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

🚨 AI Top Story: Why some teams gave up on AI agents, and what the successful ones did differently.

📊 One Quick AI Hack: A simple plugin that turns any webpage into a real-time research assistant.

🎯 AI Case Study Of The Week: How H&M is using AI-generated digital twins to scale content without cutting out real models.

🌟 Creator Spotlight: Artem Borysenko - Why the real edge isn’t AI vs. humans

Here’s Why Your AI Agent Couldn’t Do The Job

As we do our research on how teams are actually putting AI agents to work, we keep running into the same kind of headline: “They’re overhyped.” “They don’t deliver.” “They’re not ready for real business impact.”

But behind those headlines are businesses that landed in the “we tried it and it didn’t work” camp. Teams that expected plug-and-play magic, and gave up when things got messy. Meanwhile, others are quietly figuring it out, and are already seeing huge success. And the difference isn’t the tech - it’s the approach.

We’ve officially passed the hype cycle’s peak, and now marketers are rolling up their sleeves to confront the reality of what it takes to get AI agents functioning in the wild. It’s not just about integrating a tool. It’s about rethinking how work gets done, who does it, how decisions are made, and what systems need to talk to each other for these agents to actually deliver results. And that shift is challenging.

Early adopters are running into friction, from agents that hallucinate tasks or get stuck in endless feedback loops, to messy handoffs between humans and bots that stall progress instead of speeding it up. As Ken Rudin, SVP of data science at ThoughtSpot, put it during a recent industry panel: “The promise is there, but the orchestration is harder than we thought.”

Part of that orchestration problem stems from how we communicate with these agents. When task instructions are vague or open-ended, agents are far more likely to misfire or produce irrelevant results. They don’t thrive on guesswork - they need direction. The more detailed the brief, the better the outcome. Teams that treat prompt-writing with the same care they would when briefing a human, providing context, clarity, and expected outcomes, are seeing dramatically stronger performance.

So, if your team is in the “we tried it and it didn’t work” camp; here’s the thing - that’s not where the story ends. That’s where most people stop.

But the ones who are inevitably going to be leading the charge are the ones who pushed through that phase. They reworked their systems. They got more specific with task design. They stopped expecting instant results and started creating the conditions for these agents to actually thrive.

The companies gaining traction are the ones investing in better workflows, tighter prompt engineering, and real collaboration between marketing, ops, and IT. They’re not chasing perfection, they’re building a foundation for long-term performance.

These agents aren’t here to run the whole show - they’re here to amplify the talent you already have. The trick is getting intentional about where they fit, what they’re responsible for, and how we measure their impact.

📄 How generative AI is changing creator contracts to prevent brand and copyright risks - Brands are rewriting creator contracts to address AI-generated content risks—tightening terms around copyright, brand voice, and disclosure.

📲 Meta’s Instagram Announces New AI-Powered Tools, Creator Marketing Solutions - Meta just rolled out AI-powered creator marketing features on Instagram, including performance predictions, automated creative suggestions, & improved matchmaking between brands and creators.

💬 Now You Can Talk To Otter’s AI Agent During Meetings - Recaps, key points, and action items on the fly, right from within the platform.

🎯 The Artificially Intelligent Enterprise - Using the ChatGPT Mobile App to Fix Anything

☕ AI Tangle - Using the ChatGPT Mobile App to Fix Anything

This extension brings Perplexity’s real-time, citation-backed answers right into your browser. Highlight any text, right-click, and get instant context, summaries, or deeper insights - without having to bounce between tools or search results.

Install the Perplexity AI Chrome Extension

Click the Perplexity icon in your browser toolbar

Ask a question related to what you’re reading

It pulls real-time answers and links to sources—without needing a new tab

🎯 Great for quick research, competitive analysis, or validating stats while you’re already reading.

Speeds up research without leaving the page

Summarises articles, stats, or jargon instantly

Gives real-time info for strategy, content, or competitive intel

ARTEM BORYSENKO - Why the real edge isn’t AI vs. humans

H&M is reimagining how it creates product imagery—using AI-generated human models, also known as digital twins, to showcase clothing across its online store. These aren’t random composites or synthetic creations. Each model is based on a real person who gave full consent to have their body data and likeness used. And importantly, they’re still paid for their participation - even when they don’t physically appear at the shoot.

Using 3D body scans, facial data, and motion capture from consenting individuals, H&M generates digital twins that can be styled, posed, and “photographed” entirely in a virtual environment. The goal is to produce diverse, high-quality imagery at scale - faster and more affordably than traditional studio shoots.

The models vary in body type, skin tone, and aesthetic, giving H&M the flexibility to better reflect its customer base while keeping production agile. It’s a modern solution with a strong ethical foundation.

While still in early testing, this move is already showing potential to:

It’s a great example of how AI can improve efficiency and uphold values - something more brands will need to balance as they integrate generative tools into creative workflows.

‘Got it! Here’s the revised version of your patients Diagnosis’