Welcome to issue #45 of the Marketing with AI Weekly Roundup. Published every Sunday, it shares the top stories, playbooks, data and quick hits from the world of Marketing with AI over the last 7 days.
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For two years the question about AI in marketing has been what you can make with it.
This week, three different forces converged on a different question: whether you have to tell people you used it.
On Thursday, LinkedIn gave every member a button to report a post as AI slop, killed its own AI writing feature, and started building a test that will tell you privately when your audience thinks your posts read like a machine wrote them.
On Tuesday, Google shipped AI content labels across its ad platforms.
And on Sunday, the day this lands in your inbox, the European Union’s AI transparency rules become legally enforceable.
Those are not three coincidences.
A regulator, a platform and an audience arrived at the same demand in the same week, which is worth paying attention to even if you sell nowhere near Europe.
Elsewhere, the access question moved.
ChatGPT’s agents can now log into the tools you use and stay logged in, which quietly removes the wall that stopped most useful marketing automation at the sign-in page.
Voice stopped being dictation and became a way to actually move work. A
nd three separate pieces of research landed on the same uncomfortable finding: almost everyone has AI in production, and almost nobody has the plumbing to support it.
Below: four stories to act on, two workflows worth stealing, and the clearest data yet on why your launch dates have not improved.
Let’s get into it…
🔥 Top Stories
1. LinkedIn hands every member a button to report AI slop, and kills its own AI writing tool
On 30 July, LinkedIn’s Chief Product Officer for Ecosystem, Hari Srinivasan, announced six changes aimed at what the company calls AI slop, which it defines as low-effort AI-generated content that sounds polished but carries no real perspective or substance. The headline change is a “Seems like AI slop” option in the three-dots menu on any feed post. Selecting it hides the post from you and feeds LinkedIn’s classification models. Alongside it, LinkedIn is deploying new classifiers that identify AI-slop and low-quality posts and reduce their reach in suggested content and in content shown outside your network. It says it now catches hundreds of thousands of automated comment attempts every day.
Two further changes matter more than the button. LinkedIn is testing a feature that will privately flag in your own analytics dashboard when members feel your post came across as inauthentic or heavily AI-written. And it is removing the “enhance your post” AI writing feature entirely, replacing it with a proofreader that fixes grammar and spelling without rewriting your words into a generic AI voice.
Why it matters / what to do: LinkedIn is the single most important organic channel for most B2B marketers, and it has just turned “does this read like AI wrote it” into both a ranking signal and a button in the hands of your readers. The author-side flag is the part with teeth, because for the first time you will be told, in your own numbers, that your audience does not believe you wrote your own posts. Action: stop using any tool that produces a finished LinkedIn post end to end, and move to a process where AI generates raw material and you supply the specific detail, the numbers and the first-hand experience a model cannot invent. The irony worth noticing is that LinkedIn has reached the same conclusion about its own product, which is why the writing feature is being deleted rather than improved. Two caveats: the button is not on every account yet, and 404 Media’s screenshot shows the option also appearing on sponsored posts, sitting between “Hide this ad” and “Report ad”, with LinkedIn so far saying nothing about how that signal is treated on paid placements.
Read more: https://techcrunch.com/2026/07/30/linkedin-adds-a-button-to-report-ai-generated-slop/
2. Google ships AI content labels five days before the EU deadline that explains them
On 28 July, Google began rolling out AI content labels in Asset Studio, letting advertisers disclose when an image or video asset was generated or modified with AI. The setting spans Google Ads, Display & Video 360, Campaign Manager 360, Merchant Center and Google Ads Editor. You switch on a new AI label setting and can then apply text or visual labels to eligible creative manually, and Google says it may also apply labels automatically to certain assets made with its own AI tools. Related plumbing arrived the same week, with a synthetic content attestation field added to the Display & Video 360 API on 23 July and AI content labelling added to both the Campaign Manager 360 and Display & Video 360 APIs on 28 July.
