Tech founder | Leveling the AI & data playing field for DTC brands

Ten years ago we were the handymen behind the scenes - helping data giants like Axiom, Neustar and Sovrn build the targeting machines that power today’s Fortune 500.I saw all the ins and outs of how these giants operate.How they turn anonymous web traffic into persistent identities, how they stitch together fragmented journeys, and how they use that data to convert, re-engage and dominate.From there, I co-founded Opensend.Now, going into 2026 I'm on a new journey... Tune in to see what happens next.

Hiring great people is hard.

Letting them actually lead is harder.

Companies will bring in incredible talent, then spend half their time telling them what to do.

It's a contradiction I see often.

We hire smart, capable people...
Only to clip their wings the moment they arrive.

And the cost isn't just money or time. 

It's missed insight. 
Missed growth. 
Ideas that never surface because someone wasn't given the space to think.

I've had to learn this the hard way.

What works for me now is simpler than I expected: 

Hire people you genuinely admire. 

Give them room to observe, learn, and form their own view. 
Align on direction, not just the next few steps.

Then step back.

If you've made the right hire, you won't need to steer every decision. 

They'll bring ideas you hadn't considered. 

Probably even show you a better path forward.

That's the point.

What's harder for you: hiring or letting go?

♻️ Share this with a founder learning to lead differently.
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Francesco Gatti

You've seen Toyota's logo thousands of times.

But did you know it spells "Toyota"?

On first look, the logo looks like just three overlapping ovals.

But there's actually a lot more going on.

The two inner ovals represent the company and the customer.
They overlap to show trust and mutual benefit.

Together, they form a "T."

The outer oval represents the world embracing Toyota.
It also creates the "O" used twice in the name.

Even the Japanese characters are woven into the design.

This wasn't just a quick rebrand.

Toyota spent half a decade building something that would:

→ Scale from a business card to a billboard
→ Be recognized instantly on a moving vehicle
→ Carry deep meaning without ever needing to explain it

And that last part is what I find most interesting, and what matters most for marketers.

The best branding doesn't need a footnote.

It communicates before people even realize they're paying attention.

This is something I think about a lot when it comes to ecommerce:
Your brand isn't your logo.

✅ It's the feeling someone gets the moment they land on your site.
✅ It's whether they trust you enough to give you their email.
✅ It's the reason they come back after the first purchase.

Toyota understood that a logo is just the surface.

The real work is building something people recognize and trust, even when they can't explain why.

That's branding. 
And it applies to a lot more than logos.

What's a brand that earned your trust before you could explain why?

♻️ Share this with a marketer or founder who cares about the details.
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Getting comfortable isn't the reward for hard work.

It's often the end of it.

Think about it:

Anyone who gets stuck in life usually isn't someone who failed big. 
It's someone who stopped trying altogether.

Maybe they found something that works (a role, a routine, a version of themselves) and they held onto it. 

Even when it stopped fitting.

I understand the instinct. 

When you've worked hard to get somewhere, the natural response is to protect it. 
To maintain what's working rather than risk losing it.

But that's where things quietly start to calcify.

I've felt this myself. 

There have been stretches where I realized I was coasting. 
Not failing, just... not pushing. Going through the motions. 

It's a subtle shift, but you notice it eventually.

The questions you ask yourself get smaller. 

You start optimizing for comfort instead of progress.

And the tricky part is, it doesn't feel like a problem at first. 
It feels like stability.

But stability and stagnation can look identical from the inside.

The people I admire most seem to understand this. 
They're not reckless, but they don't let comfort make their decisions either.

They keep asking themselves: what am I avoiding? What have I outgrown? Where have I settled?

Those questions aren't comfortable. 
But they're the ones that keep you moving.

Growth doesn't require blowing everything up. 
But it does require honesty about where you've stopped.

Where have you been coasting lately?

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Leveling the data playing field for DTC brands | CEO & Co-Founder at Opensend

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The fastest way to keep your brain young?

