using AI tools comes naturally. For others, it’s an intimidating process without proper guidance.
Then there’s confidence—trust in AI tools varies. In our research, users on platforms such as Perplexity report high levels of trust, but they remain niche.
These disparities mean that AI literacy is quickly becoming another possible layer of the digital divide, augmenting the advantage of the digitally savvy.
For businesses, this division has tangible implications. Different audiences are developing distinct behaviors:
This isn’t a minor shift. Making incorrect assumptions about user behavior could lead to strategic missteps, like over-investing in one area and neglecting another.
Yet, there’s an upside. Fast adopters of AI are often the very decision-makers and high-income consumers that brands value most.
These users are frequently termed “digital explorers” and see AI as an integral part of their decision-making process.
Behavior and confidence are intertwined, shaping how far users will go with AI.
To respond to these fragmented behaviors, brands need to:
A comprehensive understanding of AI’s role at every step of the customer journey becomes essential.
Ultimately, as AI weaves deeper into our lives, the human element remains paramount in determining the future of search.
When it comes to achieving success in AEO, I’ve found that partnering with a growth marketing agency is essential. Through their integrated strategies encompassing SEO, PR, and social media, these agencies significantly enhance AI visibility.
The dynamic combination of these marketing strategies helps boost AI interactions, creating a more visible online presence. I’ve noticed that these agencies utilize AI-driven tactics that elevate our approach to targeting our desired audience effectively.
I’ve often wondered how AI crawlers work differently compared to traditional bots, until I dove deeper into their world. My aim is to ensure my brand’s content is not only crawlable but also highly visible to Large Language Models (LLMs) and AI-driven search engines. Let me take you through this transformative journey.
The evolution from traditional bots to AI crawlers marks a significant shift in digital presence strategies. Knowing how to optimize for these sophisticated visitors is crucial for maintaining and enhancing brand visibility. Let’s explore what makes AI crawlers unique and how I can prepare my website to meet their demands.
I’ve discovered the power of turning AI into a strategic ad partner using prompts that dive deep into buyer emotions, target high-intent audiences, and tackle objections.
Many of us are already tapping into various generative AI tools to breathe life into our marketing ideas and boost the effectiveness of ad campaigns.
Using prompts isn’t just a solo brainstorming alternative; it’s a productivity booster that opens up a world of possibilities.
In this guide, I’ll share some of my favorite marketing prompts for ad campaigns, designed to spark creativity in crafting your own prompts.
Why Use Prompts for Online Ads?
Prompts are your fast track to brainstorming ad elements like triggers, emotions, actions, and your target audience.
The beauty of prompts is they’re versatile. You can tweak outputs across different channels and initiatives like ads, emails, and social media.
Getting closer to optimal campaigns from the outset means saving time, a real boon for low-budget efforts that are hungry for feedback.
The prompts themselves make all the difference. Craft strong questions to extract valuable insights from large language models (LLMs).
Feeling stuck? Ask AI tools for prompt recommendations or use mine. Here’s a selection I often use for online ads.
Emotional Trigger Prompt
Purchases are fueled by emotions, so it’s essential to tap into what makes your audience feel.
Try this prompt: “What are the top emotional triggers that would make X audience buy Y product?”
As an example, I explored what emotional triggers would prompt parents to purchase math learning software for their kids. The LLM highlighted key triggers alongside scarcity and urgency hooks:
Fear of falling behind: Anxiety and a protective instinct. Example: “Ensure your child never falls behind in math.”
Desire to give kids a competitive advantage: Ambition and pride. Example: “Equip your child with math skills that top students develop years ahead.”
Relief from homework stress at home: Relief and peace of mind. Example: “Say goodbye to math homework battles at home.”
Purchase Intent Prompt
Explore these questions to identify who’s ready to buy your product or service now:
Who is most likely to buy immediately?
Who needs convincing?
Who will never buy?
To prevent wasting ad spend, focus on audiences poised for purchase and steer clear of those unlikely to buy.
Keep probing which audiences are most likely to convert. Use the LLM’s feedback to get more specific with your ads.
In the math software scenario, the LLM advised that parents of struggling kids in math were the best converters due to high urgency and low friction.
The second-best group? Homeschooling parents, motivated by the need to manage the entire curriculum. This insight allowed us to craft ads and test conversions.
Overcoming Objections Prompt
Addressing objections is crucial for sealing the deal. Ask for three to five potential objections buyers might have about your product.
In our math software example, the LLM identified these objections:
My child already has too much screen time.
Will this actually improve my child’s math skills?
It’s too expensive.
Next, craft a persuasive counter-argument for each using logic, emotion, and evidence. For “it’s too expensive,” consider:
Logic: “Less than the cost of a tutor.” Establishes a higher anchor, making the price seem reasonable without calling it cheap.
Emotion: “Don’t let your kids fall behind in math.”
