Hey Andrew Niepodam,

I want to show you something I have been staring at for months, because I think most people are reading it exactly backwards.

You have seen the layoff headlines. Here are the actual numbers.

AI was named in 38,579 US job cuts in May alone. That was roughly 40% of every layoff announced that month, and the highest monthly figure since Challenger, Gray and Christmas started tracking AI as a reason in 2023. Through the first five months of this year, cuts attributed to AI hit 87,714. For all of 2025 the number was 54,836. We passed last year's total before the summer.

Oracle is down around 21,000 people. Amazon cut 16,000. Meta let go of 8,000. IBM 7,800. Block cut about 4,000 and Jack Dorsey said in a shareholder letter that intelligence tools have changed what it means to run a company.

Most people read that and feel scared.

Here is what I see instead.

Those companies are not cutting because AI got cheap. AI got cheap for everybody. They are cutting because they finally worked out how to deploy it. Somebody inside those buildings mapped a workflow, built a system, proved it worked, and the headcount followed.

That deployment skill is the whole thing. And almost nobody has it.

Now look at who cannot buy it.

Your instinct might be to chase the big logos. Don't.

Around 95% of enterprise AI pilots stall before they ever reach production. Not because the technology fails. Because the organisation does. Data sits in silos behind separate access rules. Legal and compliance arrive after the architecture is already locked in. More than half of businesses name data quality as their single biggest barrier.

Then add regulation. The EU AI Act carries fines up to €35 million or 7% of global turnover, roughly double the GDPR ceiling. Every large company in Europe now has a committee whose entire job is to slow AI projects down until somebody signs off.

I have watched enterprise deals crawl for nine months and then die in a procurement review.

So where is the money?

Small and medium businesses. And the numbers here are the reason I am writing to you today.

Depending on the survey, somewhere between 55% and 89% of small businesses say they are "using AI." Sounds saturated. Now look at US Census data from May: only around 17 to 20% are actually using it in production operations. JP Morgan's research based on real transactions puts it at 17.7%.

So most of them are dabbling. Somebody in the office has a ChatGPT tab open. That is the entire adoption.

The barriers, from SBA and Goldman Sachs data:

60% say they have no internal expertise or resources to implement AI 62% do not understand how it could help them 47% say the hardest part is choosing the right tools 45% cite a lack of technical skill

And the one that defines everything: only 27% of small businesses feel confident about adopting AI effectively, compared to 82% of mid sized firms.

These businesses will never build an AI department. They cannot hire for it, because there is no job title to post and no CV to filter for. The owner cannot learn it, because he is busy running the business. And he cannot ignore it, because his competitor is quietly getting 20% cheaper to operate every quarter.

He needs somebody to walk in and do it for him.

That is the opening.

And the payoff on his side is already documented. Salesforce found 91% of small businesses using AI report revenue increases. The US Chamber found they are 2.3 times more likely to report revenue growth. So you are not selling a maybe. You are selling something with a track record, to somebody who already believes he needs it and has no idea where to start.

What they actually pay for

Not chatbots. I want to be blunt because this is where I see people waste six months.

No business is failing because it lacks a chatbot.

The work that gets paid for looks like this. Dashboards, because the owner is flying blind on his own numbers with data scattered across five tools. CRM systems, because leads leak everywhere and following up depends on whoever remembered. Custom fulfilment infrastructure, because the delivery side is held together by one exhausted operations person and a Google Sheet.

Same pattern every time. Find the process burning the most human hours per dollar of output. Find the one that breaks most often. Rebuild it so a person does not have to sit in the middle.

Notice the AI is invisible to the client in all three. They are not buying AI. They are buying "I stopped losing leads" and "I finally know my numbers."

Sell that.

Why I am telling you this now

Models get better and cheaper every few months. Deployment does not get cheaper. Every capability release makes the gap wider, because more becomes possible while the number of people who can wire it into a real business stays flat.

That gap is the margin. Right now it is growing.

But adoption among small firms went from 40% to 58% in a single year. The distance between small and large business usage is closing faster than any technology cycle before it. Eventually the tools get simple enough, or enough people learn this, and the advantage prices itself away. It always does.

I was a telecommunications engineer. Then I built landing pages. Then I went deep on AI and started implementing it inside real businesses, and those systems have driven over $2 million in online sales.

None of that happened because I waited for this to become a proper career with a proper name.

The businesses are ready. The tools are ready. The people who can connect the two are what is missing.

If you want the full breakdown of how I run this, the systems, the offers, and how to land your first client, I put together a free training here:

https://ai-integrator-os.com/free-training-video

Go watch it, then reply and tell me what you built.

Speak soon, Mike