Straight-up strategies to boost your business's bottom line. No jargon, just results.

I help Shopify apps in the age of AI — 

Nail your positioning so users don't scroll past your app. 

Land more clients, find product-market fit, optimize you onboarding and UX funnels, optimize your app store listing to attract and convert more paying clients and create revenue generating pipelines to grow ARR.

Get a free audit for your app here: https://audit.ohadmichaeli.com

Ohad Michaeli

Open any Shopify app listing.

Now scroll past the tagline. Past the description. Straight to the screenshots.

That's what merchants do. Every time.

Your feature list? They didn't read it.
Your keyword-optimized description? Skipped.
The pricing section you agonized over? They'll check later, if the screenshots don't bore them first.

I've watched merchants evaluate apps. The mouse goes straight to the image carousel.

Swipe. Swipe. Swipe. Decision made.

If your first screenshot is a settings page or a generic dashboard, you've already lost them.

They're not trying to understand your app. They're trying to feel whether it solves their problem.

Screenshots aren't illustrations of your features. They're the entire pitch.

The description exists for people who already decided to install and want to double-check. That's a small group.

What's your first screenshot showing right now?

Ohad Michaeli 🫧

Helping Shopify apps get more clients with the power of AI

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Most SaaS founders overthink pricing.

Per-product fees. Usage tiers. Feature gates. Add-on charges.

By the time a merchant finishes reading your pricing page, they're exhausted.

And exhausted people don't buy.

They bookmark. They compare. They never come back.

Complicated pricing doesn't show sophistication. It shows indecision.

You haven't figured out what your app is worth, so you built a pricing calculator instead.

Customers cluster around predictable price points. Always have. Always will.

You don't need 7 tiers. You need 2, maybe 3.

Make it inevitable for customers to move between tiers as they grow/shrink. You earn more. They don't churn when they hit hard times.

Drop the per-product pricing. One number that makes sense.

Simple pricing removes the barrier between thinking and buying.

If a 10-year-old can't explain your pricing, you're losing deals.

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Open your Shopify App Store ad campaign dashboard.

Now look at your keyword list. Actually look at it.

When was the last time you did that?

Most founders set up campaigns once. Pick some keywords. Set a budget. Walk away.

Then they wonder why installs are expensive.

Your campaign is bidding on keywords that made sense 6 months ago. Before your positioning changed. Before you knew what merchants actually search for.

You are paying for clicks from people who will never install.

Set it and forget it works for slow cookers. Not for ad campaigns.

The Shopify App Store changes. Search behavior changes. Your competitors update their keywords.

Your campaign doesn't know any of that.

I'm not saying ads don't work. They do.

But only if someone is actually managing them.

Pull up your keyword report right now. Look at what you are actually paying for.

What did you find?

I Help Corporate Professionals Gain Career Clarity & Fulfillment | Founder @Amplify Your Home | Global AI Tax Copilot Strategist @EY

Don't be fooled by my appearance. Quiet, Asian, tiny girl.

In the last decade, I've...

Successfully navigated the corporate giants: Goldman Sachs, PwC, Deloitte and EY.

Built communities, tax and financial models, and automations. (Excel wizard here)

Empowered employees to own their damn careers,

Challenge their norms to do something special.

That's when Problems become Opportunities,

Strategies into Actions.

You are not Alone.

Ready to redefine your career? Let's make it happen.

Mary Fung

Accenture and Snowflake just formed a joint business group. 5,000+ certified consultants dedicated to AI and data transformation.

That's not a partnership announcement. That's a statement about where enterprise AI is heading.

Reading between the lines:

- The big consultancies are betting hard on data infrastructure as the AI bottleneck
- 'AI transformation' is becoming 'data transformation with AI capabilities'
- If you don't have your data house in order, no amount of AI magic will save you

What this means for enterprise buyers:

The consulting giants are building massive practices around making AI actually work. They're not selling AI hype anymore - they're selling the boring stuff. Data quality. Governance. Integration. Infrastructure.