The reason this shipped now sits in the calendar. Article 50 of the EU AI Act becomes legally enforceable on 2 August 2026, the day this newsletter reaches you. Two of its four transparency duties land directly on marketing teams. Anyone publishing AI-generated or AI-manipulated images, audio or video that realistically depicts real people, places or events must disclose that it was artificially generated. And AI-generated text published to inform the public on matters of public interest must be labelled, unless a named person or company took meaningful human editorial responsibility for it. Law firms spent last week telling advertising and PR teams specifically that this reaches product shots, AI-generated backgrounds, synthetic characters, press releases and ad copy. Disclosures must be clear, distinguishable, accessible and delivered no later than first exposure. Fines reach 15 million euro or 3% of worldwide annual turnover.
Why it matters / what to do: the obligation is legal but the switch is now sitting inside the ad platform you already use, which makes this unusually easy to act on. Action: this week, open Asset Studio, turn the AI label setting on, and audit which of your live image and video assets were generated or touched by AI. Then write down, in one line, who at your company takes editorial responsibility for AI-assisted published copy, because that named responsibility is the exemption that removes the text-labelling obligation entirely. Most B2B teams already qualify and simply have not documented it. Three details worth holding onto: content both generated and published before 2 August needs no retroactive labelling, text drafted in July but published in August is caught because the trigger is the publication date, and Google itself explicitly warns that using its AI label setting does not guarantee compliance with local regulations. The visual obligation has no editorial-responsibility exemption.
3. ChatGPT agents can now log into your tools and stay logged in
Announced on 25 July by OpenAI, a ChatGPT Work agent can now reach websites that require an account. The mechanism is a handover: you take over the agent’s cloud browser, sign in yourself, then hand control back and the agent continues the job. Screenshots are disabled during the takeover and OpenAI says it never sees your password. Cookies persist afterwards, so you sign in once per site rather than every session, and the login steps do not count against your message limits. It is available on Pro, Plus, Business, Enterprise and Edu plans.
Why it matters / what to do: this is less exciting than it sounds and more consequential than it looks. Almost everything a marketer would genuinely want an agent to do sits behind a login: Google Ads, GA4, LinkedIn Campaign Manager, your CMS, your email platform, your CRM. Until now the agent walked confidently up to the sign-in page and stopped, which is why so many agent demos quietly use toy examples. That wall is now gone. Action: pick one recurring, read-only job that lives behind a login, such as pulling last week’s LinkedIn Ads numbers or checking your CMS publishing queue, run it once with the takeover, and see whether it repeats unattended next week. Do not give an agent write access to a live ad account on the first run. Two caveats worth weighing: this is sourced to OpenAI’s developer account rather than a full product post or help-centre article, so treat the specifics as launch-note quality. And persistent cookies mean an authenticated session to your ad account is now sitting in OpenAI’s cloud browser, which is a real question to put to whoever owns security at your company before you connect anything that spends money.
Read more:
4. Voice stopped being dictation, and the most useful version landed on the Mac
On 29 July, Google added Speak to Window to the Gemini app for macOS. Long-press the Fn key and speak into any window on your desktop, then release and the text lands at your cursor. Double-tap Fn for hands-free recording on longer passages. By default it is intelligent dictation, which strips the ums and ahs and catches your mid-sentence corrections rather than transcribing them.
The part that changes the job is a toggle you have to switch on yourself. Under the Speak to Window settings there is an opt-in for Gemini reasoning. With it on, Gemini reads what is on your screen, so you can highlight a paragraph and ask for a different tone, pull specific information out of a selected document, PDF or image, or generate an image straight into whatever you are working in. Pressing both Command keys sends the whole foreground window across as context. It needs an Apple Silicon Mac, macOS Sequoia 15.0 or later, and Gemini for macOS version 1.88 or later, and Google describes it as rolling out globally in English with more languages to follow.
This caps a fortnight in which every major assistant did the same thing. Claude’s voice mode moved off Haiku on 24 July and now runs on Opus, Sonnet or Haiku, and reaches connected apps mid-conversation, with Gmail, Google Calendar, Slack, Canva and Notion named. Two days before that, OpenAI brought voice into the ChatGPT desktop app, where it can coordinate several agents at once and be driven from your phone through a feature called Remote connections. In one practitioner walkthrough this week, the Gemini hotkey is used to highlight five unopened files in a folder and turn them into a finished email without opening a single one.