Be willing to feel stupid again.

Great visual from Gareth Lloyd, thank you.

People treat learning like something with an expiration date.

You finish school, land a role, figure out how things work...

And then you just coast through life.

Life gets busy. Comfort sets in.
But your brain doesn't coast with you.

It's either building new connections or losing them.
There's no neutral gear.

Take a look at the image below.

One brain is active, lit up, forming pathways.
The other is dimming.

You might think the difference is age.

But it's actually whether you're still asking your mind to stretch.

When you learn something unfamiliar, you build what researchers call cognitive reserve.

Think of it as a buffer.

It protects against mental decline, against that foggy feeling that creeps in when you've been running on autopilot too long.

And it goes beyond cognition.

Staying curious seems to affect how we handle stress, regulate mood, even maintain physical health.

People who keep learning tend to stay sharper longer.
This doesn't mean going back to school.

It can look like:

→ Picking up a hobby you've never tried
→ Reading outside your usual lane
→ Learning a few phrases in a new language
→ Taking a short course on something unrelated to work
→ Having real conversations with people who see things differently

None of these requires a major time investment.
It just requires being okay with not knowing.

Small doses of learning add up over time.
But so does stagnation.

What's one thing you could explore this month that you genuinely know nothing about?

It doesn't have to be big.
It just has to be new.

♻️ Share this to inspire someone in your network.
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Perfect plans don't build companies.

Imperfect action does.

Early in my career, I used to think the right path would reveal itself if I just planned a little more.

Researched a little deeper, considered one more scenario...

But it never worked that way.

What I've learned is that planning often masquerades as progress. 

It feels productive, as you're technically "working on it." 
But really, you're just delaying the discomfort of putting something into the world.

Years ago, I spent weeks mapping out a product launch.
By the time I was ready, the window had closed.

Someone else shipped something rougher, faster, and learned more in two weeks than I had in two months of preparation.

That changed how I think about execution:

Action creates feedback.
Feedback creates direction.
Direction sharpens the next move.

It's a loop that only starts when you take action.

I'm not saying strategy doesn't matter (it does). 
But the balance most founders get wrong isn't too little planning.

It's too much.

You'll never feel fully ready. 
The information you're waiting for usually arrives AFTER you start, not before.

So if you've been sitting on something (an idea, a decision, a conversation you've been avoiding), this is the nudge.

Start before you're ready. 
Adjust as you go.

That's where you'll find momentum.

What have you been overthinking?

♻️ Share this to help a founder stuck in their own head.
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I've spent the last year going through dozens of AI courses.

Most weren't worth time... apart from these 8:

Great resource from Paul Evans!

These have changed how I think about what's happening in this space.

If you're still unsure where to start, courses are one of the most efficient ways to build real understanding.

Not just how to prompt a tool, but how these systems actually work.
 
The hardest part isn't learning, but knowing what's worth learning in the first place.

So I thought I'd share the resources that helped me the most:
(all free, all from MIT)

1️⃣ AI 101
↳ The fundamentals. Start here if you're building from scratch.
↳ https://lnkd.in/dZ5KYWFB

2️⃣ Introduction to Deep Learning
↳ Focused on building real neural networks. Application over theory.
↳ https://lnkd.in/dCkVRQAD

3️⃣ Artificial Intelligence
↳ How machines reason and make decisions. Still foundational.
↳ https://lnkd.in/dU__VhzS

4️⃣ Introduction to Machine Learning
↳ The core algorithms powering modern AI. Dense, but worth it.
↳ https://lnkd.in/dU7vsYmm

5️⃣ How to AI (Almost) Anything
↳ Where creativity meets computation. Useful for design and art.
↳ https://lnkd.in/da2cjPKU

6️⃣ Understanding the World Through Data
↳ Learn to read data like a language. Essential for strategy.
↳ https://lnkd.in/dTy43iGU

7️⃣ AI in K-12 Education
↳ How AI is reshaping how the next generation learns.
↳ https://lnkd.in/dyCsmcUc

8️⃣ Introduction to Algorithms
↳ The logic underneath everything else. What makes AI fast and scalable.
↳ https://lnkd.in/dSyeNwAu

What these courses taught me is that understanding AI strategy is no longer optional.

