Today, we’re bringing you the latest in AI-powered marketing and business strategies. Here’s what’s inside:

🚨 AI Top Story: Learn why Perplexity’s new payout model could change how content earns value online.

🌟 AI Use Case Of The Week: See how Ralph Lauren’s AI stylist is boosting sales and loyalty — and how you can build a lighter version for your own brand.

🎯 Killer Marketing Prompt: Turn your top ad into five fresh variations with this ready-to-use AI prompt.

🎥 AI YouTube Resource Of The Week: How to turn product images into (animated) video ads using AI

The Start of AI Revenue Sharing Has Arrived

For years, search and AI tools have treated content as fuel without paying for the engine. That may be starting to change. Perplexity has introduced a revenue-share program that compensates publishers when their work appears in AI-generated answers, a move that could redefine how content earns value online.

The program is tied to Perplexity’s Comet Plus subscription and launches with a $42.5 million pool. Eighty percent of subscription revenue goes back to participating publishers, with payouts triggered in three ways: when users click through to a publisher’s site via Perplexity, when content is cited directly in AI answers, and when the company’s assistant uses that content to complete a task. Major outlets including TIME, Der Spiegel, Fortune, The Independent, Gannett, and Blavity are already signed up.

What makes this different is that it links payment not just to clicks, but to how content is used inside the AI layer itself. For the first time, publishers are being compensated for the role their work plays in shaping machine-generated answers. That shift challenges the assumption that AI can endlessly summarize, paraphrase, and repackage material without paying the creators behind it.

There are still plenty of open questions. How will Perplexity define a “citation” or an “assistant action”? How transparent will reporting be to publishers? And how much money will actually flow once the pool is divided? These details will decide whether this becomes a meaningful new revenue stream or a symbolic gesture.

Even so, the signal is important. AI does not create in a vacuum; it relies on the work of publishers and creators. By attaching dollars directly to citations, Perplexity is acknowledging that reality and setting a precedent that others in the industry may soon have to follow.

🛠️ OpenAI adds MCP tool support in ChatGPT for advanced developer automations - This update shows how ChatGPT is moving beyond conversation into workflow automation (worth knowing if you want to plug marketing systems directly into AI).

📊 The Marketing Metrics That Will Matter Most In The Age Of AI Agents - As AI agents take over more customer interactions, the article breaks down which performance signals still matter and which old metrics are losing relevance.

💡 Emerging technology trends brands and agencies need to know about right now - A snapshot of the tech shifts already shaping budgets and creative decisions. Useful for spotting where to focus resources in the coming months.

🌍 Google launches AI Max globally, bringing advanced AI optimization to all Search advertisers worldwide - Google’s AI Max is no longer a beta tool. Now every Search advertiser gets access, raising the bar for how campaigns are optimised and measured.

📱 Meta deepens AI focus for latest suite of brand advertiser solutions - Meta is rolling out new AI tools that change how creative is generated and targeted — practical updates for anyone running campaigns on Facebook or Instagram.

Ralph Lauren is reimagining how luxury brands connect with customers. The company has launched Ask Ralph, an AI-powered assistant trained on decades of brand knowledge, from design archives to styling advice.

The assistant lives inside Ralph Lauren’s app and functions like a digital stylist. Shoppers can ask for outfit recommendations, explore collections, and get advice tailored to their preferences. By embedding the brand’s tone, heritage, and aesthetic directly into the AI, Ralph Lauren ensures that the experience feels consistent with its identity while keeping customers inside its own ecosystem instead of bouncing to search or third-party platforms.

The business case is clear. An assistant that guides purchases in real time helps reduce returns, increases upsells, and deepens brand loyalty. It also gives Ralph Lauren valuable insights into what customers are asking for, helping inform future product decisions and campaigns.

You don’t need Ralph Lauren’s budget or decades of archival data to test this approach. Tools like Voiceflow and Cognigy make it possible to build brand-specific conversational assistants with no-code interfaces. OpenAI’s fine-tuning and RAG (retrieval-augmented generation) workflows can be layered on top to feed the assistant with your existing product catalog, FAQs, and brand guidelines.

Start with a simple use case: create an assistant that can answer top customer questions or guide product selection within one category. Train it with your product data, style guides, and tone of voice documents. Launch it in a contained environment such as your app, website, or even WhatsApp, and track metrics like time-on-site, upsell rate, or reduction in support tickets. From there, scale to broader product lines or integrate richer media like lookbooks and shoppable galleries.

Even the best-performing ads burn out fast once audiences see them too often. Instead of starting from scratch, you can use AI to refresh winning creative and squeeze more life out of it.

Here’s a customisable prompt you can drop into ChatGPT or any generative tool to generate 5 fresh variations of a proven ad:

If in doubt, get the hose out