Why it matters / what to do: the honest marketer’s version of this is smaller than the demos suggest, because the demos are mostly personal admin. What is genuinely new is that briefing has become the interaction. Talking through what you want is faster than typing it, and briefing is the part of the job where your judgement actually lives, which makes it a better thing to speed up than drafting. Action: pick the next piece of work you would normally write a brief for, highlight the source material on screen, and talk the brief instead. Then compare how much correcting the output needs against your usual. Three caveats. Only the Gemini release falls inside this week, with the Claude and OpenAI changes landing on 24 and 23 July respectively and included here as context for the pattern. Speak to Window needs Apple Silicon, so an Intel Mac or a Windows machine is out. And the practitioner who demonstrated it is candid that with reasoning switched on it often tries to be an assistant when you only wanted it to type, which is a real irritation rather than a small one.
Read more: https://blog.google/innovation-and-ai/products/gemini-app/speak-naturally-gemini-app-mac-os/
📋 The Playbook
1. What a real marketing agent actually is, and the ads loop one operator runs on it
This is the most useful thing published this week on agents, mostly because it starts by ruling things out. Cody Schneider runs companiesgraph.com and states on the record that this is what his company does, so treat it as an operator with a commercial interest rather than a neutral tutorial. The framework survives that.
His three tests. Something is only a marketing agent if it has all three, and if it does not, it is a linear automation wearing a costume:
1. Unified data across the whole pipeline, not a single tool’s export.
2. Autonomous decisions on a cadence, with a thinking loop that reads results back in.
3. Hosting in the cloud, not a script on your laptop.
His own words on why the third one matters, and why most “agents” fail the second: “I want something that is doing a process for me and then looking at the data and then basically improving based off of the data that’s getting back from it.”
The loop he runs for a live client, step by step:
1. Research pain points in the buyer’s own words. He uses Perplexity to pull from Reddit, then asks it to rank the results by most referenced to get the top three pains. Reddit specifically, because it is people complaining without a filter.
2. Generate the statics. Feed an existing competitor ad in as a structural example, generate the new versions, then put a vision model over the output to check it against your brand style guide, fonts, colours and text legibility, before anything ships.
3. Generate the video. He still gets results from AI-avatar video for user-generated-content style ads, and is candid about the constraint: current clips cap out under about nine seconds, so stitching them into a 30-second ad is the actual hard part.
4. Publish through the platform API, for writes only. His correction of the common complaint about account bans is blunt: “The agent is not the reason it got banned. They violated TOS and they spammed the API.” Use the API to publish, pause and promote. Do not use it to pull hundreds of millions of rows.
5. Build the data layer. Open-source and self-hostable throughout: a pipeline tool feeding a fast warehouse, with ad platform data, analytics, product analytics, your CRM and your payment processor all landing in one place so the agent can trace an individual ad through to actual revenue.
6. Run the loop. For one client: two ad sets per day, five ads per ad set, uploaded automatically. Each gets a two to three day window for initial signal. Kill the worst, and move winners into a winners pool that competes for budget. Store the actual prompts and ad scripts in a database so the system learns which creative recipes produce outcomes rather than just which ads won.
7. Solve for entropy. This is the part almost nobody discusses. The agent gets stuck thinking the same way and creative output converges. His two fixes: pull competitor ads from the platform’s public ads library to inject new ideas, and mine YouTube and podcast transcripts in your category for angles to build ads from.
The claim worth arguing with: he says Facebook has become the best B2B ads channel that exists right now, because Meta’s ranking system reads your creative and your landing page to decide who sees the ad, so you no longer build interest-based targeting lists. You write creative that names a specific pain and the platform finds the people who have it.
Do this now: most readers will not build the warehouse, and that is fine. Three things here work without any of it. Apply his three tests to the next “AI agent” a vendor pitches you. Run the winners pool manually, because a two to three day read and a budget-competing shortlist needs no infrastructure at all. And take the entropy problem seriously, because it is the reason your AI-generated creative starts strong and gets samey by week three.
