I recently discovered that OpenAI is set to introduce conversion-optimized ad campaigns starting in early June. This marks a significant step towards creating a performance advertising ecosystem within ChatGPT.
Why does this matter to us? This move by OpenAI, as reported by The Information, confirms the development of conversion-focused ads along with necessary tracking infrastructure and performance measurement tools for advertisers like us.
What’s the current update? OpenAI has communicated with advertisers, stating that those who set up the OpenAI Pixel or Conversions API in advance will get early access to these campaigns in June.
According to the company:
Advertisers configuring conversions by June 1 will gain early access by June 5.
Advertisers can already start tracking conversions using Ads Manager today.
This system enables advertisers to measure actions triggered by ads, enhancing campaign effectiveness.
A deeper look. OpenAI is setting up an infrastructure akin to performance platforms like Google and Meta. With the OpenAI Pixel, advertisers can track website activity post-ad interaction, while the Conversions API allows them to send first-party conversion data back into OpenAI’s systems directly.
This capability allows OpenAI to optimize campaigns for measurable business outcomes, beyond just engagement metrics.
What’s at stake? The future of OpenAI’s advertising strategy largely hinges on measurement accuracy and gaining advertisers’ trust.
With browser restrictions and privacy changes eroding traditional tracking methods, OpenAI’s Conversions API could play a crucial role in demonstrating campaign performance and attribution within AI-driven ad experiences.
I’ve come across some intriguing research from Princeton and UW recently that sheds light on a rather surprising aspect of AI – it’s apparent tendency to conceal sponsorship nearly 65% of the time. As I pondered on this, it struck me how crucial this finding is for those of us navigating the evolving landscape of AI-driven marketing strategies.
This revelation made me question how we’re measuring advertising effectiveness. Are we truly accounting for all variables, especially those hidden from plain sight? For those of us invested in Answer Engine Optimization (AEO), this piece of the puzzle could significantly tweak how we approach our measurement techniques and refine our marketing strategies for 2026.
What does this mean for each of us in marketing and advertising? It’s a call to action to re-evaluate and possibly overhaul our current strategies, ensuring we adapt to these covert tendencies within AI functionalities. I’m convinced that understanding these nuances will empower us to craft more transparent and effective campaigns, ultimately enhancing our overall AEO outcomes.
While AI continues to surprise us with its capabilities, I find it crucial to stay updated and adaptable, utilizing insights like these to steer our strategies intelligently. How do you plan to integrate this newfound knowledge into your 2026 marketing strategy?
I recently discovered a fascinating development from OpenAI that has the potential to revolutionize e-commerce advertising. They’ve started transforming product catalogues into automated ads within ChatGPT, allowing retailers to seamlessly scale their campaigns.
Retailers now have the option to connect their product feeds directly to ChatGPT. This integration means that the platform can generate ads automatically, using product names, images, and other attributes. Gone are the days of manually crafting campaigns!
For users, these ads will still appear beneath responses and remain clearly labeled as sponsored content. There’s no change here in terms of user experience.
As someone interested in how e-commerce brands operate, I’m intrigued by this update. It significantly reduces the barriers that retailers with large inventories face when running scaled ads.
Brands have the flexibility to establish rules on which products are featured, allowing the system to efficiently generate ads. It reminds me of how shopping campaigns function on platforms like Google, leveraging structured feeds for both organic and paid visibility.
Previously, ChatGPT could use product data for answering queries but not for advertising purposes. Now, with this advancement, the same data supports both functions, bridging the gap between organic presence and paid campaigns.
This shift signals how OpenAI is looking to monetize shopping. Instead of taking a slice of transactions, they’re targeting ad budgets typically spent on platforms like Amazon and Meta.
Industry analyst Debra Aho Williamson calls this shift to feed-based automation a necessity, highlighting ChatGPT’s unique approach to serving ads based on conversational intent, a distinct advantage.
According to ad tech partners like StackAdapt, the integration with existing feeds is straightforward, easing the adoption process.
This latest move is part of a series of updates that focus on performance, including cost-per-click bidding and new conversion tracking tools. Cost-per-action models are reportedly in development, suggesting an even deeper focus on performance advertising.
I’m eager to see more retailers experimenting with ChatGPT as a performance channel. The ease of setup might make this an attractive option, but the real test will be if conversational intent can drive conversions as efficiently as traditional methods.
The bottom line is that OpenAI is effectively turning product feeds into ads, making ChatGPT a more potent, scalable channel for e-commerce advertising.