Now, the question is how it intersects with your business, positioning and growth.

Start with the basics. 
Learn from credible sources. 
Then integrate what you learn into your own systems.

♻️ Share this to help someone starting with AI.
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After nearly three years building Opensend from zero to an 8-figure ARR business, bootstrapped with no institutional capital and facing stiff competition, my time as an executive there has come to an end, though I remain on the Board of Directors.

It's been one of the most intense and formative experiences of my professional life, and I'm proud of what we built, grounded on the conviction that we could grow something that mattered. We earned the trust of thousands of the most amazing brands in the US.

To the team, my beloved agency partners, and the customers: you made it real, and my gratitude is infinite. 

What's next for me is something new and very exciting. An AI-first company. I'll be sharing more about soon!!!

Onwards. Full steam ahead.

I studied 8 dimensions where e-commerce SEO has completely broken down.

(Here's what replaced it — with verified data 👇)

Most e-commerce teams are still running a 2020 playbook in a 2026 world.
The shift from SEO to GEO isn't gradual. It's structural.

I mapped 8 dimensions where the shift has already happened,

Old → New:

01 → Discovery Channel
Google dominated 90%+ of product discovery.
Now? AI traffic to retail sites surged 760% in Q4 2025 (Adobe).

02 → Success Metric
Organic CTR dropped 34–61% with AI Overviews.
But AI referral traffic converts at 23× higher rates.

03 → Product Pages
Less than 10% of catalogs are LLM-optimised.
Without schema and structured data, AI can't recommend you.

04 → Authority Signals
Backlinks: 0.218 correlation with AI citations.
Brand mentions: 0.664 — 3× stronger than links.

05 → Content Strategy
Generic content gets filtered out by LLMs.
44.2% of all citations come from page intros alone.

06 → Purchase Journey
Traditional: 5–7 touchpoints before buying.
Now: 48% of AI shoppers let AI buy autonomously.

07 → Competitive Intel
GSC tracked rankings in a stable system.
AI gives a different recommendation list almost every time.

08 → Technical Stack
Old SEO relied on metadata and sitemaps.
GEO requires live product API endpoints (ACP + UCP).
The brands that win won't be the ones ranking highest.

The e-commerce brands that win won't be the ones ranking highest on Google.
They'll be the ones AI recommends.

P.S. Which shift surprised you the most?

♻️ Repost if your network needs to see this.
Follow me, Francesco Gatti, for more on agentic commerce and AI visibility. Opus 4.6

I used to think working harder was the answer.
 
It took me a while to realise I had it backwards:

I've noticed a pattern in myself and in other founders I talk to:

When everything feels urgent, we default to the small stuff. The easy wins. 

But this is just overwhelm disguising itself as busyness.

The real issue is clarity.
And clarity starts with prioritization, not delegation.

A lot of people jump straight to "I need to delegate more" when they're drowning. 

But you can't hand off what you haven't defined yet. 
Delegation comes after you've figured out what actually deserves your focus.

This is what took me a while to accept:

Even when 50 things feel equally important, you still have to pick one.
And sometimes you'll pick the wrong one. That's okay.

Committing to one thing, even if it turns out to be the wrong thing, will move you further than staying frozen because you can't decide.

When I feel that overwhelm creeping in, a few things help me reset:

1️⃣ I try stepping back before speeding up
↳ Doing nothing for a few minutes often helps me see things clearer.

2️⃣ I remind myself that most "urgent" things are just loud
↳ They're rarely what actually matters.

3️⃣ I force myself to commit to one priority
↳ Not five or three. Just one. I can always adjust later, but I can't iterate on inaction.

And the more I build, the more I realize...

Knowing what to ignore is just as important as knowing what to do.