Watch more:
2. A 14-step system for updating decayed pages without destroying what already ranks
Published on Search Engine Land on 29 July by Alex Galinos, this is the rare AI-and-SEO piece that is a process rather than a tool pitch, and it targets the highest-return content work most teams skip entirely.
The problem it solves is familiar to anyone who has run a content programme for more than two years. Existing pages quietly lose rankings and revenue over time. Updating them recovers that performance far more cheaply than publishing something new. But rewriting too aggressively wipes out the equity the page had already accumulated, so the update makes things worse and nobody can explain why.
The 14 steps cover diagnosing which pages have actually decayed rather than which ones feel old, making targeted changes that preserve the existing signals, measuring whether the update worked, and then turning the whole sequence into a repeatable system driven by Claude Code rather than a one-off project. The worked example runs on a marketplace with pages spanning airports, resorts and routes across multiple countries and languages, which is a harder case than most B2B sites and makes the method easier to trust on an easier one.
Do this now: before touching anything, pull the list of pages that have lost the most traffic over the last twelve months and sort by the revenue or pipeline they used to produce rather than by traffic volume. That list is almost always shorter and more valuable than the content calendar you were about to write. This one requires Claude Code, so it sits a step above the usual entry point, but the diagnosis half works manually.
Read more: https://searchengineland.com/scale-seo-content-updates-claude-code-483862
🛠️ New Tools & Features
1. Google Search Console Platform properties, now available everywhere
On 29 July Google rolled Platform properties out to every Search Console account worldwide and published a guide to reading the data. Platform properties let you track how your posts on Instagram, TikTok, X and YouTube perform in Google Search, Discover and News, with clicks, impressions, average click-through rate and average position for each connected account. It works even if you do not own a website. For B2B teams this fills a genuine measurement gap, because it shows which social posts are earning visibility in Google rather than only on the platform where you published them. It covers Google surfaces only, so it complements your native analytics rather than replacing them. Free.
Read more: https://developers.google.com/search/blog/2026/07/platform-properties-social-video-guide
2. StackAdapt Ivy Studio
Announced 28 July. A conversational workspace inside StackAdapt’s advertising platform that combines planning, forecasting, analysis, optimisation and execution. You describe the outcome you want in plain language, and agents grounded in StackAdapt’s own campaign, audience, creative and performance data surface context, identify opportunities and recommend next steps, with the marketer keeping approval. Worth noticing as a pattern rather than a product: this is the third “describe the outcome, agents propose, human approves” advertising product in six weeks. Pricing is not published.
Read more: https://www.mrweb.com/drno/news40126.htm
3. Nielsen Ad Intel AI, and you can query it from your own tools
Launched 27 July. Nielsen turned Ad Intel, its competitive advertising database covering 5.5 million brands and 4.6 million advertisers across 23 media types in more than 90 markets, from a reporting tool into something you ask questions of. It surfaces competitor creative strategies, emerging trends and spend shifts. The detail worth noting is that it can be exposed through MCP, the Model Context Protocol, which is the open standard that lets an AI assistant connect directly to an external data source, meaning your own assistant can query it inside your workflow. Enterprise-priced with no public pricing, so for most readers this matters less as a tool to buy and more as evidence that MCP access is quietly becoming a martech buying criterion.
Read more: https://agilebrandguide.com/yesterdays-marketing-technology-ai-news-july-28-2026/
4. OpenAI ships two new transcription models
Announced 29 July: gpt-transcribe for files and gpt-live-transcribe for live streams. Transcription is the unglamorous foundation under most content repurposing, turning a webinar into a blog post, a sales call into a messaging document, a podcast into a fortnight of social. Better and cheaper transcription lowers the floor on every one of those workflows. These are developer-facing models rather than a product with an interface, so the practical benefit arrives when the tools you already use adopt them.
Watch more:
📊 Research and Data
1. Most marketers cannot tell real AI capability from a sales pitch
StackAdapt and B2B agency Ledger Bennett published research on 27 July, with fieldwork by B2B research specialist NewtonX, surveying 426 marketers across the US, UK and Asia-Pacific. 77% say AI-related questions are not asked rigorously enough when evaluating a vendor. Only 23% assess AI against defined evaluation criteria. More than 60% say their RFP process cannot separate meaningful AI capability from marketing hype. The same survey found 63% cannot effectively measure cross-channel performance despite having clear targets, 76% are managing six or more platforms, and 59% still combine reporting data manually.