Proof: “80% of students improve by one letter grade in two months.”
Psychological Profile Prompt
Request a comprehensive psychological profile of your ideal customer from an LLM. Use questions like:
What are your ideal customer’s fears?
What are their frustrations?
What do they envy?
What do they pretend doesn’t bother them?
What keeps them up at night?
In the math software scenario, I asked, “What or who do my ideal customers envy?”
The response indicated parents envy children in enrichment or advanced classes, seeking future educational opportunities.
Here’s a message for them: “Help your child stay ahead instead of playing catchup.”
The Lifetime Value Prompt
Sustain long-term success by focusing on customer lifetime value (LTV) instead of one-time sales.
Consider these questions:
Why might your customers stick around?
Why might they buy more?
What retention strategies are effective?
For a luxury furniture brand, we turned these into a brief playbook to boost LTV. The LLM suggested shifting from a transactional relationship to a long-term design partnership.
For instance, segment your customer base and use direct mail for your highest-value group by sending a lookbook. Though it seems old-school, it can result in a higher LTV than general mailings.
Your clients deserve strategic thinking and clear priorities. AI tools help us achieve that, supporting both strategy and execution.
Fix Lagging Average Order Value Prompt
When performance dwindles, it’s tempting to ask sweeping questions about metrics like return on ad spend (ROAS).
But that’s a path well-trodden, often leading to generic, uninspired checklists.
We grapple with B2C and B2B search query overlaps. Focusing on B2B users is challenging but crucial for securing high-value, long-term customers.
We noticed a likely cause of a B2B client’s lagging ROAS: average order value (AOV) as reflected in Google Ads’ Value/Conv. Smart Bidding had shifted to high-converting but lower-quality sessions, impacting performance.
We enlisted an LLM to ascertain and address the issue.
With Ads Advisor (Gemini) in Google Ads, the initial response focused on trivial consumer scenarios, like holiday themes.
Upon refining the prompt, we received more targeted, actionable suggestions, saving valuable time.
We doubled down on audience targeting, emphasizing specific Google audience segments and first-party audiences with value rules.
AOV increased. While it didn’t promise higher order values, it honed focus on B2B intent and reduced low-priority consumer purchases.
Key performance metrics improved, guiding the path to growth and profitability.
Better Prompts Lead to Better Campaigns
Begin simply — incorporate one or two of these prompts into your next campaign, tweak the outcomes, and expand from there. Over time, you’ll establish a repeatable system where AI becomes integral to your marketing workflow.
Most product feeds are traditionally geared towards paid media. But I’ve discovered aligning them with organic search behaviors significantly enhances visibility across Shopping and AI platforms.
When I ask most e-commerce brands who manages their product feed, the response is usually the same: the paid media team is in charge.
Often, a feed management tool is categorized under PPC. It might even be a relic created by the shopping team years ago, with titles that haven’t been updated since. SEO, unfortunately, rarely has its say in these strategies.
Whether you’re focused on AI-powered search or traditional clicks, excluding SEO from your product feed strategy means missing out on substantial opportunities.
AI Shopping Results Are Connected to Google Shopping Data
According to a recent Peec AI study, up to 83% of ChatGPT carousel products reflect Google’s organic Shopping results—and 60% of those are from Shopping positions 1-10.
Data shows how ChatGPT’s product carousel matches Google Shopping’s organic results, with Google dominating over Bing.
On Google’s side, their Shopping Graph includes over 50 billion product listings, directly feeding AI Overviews, AI Mode, and Gemini. AI Overviews now appear in about 14% of shopping inquiries, a leap from roughly 2% in late 2024. As I’ve seen, AI search results are still largely based on the traditional search engine result page (SERP).
SEO is vital for establishing brand authority. It opens up valuable opportunities to collaborate across channels for improved search visibility. It’s time for SEOs, commerce, and paid media teams to come together.
The Case for a Dedicated Organic Feed
Most brands run a single product feed aimed at Google paid shopping campaigns. The focus is often on optimizing titles for bid relevance and descriptions for Quality Score rather than for user search behaviors.
As user search habits evolve, aligning product data with search queries becomes increasingly important. A title with too many paid-friendly modifiers doesn’t necessarily match natural search queries.
When we tested this with a major ecommerce brand, our agency’s AI SEO team worked with the commerce team to create a dedicated product feed just for organic listings. Optimizing specifically for organic visibility made a world of difference.
After implementation, we saw the following results:
Organic listing CTR increased by 10% month over month and purchasing rates rose by 4%.
A product-level test revealed a 92% increase in revenue for free listings, with an 83% increase in visibility and a 14% rise in add-to-cart rates.
Organic optimizations alone generated 35,000 impressions with a 1.4% CTR—55% higher than paid CTR for the same period.
We recognized that our paid and organic strategies serve different needs, so they should be optimized independently. Organic feed titles should reflect how customers naturally search.