They learned something from the last 2 years: Executives buy AI demos. But they pay for data cleanup.

If Accenture is deploying 5,000 people to this, ask yourself: Is your organization investing anywhere close to proportionally?

Own Your Damn Career | Founder @Amplify Your Home | Head of Synthetic Data & Simulations @EY

10 AI Predictions for 2026:

1. Gemini will quietly become the default AI model — not because it's best, but because it's baked into Google Workspace and enterprises buy whatever comes with the contract.

2. AI agents will be the buzzword of 2026, but 90% of enterprise "agents" will just be fancy if-then workflows with a chatbot wrapper.

3. Middle management will shrink faster than anyone expected. If your job is routing information between people, start upskilling now.

4. LinkedIn will be flooded with AI slop, which will make authentic human writing easier to spot and more valuable.

5. The companies that win with AI won't have the best models — they'll have the cleanest data and fastest approval pathways.

6. Most "AI transformations" announced in 2025 will be quietly walked back, rebranded, or buried in footnotes.

7. Copilot will be on every enterprise desktop, but 80% of employees will use it the same way they use Excel — poorly and rarely.

8. The Chief AI Officer title will peak and then quietly disappear because nobody figured out what it was supposed to do.

9. Prompt engineering will stop being a job title and become a baseline skill, like knowing how to Google.

10. The people who actually ship AI in 2026 will be the same ones who shipped software in 2016 — builders who know that technology is 20% of the problem.

Everyone keeps saying, “AI needs better foundations.”

That’s true.
But it’s not the part people think.

KPMG isn’t warning about missing tech.
They’re warning about missing discipline.

Most enterprises want AI to move fast
on top of systems that were never designed to be trusted.

Bad data.
Unclear ownership.
Processes built for humans to patch gaps with judgment.

So when AI breaks, leaders don’t ask:
“Why was this workflow fragile to begin with?”

They say:
“AI isn’t ready.”

AI doesn’t fail in enterprises because it’s too powerful.
It fails because it refuses to hide the mess underneath.

Foundations aren’t glamorous.
They don’t demo well.
They don’t win internal applause.

But without them, AI just scales dysfunction faster.

The companies that actually win with AI won’t be the loudest adopters.
They’ll be the ones willing to do the unsexy work first:
clarify ownership, clean data, redesign workflows, define accountability.

Everyone else will keep running pilots —
and blaming the tech when reality shows up.

Most companies don’t hire talent.
They hire job descriptions.

And then they’re shocked when they end up with average people doing average work.

Cursor does the opposite.
They don’t ask “What role do we need?”
They ask “Is this person undeniable?”

If the answer is yes, they bend the org around them.
They don’t wait for headcount approvals.
They don’t debate leveling.
They don’t hide behind process.

They move.

Because:
> Elite talent is rarer than clean org charts.
> Process is cheap. People aren’t.

Most companies optimize for order.
The best ones tolerate chaos to secure leverage.

If you hire roles first, you get replaceable people.
If you hire people first, they create roles you didn’t know you needed.

That’s how you build an org of superstars.
Not by perfect hiring processes but by having the courage to recognize greatness
and act before it’s gone.

Goldman’s CEO said AI can now do 95% of an S-1 in minutes.

Everyone keeps asking what happens to the jobs.

That’s the wrong question.

The real question is:
What is the 5% that still matters?

It’s not formatting.
It’s not drafting.
It’s not models, footnotes, or precedent decks.

The 5% is:
• deciding what risk actually matters
• knowing what to leave out
• framing the story regulators and investors will believe
• understanding where this deal breaks under pressure
• making judgment calls no template can defend

The 95% was always execution theater.
The 5% was always the job - it was just hidden behind process and headcount.

AI didn’t remove human value.
It stripped it down to the part you can’t fake.