What to do: every martech vendor you speak to this year will claim AI capability, and by marketers’ own admission most buyers currently have no way to test the claim. That means a lot of budget is being committed on impression rather than evidence. The fix is cheap. Write a five-question AI block into your next RFP and reuse it: which model or models does this use, what happens to our data, what does the human approval step actually look like, what does it cost per action rather than per seat, and can you show us the output on our own data before we sign. Doing that once puts you ahead of roughly three-quarters of your peers. One caveat to hold: StackAdapt is itself an AI advertising platform, so this is a vendor publishing research about how difficult it is to evaluate vendors.
2. AI made the drafting faster, and your launch dates did not move
Knak published “Marketing Production in the Age of AI” on 28 July, surveying more than 300 enterprise marketing leaders. 85% missed at least one planned campaign launch date in the past twelve months because of workflow constraints, and 1 in 10 miss more than five a year. 70% have deployed AI in production, but 88% say the output still needs moderate to substantial human editing. The revealing split is where AI is actually being used: 64% for first-draft copy and 56% for image generation, but only 25% for building and coding the emails and landing pages, which is where the hours genuinely go. On process, 60% of campaigns involve four or more people, 54% juggle three to five separate tools, and 69% need two to three rounds of revisions.
What to do: this is a budget-direction finding, and it points away from where most teams are spending. The instinctive answer to “we are too slow” is to buy another AI writing tool, and this data says drafting was never the constraint, so that purchase will not move a single launch date. Handoffs, reviews and rebuilds are the constraint. Action: time one campaign end to end this month and record where the hours actually land, then redirect the next automation effort at whatever that measurement tells you rather than at whatever is easiest to automate. It also gives you a straight answer for the stakeholder asking why AI has not made the team faster yet. Caveat: Knak sells a marketing production platform, so the finding that production is the bottleneck is directly convenient for them, and the sample is enterprise.
💡 Quick Hits
1. Google Ads is switching to passkeys on 5 August.
From that date, new sign-ins that generate OAuth refresh tokens for the Google Ads API, Google Ads Editor and Google Ads scripts will require passkey authentication.
2. Google indexed people’s shared Claude conversations.
Shared Claude chats began appearing in Google Search on 28 July. The cause was a robots.txt block sitting in front of a noindex header, so Google never crawled the pages to see the noindex and indexed them from links instead. A painful reminder that disallow and noindex are not the same instruction.
3. Gemini Spark can now drive Chrome on your own machine, and most of you cannot use it.
Announced 30 July, Google’s background agent now controls desktop Chrome using your logged-in accounts and saved passwords rather than a remote browser, handing sensitive actions such as payments back to you. Google AI Pro allows up to 20 multi-step Chrome tasks a day, Ultra up to 200. The catch is geography, and it is worth stating plainly: Google’s own documentation says Spark is unavailable in the European Economic Area, the United Kingdom, Switzerland and Nigeria regardless of plan, and the Chrome integration is United States only at launch.
Read more: https://blog.google/innovation-and-ai/products/gemini-app/gemini-spark-updates-july-2026/
4. Anthropic shipped the biggest MCP update since launch.
The 28 July specification makes the protocol stateless so servers can run on serverless and edge infrastructure, standardises interactive interfaces rendered inside a Claude conversation, adds long-running tasks that do not block the chat, and hardens authorisation for enterprise identity providers. Claude now lists more than 950 connectors.
Read more: https://claude.com/blog/bringing-mcp-2026-07-28-to-claude
5. ChatGPT Atlas stops working on 9 August.
OpenAI is deprecating its standalone agentic browser and folding the capabilities into ChatGPT and Codex. Hard cutoff, no extension announced.
