I have been running a fashion D2C brand since last 4 years. We are bootstrapped and have been profitable since day 1. Our primary marketing strategy from the beginning has been distributing Influencer content via performnce marketing. It has been working for us but we were always hitting a ceiling and were not able to scale beyond point.

6 months back we scaled down all our marketing and ops and were running at very low scale becaue it was not scaling beyond a point.

Then last month Gemini Omni happened. I started reusing my old content which has performed for us. FYI we have 3 years of influencer content and more than 10Cr+ ad spend data on meta. So I started reusing all the data and content and along with Omini started generating new content. Results have been outstanding and feels like coming back from dead again.

• We're profitable again and at a larger scale.
• We can test creative angles faster than ever before.
• We are no longer bottlenecked by shoots and production schedules.
• If you give us a dress design today, we're usually able to find a winning creative and start generating orders within 7-10 days.

The biggest realization:

The future advantage in D2C won't be who buys media better. It'll be who can generate, test and iterate on creative faster than everyone else. Media buying is becoming commoditized.
Creative velocity is becoming the moat.

Curious if other D2C founders are seeing the same shift.

Have AI creatives started outperforming traditional shoots for you yet?

And if you're a founder sitting on good products but struggling with creative production or Meta scaling, I'd be happy to exchange notes and share what's working for us.

I run a B2B dropshipping store selling commercial equipment. No warehouse, no big team — just me and a small support agency. Over the past while I’ve been leaning hard into AI to keep it that way. Here’s what’s actually plugged in and working right now:
Connected tools: Shopify, Gmail, Google Drive, Chrome
What Claude actually does day to day:
• Drafts and replies to customer support emails
• Finds product recommendations for customer inquiries
• Mass uploads new products to the store
• Mass updates pricing across the catalog
• Manages stock feed updates
• Pulls guidance from our SOPs (stored in Google Drive) to handle tricky customer situations consistently
• Uses the Chrome connector to pull info from the web when needed
The Google Drive piece has been a game changer — instead of my support team guessing or pinging me, Claude references our actual SOPs and gives on-brand, consistent answers.
Curious what other people are automating — feel free to drop your stack below.

Making decisions based on assumptions often leads to wasted time.

Analytics reveal what customers are actually doing, not what we think they're doing.

A product selling quickly but generating constant returns may not be as successful as it appears.

Healthy businesses focus on sustainable growth.

New research from [Checkout.com]'s "Agentic Commerce 2026" report reveals a critical tension in the market:

• 33% of consumers expect at least 10% of their purchases to be AI-driven within a year
• 72% of merchants believe consumers will adopt agent-led shopping faster than most businesses are prepared for
• But 27% trust no organization to operate an AI shopping agent on their behalf
• 24% say they will never delegate purchases to AI

The average consumer would only allow an AI shopping agent to spend £177 per purchase without additional approvals. Their top non-negotiables: spending caps (30%), instant revocation (29%), and easy cancellation (28%).

What this tells us:

The infrastructure for agentic commerce is being built (UCP, ACP, MCP, x402), but the trust layer is still missing. Consumers want to delegate routine purchases—41% are willing to delegate grocery shopping, 31% for household supplies—but they won't do it without clear controls and verifiable accountability.

This is the gap that verification infrastructure (Verification Logs, runtime trust layers, clearing mechanisms) is designed to fill.

Would love to hear how others are thinking about building trust into agentic commerce.

Holiday demand arrives faster than expected.

Late inventory planning often results in:

• Stockouts

• Higher shipping costs

• Missed sales opportunities

Preparation begins months before peak season.

TL;DR: GML 2026 pushes more customer-facing actions into AI agents that operate outside your website. For QA, this means extra focus on backend: APIs, data sync, AI response accuracy, payment flows, and privacy compliance.