Adthena has unveiled an exciting new platform that offers advertisers a clearer view of the ChatGPT ad landscape. This development gives me unprecedented insight into my competitors and ad performance within the ChatGPT ecosystem.
As a digital marketer, I find Adthena’s ChatGPT Intelligence Platform fascinating because it’s the first tool of its kind offering whole-market visibility into ChatGPT Ads, similar to the comprehensive insights I already get from Google Ads.
Tracking over 300,000 daily prompts, Adthena allows me to see which brands are advertising, the locations of these ads, and the messaging strategies employed. It’s a powerful way to stay ahead in a competitive field.
The current native tools in ChatGPT provide a limited, self-centric view of my ad performance. Now, Adthena bridges that gap, enabling me to understand my competitors’ positions, share of voice, and specific prompt activity in an often unclear channel.
What I find particularly useful is how Adthena offers a comprehensive view of ad appearances across ChatGPT conversations, complete with competitive intelligence on advertising bids and creative types used.
The platform also provides real-time recommendations to optimize my campaigns—it’s about taking action based on insights rather than just watching things happen.
Furthermore, I can evaluate ad copy effectiveness, monitor my brand’s presence, and track share of voice—all from one dashboard that integrates both ChatGPT and Google Ads data, helping me make informed budget decisions as search behaviors evolve.
The introduction of this tool follows Adthena’s earlier AdBridge tool, which helps in the seamless transition of Google Ads campaigns into ChatGPT’s Ads Manager, indicating a burgeoning AI-driven search advertising ecosystem.
Ashley Fletcher, CMO, emphasizes that early adopters like me have the potential to influence the competitive terrain, with the platform clearly indicating the best strategies to employ.
Looking ahead, I anticipate more third-party tools emerging as advertisers like myself desire greater transparency in AI-driven ad environments. The pace at which brands recognize ChatGPT Ads as a vital performance channel will likely drive this adoption, possibly urging platforms like ChatGPT to enhance their native reporting capabilities.
The bottom line is that Adthena is positioning itself as the go-to visibility layer for ChatGPT Ads, offering me a clearer understanding of this rapidly growing but still enigmatic channel.
I’ve noticed that advertisers, including myself, are expressing concerns about AI Max’s limited control over landing pages compared to the older Dynamic Search Ads (DSA), especially as Google acknowledges some existing gaps in this area.
During a recent discussion on LinkedIn, digital marketing expert, Gabriele Benedetti, pointed out that AI Max doesn’t offer the same URL-based targeting controls that DSA campaigns did. This is a significant issue for those of us who depend on detailed URL targeting for effective campaigns.
To give more context, DSA allowed us to fine-tune campaigns to align with website architecture using categories, URL paths, and page rules. Unfortunately, AI Max doesn’t yet offer that detailed level of control.
For advertisers like me, managing large or structured sites, maintaining campaign structures that reflect site architecture is crucial. Losing detailed control over where users land could impact the user experience, relevance, and conversion rates.
This situation underscores a larger conflict within Google Ads: balancing automation with our need for control.
In response, Google Ads Liaison Ginny Marvin assured us that AI Max does support some URL-based controls that include:
URL rules and combinations
Page feeds with custom labels
URL inclusions at ad group level and exclusions at campaign level
Nevertheless, she admitted that not all DSA targeting rules, like “page contains” conditions, are supported yet.
Reading between the lines, it seems Google isn’t taking away control entirely but rather redefining how it operates. Instead of elaborate rule-building, we’re being encouraged to use structured inputs, such as page feeds and labels, which AI Max can interpret.
For those of us transitioning from DSA to AI Max, there’s a transition phase where existing URL rules will persist, albeit with limitations. Unsupported rules will remain active as read-only—functional but uneditable.
This setup, however, is merely a stopgap and not a permanent solution.
Looking forward, Google plans to further enhance controls, including introducing content and title-based exclusions at the account level later this year. This would add to the “inventory-aware” capabilities of AI Max, which already automatically excludes out-of-stock items.
The takeaway is clear: AI Max is evolving, yet it doesn’t fully replace DSA’s granular control, and this has been a point of contention for advertisers like me.
If you’re keen on diving deeper into the discussion, you can check the full conversation on LinkedIn.
The exciting news is here! OpenAI has officially launched its self-serve ChatGPT Ads Manager, bringing a revolutionary change for U.S. advertisers by removing the $50K minimum spend requirement.
Now, I’m thrilled to share that we simultaneously introduced four OpenAI Ads nodes specifically for Profound Agents. This means you and I, as marketers, can now integrate this data directly into agentic workflows, enhancing our marketing strategies.