If your role was built on producing the 95%,
you weren’t senior...you were insulated.

Now the insulation is gone.

What’s left is judgment, taste, and accountability.
And that’s the part most people were never trained for.

That’s the real disruption.

Users don’t need Copilot to be visionary.
They need it to be boringly correct.

Once it earns trust, then it can become transformative.

But this is exactly why Copilot keeps disappointing:
the people building it aren’t the people living in Word, Excel, Outlook, and PowerPoint all day.

They don’t feel the cost of a small error.
They don’t feel the “I now have to double-check everything” tax.
So the product ships features… before it ships trust.

And Satya stepping in as PM won’t magically fix that.

This isn’t a leadership-attention problem.
It’s a proximity problem.

Until the builders are forced to live in the workflow and be judged on accuracy, not demos
Copilot will keep being impressive… and optional.

Trust first.
Then transformation.

How to Position Yourself for AI-First Roles:

> Stop learning tools. Start solving problems. Anyone can take a ChatGPT course. Few people can articulate how AI solved a specific business problem with measurable results.

> Document everything you build. That workflow you automated? That analysis you accelerated? Screenshot it. Write it up. Your future employer can't see what you don't show them.

> Get uncomfortable with ambiguity. AI roles live in the gray zone between 'definitely possible' and 'completely impossible.' If you need clear requirements to function, this isn't your path.

> Learn to speak two languages. Technical enough to evaluate AI capabilities. Business enough to translate them into ROI. The people who bridge this gap are absurdly valuable.

> Find the AI skeptics. The person in your company who thinks AI is overhyped? Befriend them. They'll show you every gap in your thinking and make your pitches bulletproof.

> Build a public track record. LinkedIn posts about AI projects. GitHub repos with experiments. A portfolio site showing what you've built. Hiring managers are tired of 'AI enthusiasts' with nothing to show.

> Apply before you're ready. The AI field moves so fast that job requirements are wishlists, not checklists. If you meet 60% of the requirements, you're qualified. They'll teach you the rest.

Most “agentic AI” projects fail for the same reason most corporate transformations fail:
people mistake motion for progress.

McKinsey looked at 50 real agentic AI builds.

The failure wasn’t the models.
It wasn’t the prompts.
It wasn’t the talent.

It was ego.

Teams built impressive agents instead of fixing broken workflows.
They layered AI on top of chaos and called it innovation.

AI isn’t limited by capability.
It’s limited by imagination.

We already have models powerful enough to solve problems most companies haven’t even defined yet.

The gap isn’t technical - it’s structural.
It’s the lack of domain context, product packaging, and change management that turns potential into adoption.

Most enterprises don’t need “better AI.”
They need AI that speaks their language - built around real workflows, real UX, and real ownership of change.

That’s why the next wave won’t come from another foundation model.
It’ll come from domain-specific agents - vertical copilots that combine deep expertise with flawless execution.

The real unlock isn’t general intelligence.
It’s the ability to pour capital into problems that were never scalable before.

And the only thing left standing in the way isn’t the tech -
it’s the willingness to use it.

5 Things I Learned Implementing AI at Enterprise Scale:

1. Your data problem is bigger than you think. Every executive says 'our data is pretty clean.' It never is. Plan for 60% of your timeline to be data work.

2. The pilot trap is real. Successful POCs die in production because nobody planned for security reviews, compliance checks, and the 47 stakeholders who need to approve everything.

3. Change management eats AI for breakfast. The technology works. The humans resist. Budget 3x more time for training, communication, and handling the 'but we've always done it this way' crowd.

4. Start with the boring use cases. Everyone wants the sexy AI chatbot. The real ROI is in automating the mind-numbing document processing that costs you 200 hours/week.

5. Your AI strategy needs an AI exit strategy. Models change. Vendors pivot. Build for portability or you'll be rewriting everything in 18 months.

i won't be surprised if this number 10x by now

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