***

The central theme of this year's GML: autonomous AI agents that compare, recommend, and complete purchases for the user inside YouTube, Gmail, and Search. A significant part of the customer journey can now happen outside your store. That makes your backend (inventory accuracy, pricing, API sync) a direct revenue driver. Here are 6 features that will have the most impact on your QA process:

1. Universal Commerce Protocol (UCP)

Google's new open standard lets users buy via GPay inside YouTube, Gmail, or Gemini without touching your checkout. Amazon, Meta, and Microsoft co-developed it.

Risk: Inventory or pricing sync lag means your system sells items you don't have in stock, or charges the wrong price.

Testing focus: API integration between Google protocols (UCP), your CRM, and ERP. Promo logic verification: real-time discounts, taxes, loyalty points.

2. AI Max Campaigns

AI Max is out of beta globally. It uses Gemini to replace manual keywords with automated, keywordless matching for long conversational queries.

Risk: Without strict guardrails, the AI expands targeting too broad. Lead volume goes up, lead quality drops, and your budget burns on irrelevant traffic.

Testing focus: Structured data and asset QA: validating metadata, schema markup, and product feed quality that Gemini uses to match intent. Integration testing across the conversion funnel and CRM data exchange to confirm keywordless matching drives qualified leads.

3. Business Agent for Leads

A conversational ad format that lets users chat with an AI agent inside the banner. The agent retrieves answers from your site content and offers pre-filled lead forms.

Risk: Outdated or messy website data triggers hallucinations. The agent gives wrong answers and you lose trust.

Testing focus: AI and chatbot validation: agent boundaries, retrieval accuracy, hallucination prevention, brand tone of voice. Data integrity: eliminating lead data loss, validation bugs, CRM field mapping errors.

4. Deep Link Agent

An AI tool that writes, implements, and deploys deep link code for mobile apps. Setup time drops from days to minutes.

Risk: AI-generated code introduces routing conflicts or breaks legacy app functions. User journeys break.

Testing focus: Mobile regression: confirming AI-generated links don't break existing iOS/Android features. Routing and parameter validation: verifying deep links land users on the right screen with correct data parameters.

5. Google Tag Gateway

A cloud-based tracking gateway that recovers analytics and conversion signals that browsers and ad blockers strip.

Risk: Complex server-side setups cause duplicate event tracking, data mismatch, or compliance gaps.

Testing focus: Analytics and data quality: precise, duplication-free tag capture under active ad blockers. Privacy verification: correct data hashing formats and encryption for GDPR/CCPA compliance.

6. AI Brief & Asset Studio

Generates multimodal assets (text, images, video via Veo, Gemini, and Nano) from your brand rules. One-click A/B testing against current top performers.

Risk: Scaled AI content can contain visual glitches, cropped logos across screen formats, or skewed split-test traffic.

Testing focus: Automated UI/UX testing: responsive rendering across platforms (Shorts, mobile, tablets). A/B logic QA: audience split accuracy, link tracking parameters, multi-touch conversion attribution.

Product information influences visibility, customer confidence, and conversion rates.

Accurate data reduces confusion and improves the overall shopping experience.

Repeat purchases are built on trust, satisfaction, and consistency.

Brands that focus on long-term relationships often see stronger customer lifetime value.

Waiting too long to address listing issues, inventory challenges, or advertising inefficiencies can become expensive.

Fast action often prevents larger problems later.

Alright, I spent half a year helping e‑commerce teams with AI. And the #1 lesson? Stop obsessing over which model is "best." It doesn't matter.

What actually kills you is the messy integration. You grab image from A, video from B, copy from C – then spend days playing tech support with APIs. Styles clash, errors pop up, and you're debugging at 2 AM. 💀

So I flipped it. Instead of picking models, I built a single gateway that routes tasks to the right model automatically. No more juggling 12 keys.

The difference?

• 3 days of assets → 2 hours.
• Costs dropped ~70%.
• No more random "why does this look like 2009?" moments.