Moreover, this update brings CPC bidding to the table alongside the existing CPM model, offering more flexibility and control over ad investments. It’s exciting to think about the possibilities this opens up for audience engagement and campaign optimization.
I’ve always believed that negative keywords are more than just a checklist. In 2026, they represent strategic decisions that shape how the algorithm interprets your ad account.
If you’re still viewing negative keywords as a mere maintenance task, you’re missing out. Each exclusion signals who you intend to target, what you’re willing to pay for, and how you expect your campaigns to perform.
Let me share six key decisions that define today’s negative keyword strategy, and explain their growing significance.
Negative keywords help shape our campaigns so the right ad appears in front of the right audience. Achieving alignment between the user’s search query, your ad, and the landing page is crucial for creating an exceptional user experience.
When this alignment is absent, budget is wasted, click-through rates (CTR) decline, Quality Scores suffer, and cost-per-click (CPC) rises. These challenges can make the algorithm seem like it’s working against you.
However, many of us weren’t taught how negative keywords fit into an overall account strategy, only how to add them. Let me delve into these six critical strategic choices.
Determining how aggressive to be with negative keywords is the first decision every account manager needs to make, yet it’s often overlooked.
Are you relentlessly removing every low-performing search term? Are you deliberately allowing space for keyword opportunities? Or do you find yourself somewhere in between?
There isn’t a universal right answer, but it is essential to choose your level of aggression. A growth-focused account may need a less aggressive approach, whereas an efficiency-focused account might require more aggression. This choice should align with the account’s goals and performance metrics.
Using the right match types for negative keywords is crucial. Most advertisers default to one type without understanding why.
Here’s my breakdown:
Use negative exact match for strictly removing specific long-tail variations, negative phrase match for groups of related queries, and negative broad match for eliminating words that indicate a misaligned audience.
A well-thought-out negative keyword strategy utilizes all three match types, each serving a distinct purpose.
When should you add negative keywords? This is a consideration I’ve seen approached in various ways by different account managers.
Some add negatives weekly regardless of data, while others only when conversions drop, or during quarterly reviews. The right approach depends on your goals and data-driven insights.
For growth-focused accounts, trigger addition when a query exceeds three times your target CPA over 90 days without conversion. For efficiency-focused accounts, use a stricter budget-focused trigger.
The timeframe for reviewing data when deciding on negative keywords is another crucial factor.
A 30-day window might be too aggressive unless dealing with short-term promotions. A 90-day window is balanced and often recommended, while a 365-day window may be conservative, excellent for long buying cycles.
Choosing the correct timeframe informs smarter strategic decisions.
The role of AI in campaign sculpting through negative keywords is increasingly pivotal.
Decide how much control you want versus how much you rely on the machine. Some eliminate competitor keywords, yet others let them through for conversions.
While AI holds more information than us, sculpting is necessary for communicating your intent.
In 2026, we have more options than ever for managing negative keywords effectively.
You can conduct a manual review, use AI tools for suggestions, or let AI handle it fully. The key is balancing efficiency with oversight according to the comfort level and stakes of the account.
In every era, a few principles remain true. Keep your search terms report in check, make sure to update negatives as your campaign evolves, and always remain flexible to changes in user intent.
Ultimately, efficient advertising starts with strategic exclusion. What we choose not to target often holds equal importance to what we do target.
Recently, I’ve noticed that ChatGPT is rolling out ads to users who aren’t logged in. This change could dramatically boost the ad inventory as advertiser interest surges.
What’s happening. According to early reports, ads are seamlessly appearing within conversations for those not logged in, although OpenAI hasn’t made a formal announcement. Interestingly, these ads fit into the chat responses rather than looking like traditional banners.
Why we care. For me, the expansion to logged-out users means more inventory, allowing budgets to stretch further and reach audiences with intent. If this trend continues, I believe ChatGPT could become a powerhouse in the performance marketing arena.
Zoom in. I’ve noticed that advertisers in the pilot phase struggle to spend due to limited inventory, despite lowered financial barriers (from $200,000 to $50,000). Expanding the potential audience seems like a logical step to overcome this hurdle.
User experience. Personally, I find the ads relatively unobtrusive and well-integrated into conversations, though some minor UX issues persist.
Between the lines. It’s clear to me that this is an inventory issue, not a demand one. Advertisers are eager, and OpenAI is diligently working to scale up.