What's one thing you could let go of this week to make room for what actually matters?

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You don't need to be technical to use AI.

You just need to unlearn these 9 things first:

There are a lot of opinions around AI right now.

And buried under those opinions are beliefs that sound reasonable but actually hold people back.

I've watched founders delay adoption because they thought they needed technical skills. 

Teams avoid AI because they feared it would replace them. 
Leaders wait for "perfect data" that never comes.

These myths cost more than people realize: time, money, and ground lost to competitors who didn't hesitate.

I believed a few of these myself early on. 
Especially the one about needing everything in order before starting.

But (as always), action teaches faster than preparation.

Here are 9 AI myths I see holding people back (and the reality behind each):

❌ You need to be technical to use AI
✅ You need to understand the problem, not the code. Ask the right questions and you'll get far.

❌ AI will replace my team
✅ The teams that win use AI to eliminate busywork so humans can focus on strategy, creativity, and relationships.

❌ AI is too expensive for small businesses
✅ What's actually expensive is doing manually what AI handles in seconds. Time is the real cost.

❌ You need perfect data before you start
✅ Waiting for perfect data means waiting forever. Start small, test fast, improve as you go.

❌ AI is only for tech companies
✅ If you serve customers or make decisions, AI applies to you. It's not niche. It's universal.

❌ AI will solve all my problems 
✅ AI is a tool, not a miracle. It amplifies good strategy and exposes broken process. 

❌ I can wait and adopt later
✅ Your competitors aren't waiting. Every month you delay, the gap grows. 

❌ AI outputs are always accurate
✅ AI hallucinates often. The skill is knowing how to prompt, verify, and treat AI as a collaborator.

❌ Understanding AI takes months of study
✅ You can capture 80% of the value in a week. Watch a few talks, test a few tools, apply AI to real work.

Ultimately, the goal isn't to convince you AI is magic.

It's to help you think more clearly so you can move more effectively.

♻️ Share this with someone still on the fence about AI.
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12 AI Myths Preventing You From Success.

SEO is not dead.

But if it's all you're doing, you're playing one instrument in a three-piece band.

Here's the difference between SEO, AEO, and GEO - and why D2C brands need all three in 2026.

(1) SEO - Search Engine Optimization 
→ You already know this one. Rank high-intent PDPs and category pages on Google. Build topical authority through ingredient guides, founder stories, and comparison content.
→ Own the "best [product] for [concern]" queries that precede purchase.
→ Still the foundation. It drives compounding traffic with high purchase intent and fuels repeat-purchase discovery organically.

(2) AEO - Answer Engine Optimization
Get chosen by AI Overviews, People Also Ask, and voice search.
This is about structuring your content so AI engines surface your brand as the answer, not just a result.
The key plays for D2C:
→ Add FAQ schema to ingredient and formulation pages
→ Answer buyer questions directly: "Is niacinamide safe during pregnancy?" "How much protein per day?"
→ Structure short answers for zero-click visibility on category queries

(3) GEO - Generative Engine Optimization
Get cited, recommended, and sold by AI agents. This is the new frontier.
GEO is about building entity presence on the sources LLMs trust most, so when a shopper asks an AI to find the best product, your brand shows up.
The key plays for D2C:
→ Write entity-rich product descriptions LLMs can parse and cite
→ Seed presence on Reddit (r/SkincareAddiction, r/Supplements), Healthline, Byrdie, and Wirecutter-style review sites
→ Get cited in DTC newsletters and aggregator roundups AI trusts

Here's the uncomfortable truth:
You can rank #1 on Google and still be completely invisible to ChatGPT.

I've seen it with D2C brands spending $15K+ per month on SEO. Page 1 for dozens of high-intent keywords. Great reviews. Strong formulations.

But ask an AI agent to recommend the best collagen supplement or creatine for women?

Their competitors show up. They don't.

The fix isn't choosing one over the other.