What to Prioritize in an Organic Feed Strategy
Not all feed attributes are equally important. Whether you’re setting up a dedicated organic feed or auditing an existing one, these elements are essential starting points.
Focus on Titles as the Key Lever
Google’s algorithm favors feed titles highly in matching products to queries. As Google documentation suggests, including significant attributes can lift performance. Consider what customers might conversationally say when searching for your product.
Google’s Merchant Center documentation emphasizes aligning your feed strategy with how customers shop, enhancing their search journey.
Don’t Neglect Global Trade Item Numbers (GTINs)
According to Google’s GTIN documentation, products with accurate GTINs gain significant visibility. Data shows well-matched products can attract up to 40% more clicks and are key in aggregating reviews.
Images Add Value
Images are often flagged in Merchant Center disapprovals. Products with both standard and lifestyle images engage more users. Google’s Product Studio can assist in editing, helping SEO and creative teams work together on feed assets.
Optimize Key Attributes: product_highlight and product_detail
product_highlight allows you to add concise benefit statements in Shopping views. Descriptions like “water-resistant for light rain commutes” are more beneficial than vague terms like “high-quality material.”
product_detail gives structured specs that influence Google’s filters in product grids.
The semantic optimization SEOs apply to product pages should guide feed attributes. Product and content teams’ insights are vital not just for PDPs but also for feeds.
Your Feed is Your Agentic Commerce Foundation
Investing in feed optimization for organic visibility will prepare your brand for the agentic commerce landscape.
Google’s Universal Commerce Protocol is essential for AI agents to complete transactions directly in AI Mode and Gemini. Feeds entering the Shopping Graph fuel AI responses to shopping requests.
Google added the native_commerce attribute for UCP-powered buy buttons across Google services. Several new conversational commerce attributes will soon be available, which means feed and on-page content must be in sync.
Building a Cross-Channel Strategy for AI Search
Product feed strategy is ideal for cross-team collaboration to test, execute, and measure brand visibility. A harmonized approach across all surfaces benefits both traditional and AI-driven search outcomes.
SEOs contribute keyword intelligence and semantic insights about AI system matching.
Commerce teams manage product data and retail relationships.
Paid teams have the infrastructure and expertise in feed health management.
These teams should collaborate to create a unified AI SEO strategy. Reviewing existing feeds and gathering all relevant stakeholders is essential to developing a comprehensive and effective product feed strategy.
I’ve noticed that the search landscape is evolving quickly, and it’s crucial for our companies to adapt. Are we appearing in Large Language Model (LLM) and AI-driven searches?
To thrive in this new era, understanding the Answer Engine Optimization (AEO) landscape is essential. Let me guide you on how to optimize your presence in AI search to stay ahead.
As someone who’s been closely observing AI advancements, I found Google’s AI Overviews to have improved significantly. By February, they correctly answered standard factual benchmarks 91% of the time, a notable rise from 85% back in October. This assessment came from a rigorous analysis conducted by The New York Times in collaboration with the AI startup, Oumi.
Yet, considering Google processes more than 5 trillion searches annually, this still implies that millions of answers could be incorrect every hour. In essence, there’s much room for improvement.
Why it matters to me. My interactions with Google have evolved from just link clicks to encountering AI-generated summaries. This evolution suggests that while AI Overviews have gotten better, they still mix accurate responses with poor sourcing and blatant errors, potentially misleading searchers and affecting visibility for many publishers.
The nitty-gritty details. Oumi put 4,326 Google searches to the test using SimpleQA, a benchmark known for measuring factual precision in AI systems. AI Overviews hit a 91% accuracy rate post-upgrade to Gemini 3 from Gemini 2’s 85%.
The more pressing issue for me is the sourcing. Oumi discovered that more than half of February’s correct responses were ‘ungrounded,’ meaning the linked references didn’t fully back the answers.
This lack of grounding makes verification a challenge. Even if the answer is correct, the linked pages might not sufficiently illustrate the reasoning.
What shifted. While the accuracy saw improvements from October to February, grounding declined. In October, 37% of accurate answers were ungrounded; by February, this figure increased to 56%.
Real-world examples. The Times pointed out several inaccuracies: For instance, Google incorrectly dated when Bob Marley’s home became a museum. Google’s answer was 1987, but the actual year was 1986, and the cited sources conflicted. A search about Yo-Yo Ma and the Classical Music Hall of Fame yielded a link to the Hall’s site, yet Google stated he wasn’t inducted. Moreover, while Google got Dick Drago’s age at death right, it flubbed his date of death.
Google’s standpoint: Google contested the Times’ findings, arguing that the benchmark used in the study was flawed and didn’t mirror actual search behavior. Google spokesperson Ned Adriance mentioned that the study had some ‘serious holes.’
Furthermore, Google asserted that its AI Overviews utilize search ranking and safety measures to minimize spam and has consistently cautioned that AI responses might contain errors.