What to watch. I’ll be keeping an eye on whether OpenAI formalizes this rollout and expands further, which will indicate how rapidly ChatGPT can evolve into a competitive ad channel.
Bottom line. I think opening ads to logged-out users is the key that could convert advertiser interest into substantial spending power for ChatGPT.
I recently came across OpenAI’s testing of a new ChatGPT Ads Manager interface, which heralds a promising shift towards a more scalable and self-directed advertising platform.
Advertisers are buzzing about their experiences with the new Ads Manager interface for ChatGPT. It’s a leap forward, offering a mature advertising platform where we can manage campaigns in real time. This is a significant improvement over what we’ve had so far in terms of reporting and controls, as shared by digital marketers Juozas Kaziukėnas and Glenn Gabe through their detailed images.
What’s New: The Ads Manager is essentially a dashboard that allows me to run, monitor, and optimize campaigns in real-time—a significant advancement from the limited reporting we’ve seen previously. Juozas Kaziukėnas and Glenn Gabe shared some fascinating insights through images of this evolving interface.
Why It Matters: Up to now, ChatGPT ads have been in the nascent stages, with advertisers relying on basic tools like weekly CSV reports. The introduction of a comprehensive Ads Manager indicates OpenAI’s efforts to construct an infrastructure analogous to what we see in platforms like Google Ads or Meta.
Zoom In: I’m noticing more ads popping up inside ChatGPT, with brands such as Best Buy and Expedia being visible in early tests. The increase in ad inventory, combined with a sophisticated management interface, suggests a swift expansion in monetization endeavors.
What to Watch: As the Ads Manager continues to evolve, I’m looking forward to more refined targeting, reporting, and automation features. Initial feedback indicates there’s still room for growth here, especially concerning ChatGPT ads.
First Seen: Glenn Gabe was among the first to share glimpses of the ChatGPT ads manager interface on X.
I recently dove into Google Ads Asset Studio to see what all the hype was about. I’ve heard declarations like, “Google just ended all excuses for not running video ads!” and “It’s a total game-changer; no production budget needed!”
The process is supposed to be simple: upload some images and get campaign-ready videos in minutes. Using Google Ads > Tools > Asset Studio, I can manage and scale images and videos effortlessly across various ad formats.
Recent additions like Veo, Google’s AI video model, and Nano Banana Pro suggest we can transform a few product images into engaging video ads almost instantly.
But does it really change the advertising game? Let’s explore if it’s truly worth our time.
From the Think with Google article about AI-generated ads, such as those for Cosmorama, I tried to reverse-engineer their imaginative approach. Unfortunately, despite using Nano Banana and Veo, I encountered many limitations.
For instance, I found the lack of scene-level control problematic. No prompting for video scenes meant I couldn’t guide the animation’s motion or pacing.
When generating videos, anything that resembled a human face—AI-generated or not—caused errors. This restriction limited my asset options significantly.
The audio options were also very limited. Unlike Cosmorama’s videos with cinematic scores, I was stuck with a small set of preloaded audio without the ability to upload custom tracks.
Overall, while Veo 3 introduced significant restrictions within Asset Studio, requiring a shift from expectations of advanced creative freedom.
While simplifying production could be beneficial, if you were expecting full creative control, you might be disappointed.
Thinking about whether Asset Studio truly saves time and effort, my experience suggests it’s a mixed bag. For brands previously in need of full production teams, Asset Studio might offer a faster and more cost-effective solution. However, for agencies or individuals incorporating this into existing workloads, it turns creative constraints into a newfound responsibility.
Regarding AI ad compliance, it’s worth noting there are no current U.S. federal laws against using AI in ads. However, places like New York are setting new precedents with upcoming laws requiring disclosure of AI use.
On the brighter side, if you use Asset Studio with ethical transparency in mind, although there’s no watermark or disclosure methods built-in, Google’s SynthID supports invisible AI tagging.
Could this tool live up to its potential without succumbing to ‘AI slop’? Josh Spanier from Google suggests not to worry, yet it’s essential to maintain control to avoid low-quality AI-generated ads from being published unwittingly.
Asset Studio indeed offers a streamlined way to bring product images to life, optimized for product integrity through tools like Nano Banana 2.
Features like quick trimming and leveraging simple templates show promise in turning around high-performing, concise ad creatives, even doubling CTR compared to previous client efforts.
In conclusion, while Asset Studio isn’t a complete game-changer, it provides tools that democratize creative access for those lacking a full production budget. However, it’s vital to measure the outcomes in terms of conversions and sales.
I’m running tests to see what truly holds up. Stay tuned.