The fix is the D2C Priority Roadmap:

SEO First → Product schema, site speed, PDP keyword targeting. Capture buyers at the "best [product] for [need]" moment. Underpins everything.
AEO Layer → Win AI Overviews and PAA with ingredient FAQs. Own the zero-click layer as buyers research formulas, safety, and comparisons.
GEO Now → Seed Reddit, review sites, and trusted DTC publications. Become the brand AI recommends when shoppers ask agents to "find the best."

Most D2C brands are still running single-player SEO in a three-layer game.

The brands winning in 2026 are running all three simultaneously.

I've watched dozens of AI talks over the years.

These 7 are the ones I keep recommending.

The resources out there are endless.
Courses, newsletters, YouTube rabbit holes, Twitter threads...

It's easy to spend more time searching than learning.
So I put together a shortcut.

These are 7 TED talks I keep coming back to, one for each day of the week.

Disclaimer: they won't make you a machine learning engineer.

But they'll give you the mental models to think more clearly about where AI is heading and what it means for your work.

Day 1️⃣: The Future of AI and What It Means for You — Yann LeCun
↳ Foundational principles from a Turing Award winner.

Day 2️⃣: How AI Can Save Our Humanity — Kai-Fu Lee
↳ What AI will change about work, and what it can't replace.

Day 3️⃣: The Incredible Inventions of Intuitive AI — Maurice Conti
↳ How AI augments creativity and design thinking.

Day 4️⃣: What Happens When Our Computers Get Smarter Than We Are? — Nick Bostrom
↳ The ethical and existential questions we need to sit with.

Day 5️⃣: Can We Build AI Without Losing Control Over It? — Sam Harris
↳ Why the alignment problem matters now, not later.

Day 6️⃣: The Wonderful and Terrifying Implications of Computers That Can Learn — Jeremy Howard
↳ A clear breakdown of how deep learning actually works.

Day 7️⃣: How AI Can Bring on a Second Industrial Revolution — Kevin Kelly
↳ Where the opportunities are as industries shift.

One talk a day. Around 15 minutes each.

By the end of the week, you'll have a much clearer understanding on AI.
Not just the surface stuff, but the substance underneath.

Which one are you starting with?

♻️ Share this to help someone trying to make sense of AI right now.
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Sue G.

Everyone thinks AI governance is a future problem.

But it's already happening:

"AI governance" sounds like a problem for legal departments.
In reality, it's mostly just... good operations.

The term carries a lot of weight.

It conjures up images of regulations, audits, and dense policy documents that no one reads.

And because of that, a lot of teams freeze up.

They assume governance is something they'll "figure out later".
Once they're bigger, more resourced, or forced to care.

But I've noticed that most teams are already doing AI governance.

They just don't call it that.

If you've ever asked: "Who has access to this tool?"
That's governance.

If you've set guidelines for how your team uses customer data with AI?
Governance.

If you've documented which tools you're using and why?
Governance.

The gap is simply perception, not capability.

We've turned something practical into something intimidating.
And that perception gap is causing real paralysis.

Teams delay decisions.
They avoid putting structure around AI use.
And that lack of clarity slows everything down.

My take: governance isn't a checkbox for compliance.

It's the foundation for scaling AI responsibly.

Ultimately, it's how you move faster without creating chaos.

♻️ Share this to help a founder navigating AI adoption.
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20 AI terms every ecommerce brand needs to know in 2026.

Most of them didn't exist two years ago.

Your CMO is hearing "agentic commerce" in board meetings.
Your Head of Digital is getting questions about "GEO."
Your SEO team is Googling "AI Share of Voice."

And nobody has a shared definition.

That's a problem. Because when your team doesn't speak the same language, you can't build the same strategy.

So I mapped out the 20 terms that matter most right now, organized into three layers:

→ Agentic Commerce and Protocols (UCP, MCP, A2A, and more)
→ AI Visibility and Search (GEO, AEO, AI-SOV, Citation Frequency)
→ Content and Technical (Schema Markup, Chunking, SSR, Catalog Enrichment)

Here's why this matters:

AI agents are already researching, comparing, and completing purchases on behalf of shoppers. The brands that win are the ones whose teams understand the infrastructure behind that shift.