Read more: https://help.openai.com/en/articles/6825453-chatgpt-release-notes
6. Independent testing of Claude Opus 5 has started arriving, and it is mixed.
Artificial Analysis ranks it first of 170 models on its Intelligence Index, and ARC Prize independently published 30.16% on ARC-AGI-3. But a separate evaluation that feeds requirements in sequential checkpoints rather than handing over the whole specification up front returned a 24% strict pass rate with quality warnings on 93% of the output, which is a notable gap from the launch benchmarks Anthropic published itself.
Read more: https://www.ayautomate.com/blog/claude-opus-5-benchmarks
7. You have until Tuesday to try Google’s video model for nothing.
Google announced on its Gemini account on 29 July that you can create ten videos at no cost until 11:59pm Pacific on 4 August, through Create video in the tools menu. Two details decide whether this applies to you: the offer is for users without a Google AI subscription, so paying subscribers are not the target, and while uploading your own footage to edit is blocked in the EEA, Switzerland, the UK and some US states, generating from a text prompt is not. Ten in total rather than ten a day, and 18 or over.
Read more:
8. OpenAI cut the price of its cheap models by up to 80%.
From 30 July, GPT-5.6 Luna dropped 80% to $0.20 in and $1.20 out per million tokens, and GPT-5.6 Terra dropped 20% to $2 and $12. Sol is unchanged but gains a Fast Mode running up to 2.5 times quicker at double the price. Worth being precise, because the coverage has not been: this is an API price cut. Inside Codex and ChatGPT Work nothing gets cheaper, the usage is simply counted more favourably, so the same subscription stretches further. The practical effect is that high-volume, low-judgement jobs like classifying inbound enquiries or tagging survey verbatims became roughly five times cheaper to run at scale.
Read more: https://openai.com/index/advancing-the-price-performance-frontier-with-gpt-5-6/
9. ChatGPT Ads grew up, and most B2B advertisers still cannot use it.
Announced 24 July and live from 27 July: a Conversions objective that creates conversion-optimised cost-per-click campaigns, geographic exclusions, automatic advanced matching using hashed customer data, AppsFlyer and Adjust integrations, bulk operations in the API, and daily budgets becoming an average across a rolling seven-day period. The catch sits in OpenAI’s own ad policies, which limit the test period to consumer verticals such as lifestyle and household goods, local services, travel and digital products, allow financial services, healthcare and legal case by case, and state that all other categories are disallowed at launch. B2B software and professional services are not on the list.
👀 On Our Radar
OpenAI appears to be building ads whose destination is a chatbot rather than a landing page.
Search Engine Land reported on 31 July, from observations inside ChatGPT Ads Manager, on a format under test that replaces the landing page entirely with a business-specific AI agent. The system automatically profiles your business by crawling your website, you configure the agent with custom instructions and data sources, and the campaign sends people into a conversation with that agent rather than to a web destination. The agent can answer questions, make recommendations, schedule appointments and qualify leads before anyone reaches your site. Search Engine Roundtable separately noted the same day that ChatGPT Ads Manager is testing “Agent” campaign types, which is consistent.
Treat this as unconfirmed. OpenAI has not announced it, it appears limited to selected advertisers, and nobody has observed the end-user experience, so there is no way to judge how prominent these would be. It is worth watching because of what it would change rather than what it is today. Every conversion habit most marketers have was built around a page you control, optimise and measure. If the destination becomes a conversation, the asset you are optimising stops being a layout and starts being a set of instructions and a data source, which is a genuinely different job and one almost nobody has practised.
Tell me: What’s your biggest takeaway from this weeks news from the world of AI Marketing? Love to hear your thougths in the comments.
Thanks for reading the Marketing with AI Weekly Roundup! If you enjoyed this read, the best compliment I could receive would be if you shared it.
My biggest takeaway is simple. AI should help us communicate our ideas, not replace the person behind them.
I use AI every day, but my experience, opinions, mistakes, successes, and voice must still be present. After helping bring more than 160 products to market, I know that faster content does not automatically mean better marketing or faster execution.
If AI makes everyone sound the same, we have missed the opportunity. The real advantage comes when technology strengthens human experience, judgment, and originality.
Use the tool. Keep the voice.
AI disclosure labels will quietly become a ranking signal in answer engines - worth watching who complies early.