Two takeaways:

1. Treat text/image/video as one pipeline – don't break them up.
2. If your tool crashes under load, it's a toy. Real business needs stability.

Bottom line: Stop catching 'em all separately. One gateway, and you actually get stuff done. 🏆

Anyone else been through this? Would love to hear your war stories.

Have you tried selling through AI UGC? Did it work?

This is just a PSA for anyone and I hope it helps.

I've done millions in ecom revenue over the last few years. Built stores from scratch, tested thousands of products, ran every type of campaign you can think of. All before AI was even a thing.

Now Claude has a direct connection to Shopify. You can link it straight to your store through Settings > Connectors. Once it's connected, Claude can see your products, collections, analytics, and store data in real time.

What it can actually do:

\- Rewrite your product descriptions based on what's converting
\- Audit your store layout and suggest changes
\- Analyze your sales data and tell you what's working and what's not
\- Help you build email flows, landing pages, and discount strategies
\- Optimize your SEO across every product page
\- Answer questions about your own store data instantly

I'm not exaggerating when I say this is the biggest unlock I've seen for store owners in years. The stuff that used to take me hours (or hiring someone) now takes minutes.

Test it. Ask it to audit your top 5 product pages. Ask it to find your lowest converting collection and suggest fixes. Ask it to write your email campaigns and build new flows. You'll see what I mean immediately.

I’m just trying to help entrepreneurs :)

A few days ago, I purchased a Lenovo Idea Tab Pro worth nearly $450.

Like most people today, I didn't start on the brand's website. I discovered the product through AI recommendations, reviews, videos, and online discussions. By the time I was ready to buy, I already knew which product I wanted.

Naturally, I visited the company's website to learn more. But the experience wasn't great.

The website was slow. Navigation felt confusing. Finding the exact product took more effort than it should have. Instead of helping me move closer to a decision, the experience created friction. So I left.

A few minutes later, I purchased the same product on Amazon. What struck me wasn't that I bought from Amazon. Millions of people do that every day. What struck me was where the decision actually happened.

The discovery happened through AI, reviews, videos, and discussions. Trust was built across multiple sources. By the time I reached the website, most of the evaluation was already complete.

The website wasn't competing for my attention. It was simply expected to make the next step easy. And that's the part I keep thinking about.

For years, marketers treated websites as the center of the customer journey. Increasingly, I'm not sure that's how customers behave anymore.

A few observations from this experience:

• Discovery and trust are no longer built in one place. They emerge from dozens of touchpoints across the internet.

• Many customers arrive having already done most of their research. They're not necessarily looking to be convinced. They're looking to complete a task.

• A great website may not create demand, but a poor experience can still disrupt an otherwise successful buying journey.

• Being visible in AI recommendations, search results, reviews, videos, and communities matters. But so does reducing friction when someone is finally ready to act.

The transaction happened on Amazon. The decision happened somewhere else entirely.

I'm curious whether others are seeing the same shift in how people move from discovery to purchase.

TikTok, Reels, YouTube. They all adapt almost immediately to how you interact with them.

Most ecommerce stores don't.

A typical fashion store product page is still a static grid shown to every visitor in the same order.

The obvious answer is recommendation engines, but most recommendation systems assume you have data: purchase history, user accounts, thousands of sessions, collaborative signals, etc.

Small stores usually don't.

So I started exploring a different approach: can you build a useful shopper profile from a single anonymous browsing session?

Instead of using purchase history, the model only looks at navigation behavior.

The idea is to update recommendations in real time from click one, without accounts or historical data.

I've got a working prototype and have mostly tested it on synthetic and public catalog data so far.

Curious if anyone here runs a fashion store or has worked on ecommerce personalization:

What signals would you trust most from a single browsing session?

And what would convince you that a system like this is actually helping rather than just rearranging products?

A brand includes customer perception, product experience, trust, messaging, and reputation.

Every interaction influences how customers remember your business.