Not just the buzzwords. The actual mechanics.

A few that trip people up the most:

UCP (Universal Commerce Protocol) is how AI agents query live product data across stores. Think of it as the API layer between your catalog and every AI shopping assistant.

AI-SOV (AI Share of Voice) measures how often AI mentions your brand versus competitors. It's the new market share metric most teams aren't tracking yet.

Zero-Click Commerce means a purchase completed entirely inside a chat interface. The shopper never visits your site. That changes everything about how you think about PDPs and conversion.

Save this as a reference for your next strategy meeting.

And if a term is missing, tell me in the comments. This space moves fast.

P.S. Which of these 20 did your team only learn about this year?

♻️ Share this with an ecommerce founder who needs this vocabulary.

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Most brands are optimizing for AI search.

But ecommerce brands are playing a completely different game and most don't realize it yet.

There's no committee. No discovery call. No nurture sequence.

A shopper types "best running shoes for flat feet under $100" into ChatGPT.

AI scans your PDP, your Reddit presence, your comparison listings, your product feed.

And in that single moment, it either recommends you or it doesn't.

That's your entire window.

So what actually gets ecommerce brands recommended by AI?
→ PDPs written answer-first, with FAQs and specs AI can parse
→ Buying guides and listicles on third-party sites — brands get cited 6.5× more via 3rd-party than own domain
→ Reddit threads in your product subreddits — authentic presence, not planted links
→ A live, complete product feed refreshing every 15 minutes for agentic checkout
→ Bottom-funnel query ownership: "best [product] for [use case]", that's where 70%+ of citations happen

The KPIs shifted too.

Forget impressions and ROAS as your north star. The metrics that matter now: AI revenue, Share of AI shelf, cart adds from AI, return-adjusted margin.

(B2B has its own version of this, citations over recommendations, depth over clarity, LinkedIn over Reddit. But the ecommerce playbook is faster, higher-stakes, and less forgiving.)

The endgame for ecommerce is simple:
AI recommends you → shopper clicks → checkout done.

One query. One shot. No second chances.

Is your catalog structured for the AI that's already recommending your competitors?

Share this with an ecommerce brand still optimizing for Google only.
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I audited how AI recommends products. 

The same 8 ranking factors keep showing up. 

Most ecommerce brands haven't optimized for a single one.

Here's what actually determines whether ChatGPT, Perplexity, or Google AI recommends your product, or your competitor's.

Factor 1: Intent Alignment

→ AI prioritizes content that directly resolves specific, conversational buyer questions, not pages built around exact-match keyword targets.

Factor 2: Entity Strength

→ AI models verify brand credibility by cross-referencing your company data across Wikidata, directories, and social platforms. 
→ Fragmented or conflicting data causes AI to lose confidence and skip recommending your products.

Factor 3: E-E-A-T Signals

→ Experience, Expertise, Authority, and Trust are the foundational filters AI uses to select reliable sources. 
→ If your product page doesn't show WHO stands behind it, AI skips you.

Factor 4: Structured Data & Schema ← start here

→ Comprehensive JSON-LD markup acts as a direct API for AI. 
→ Full Product, Review, and Organization schema allows LLMs to instantly extract exact pricing and availability.

Factor 5: Extractable Structure

→ AI engines favor content formats they can easily scrape, parse, and present in synthesized summaries. 
→ Q&A sections, comparison tables, and quick-summary blocks improve your machine-readability.

Factor 6: Factual, Conversational Tone

→ AI engines are trained to discard subjective marketing fluff in favor of objective, verifiable data points. 
→ "20% L-ascorbic acid, clinically tested for dry skin" gets cited. "Revolutionary, life-changing serum" gets ignored.

Factor 7: Topical Authority

→ Publishing isolated blog posts fails to build the comprehensive knowledge graphs that AI models look for. 
→ Centralized hub pages linked to 8–12 hyper-specific supporting articles prove you are the category expert.

Factor 8: Cross-Platform Consistency

→ AI evaluates your brand as a holistic entity across the entire internet, not just the pages on your primary domain. 
→ Synchronizing product specs, pricing, and messaging across Amazon and Google Shopping solidifies entity recognition.

The priority order:

→ Schema (+94% relevance boost) - do this week
→ E-E-A-T (2.1× citation lift) - do this month
→ Freshness (2025–2026 data wins) - make it ongoing

Then test: query your products in ChatGPT and Perplexity. Track what gets cited.

Target: 2–5x AI visibility improvement.

♻️ Repost so your ecom network sees this before their competitors do.

If your product isn't ranking in ChatGPT, you're losing money.

Here's how to fix that ↓

50 million shopping queries happen inside ChatGPT every single day.

Real buyers asking things like:
"Best running shoes for flat feet under $150"
"Gentle face serum for sensitive skin with no fragrance"
"Lightweight stroller that folds with one hand"
 
ChatGPT doesn't return 10 blue links.

It names specific products. Yours or your competitor's.

And right now, most ecommerce brands aren't even eligible to show up. 

The difference between being recommended and being invisible comes down to 5 signals:
 
1. Crawl access
→ If OAI-SearchBot is blocked in your robots.txt, ChatGPT literally cannot see your products.
→ 15-minute fix. Without it, nothing else matters.
 
2. Structured data
→ Schema markup is how AI reads your catalog — price, availability, reviews, specs.
→ Without JSON-LD, AI guesses. Competitors with clean schema win.
 
3. Third-party reviews
→ LLMs synthesise sentiment from Trustpilot, Reddit, Google Reviews.
→ Reviews are your new backlinks. One bad cluster affects every recommendation.
 
4. Content specificity
→ "Premium, high-quality, innovative" = invisible to AI.
→ Write WHO it's for + WHAT problem it solves + exact specs.
 
5. Data freshness
→ Stale pricing or out-of-stock products = deprioritised immediately.
→ Sync your product feed weekly at minimum.
 

Here's what most ecommerce brands don't realise:

Amazon blocked ALL OpenAI crawlers.
If you're selling exclusively on Amazon, you're invisible to ChatGPT entirely.
 
This is the window to get ahead, while competitors haven't figured this out yet.
 
I put together a full playbook covering everything you need:
 
What's inside:
✅ The 5 signals ChatGPT checks before recommending your product
✅ Product schema checklist — every JSON-LD field that matters
✅ Before/after product copy rewrites (generic → LLM-optimised)
✅ Authority signal strategy — reviews, Reddit, UGC, digital PR
✅ How to track AI search visibility with no paid tools
✅ 10 mistakes that silently kill your AI recommendations
✅ Full 90-day action plan — foundations → content → scale
 
Comment "playbook" and I'll send it over.
 
♻️ Repost to help an ecommerce founder in your network.
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The AI Ecommerce Readiness Checklist 

(10 questions every brand should be asking right now)

Most ecommerce brands have no idea if AI is recommending them or not.

They're optimizing for Google. Running ads. Tweaking landing pages.

Meanwhile, half of all consumers are already using AI to search for products.
And AI doesn't browse your site. It reads your data.

I put together a 10-point checklist with Francesco Gatti to help brands figure out where they stand 👇

It covers 4 key areas:

AI Visibility & Discovery
→ Have you tested what AI actually says about your brand?
→ Is your product data structured for machines, not just humans?
→ Do your product titles contain specific, entity-rich descriptions?

Content & Data Readiness
→ Are your product comparisons clear enough for AI to summarize?
→ Do you have FAQ content that directly answers buyer questions?
→ Is your catalog connected to any AI agent workflow?

Competitive Intelligence
→ Can you measure your share of voice in AI recommendations?
→ Do you know which competitors AI recommends instead of you?

Future-Proofing
→ Is your schema markup talking to AI platforms, not just Google?
→ Do you have a plan for when discovery happens outside your website?

If you answered "no" to more than 3 of these, your brand isn't behind your competitors.
It's behind the shift itself.

The good news? It's still early. Most brands haven't started.
P.S. Which of these 10 do you think most brands are getting wrong?

♻️ Repost to help an ecommerce founder in your network.
Follow Francesco Gatti for more on AI and ecommerce growth.

AI doesn't need to be evil to hurt your business.

It just needs the wrong goal.

Elon Musk said something interesting about AI:

"If AI has a goal and humanity isn't aligned with that goal, we're in trouble."

Most people hear this and think about robots and existential risk.
But there's a version of this playing out in businesses right now.

AI tools do exactly what you tell them to do. 
The problem is, what you tell them and what you actually want aren't always the same thing.

When the goal is wrong, AI can work perfectly... and still hurt you.

A few examples I've seen:

→ AI optimizing for clicks starts writing sensational headlines. 
↳ Engagement goes up, but brand trust goes down.

→ AI optimizing for conversions pushes aggressive sales tactics. 
↳ Short-term revenue spikes. Long-term customer relationships erode.

→ AI optimizing for efficiency automates customer support. 
↳ Response times improve. But the experience feels cold, and people stop coming back.

→ AI optimizing for content volume produces more posts than ever. 
↳ But they're generic, and your brand voice disappears.

In each case, the AI did its job. The issue wasn't performance; it was alignment.

This is the part most businesses skip.

They plug in AI tools without asking: what are we actually optimizing for? And is that the right thing?

Efficiency sounds good until it costs you loyalty. 
Conversion rate sounds good until it burns through trust. 
Volume sounds good until it drowns out your voice.

The AI doesn't know the difference. It just optimizes.

That's why human judgment still matters. AI amplifies your strategy, whether it's good or bad. 
It moves faster in whatever direction you point it.

So next time you're leveraging AI in your business...

Make sure you've defined the right goal for it.

♻️ Share this with someone integrating AI into their business.
Follow me, Francesco Gatti, for more on AI and ecommerce growth.

People don't buy products during sales.

They buy permission to finally say yes.

Picture this:
You've watched a product for weeks.

Then Black Friday hits, the price drops 15%, a red sale tag appears... and suddenly you buy it.

The discount wasn't even significant. 
But something shifted in how it felt.

This isn't accidental. It's psychology, applied well.

I've spent years studying why people buy. And the mechanics behind sale pricing are more interesting than most realize.

Here's what's actually happening:

1️⃣ The anchor is set

The original price creates a reference point in your mind. Once you've seen $120, anything less feels like a win, even if $102 isn't objectively cheap.

The discount doesn't need to be deep, ir just needs to feel like change.

2️⃣ Loss aversion kicks in

People buy because they don't want to miss the deal, not because they NEED the product.

The fear of losing the discounted price is stronger than the desire for the item itself. The sale tag triggers that fear.

3️⃣ Timing provides social proof

If everyone is shopping this weekend, it validates your decision to buy now. 
The collective behaviour gives individual purchases permission.

4️⃣ The excuse to buy

Some customers have been circling for weeks. They want the product but can't justify it.

A sale, any sale, gives them the story they need. "It was on offer" is easier to say than "I just wanted it."

5️⃣ Scarcity gets manufactured

Sale periods have end dates. That countdown creates urgency for products that aren't actually limited.

You're not buying because stock is low, but because the price window is closing.


Timing matters as much as the discount itself.

A 10% discount during a major sale event can outperform 25% off on a random Tuesday. 

For ecommerce brands, this means your pricing strategy isn't just about the numbers. It's about the frame around the numbers.

When you discount matters, as well as how you present it and the story the customer tells themselves.

♻️ Share this with someone running an ecommerce brand.
Follow me, Francesco Gatti, for more on ecommerce growth.

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