❓ Problem

Every small business owner has now had a poke at AI. Goldman Sachs reckons 76% of them use it and 93% say it helps, which sounds like a solved problem until you read the next line: only 14% have actually wired it into how the business runs. The rest are paying $20 a month for a tool that writes LinkedIn posts nobody reads, then quietly forgetting the tab exists.

The blocker was never cost. It is translation. Just 27% of small businesses feel confident adopting AI, against 82% of mid-sized firms. A plumber, a dental practice, a three-van logistics outfit does not need a developer or a strategy deck. They need someone to look at their actual week and say "this tool, this process, start here on Monday". That gap, between owning the tools and knowing what to do with them, is where the money is.

And it is a wide gap. There are 36.2 million small businesses in the US, roughly 6.3 million of them employer firms with staff, payroll and processes worth optimising. Almost none of them have anyone whose job is to answer "what should we be doing with AI?". They are all being asked the question. Nobody is giving them the answer.

✅ Solution

A productised $999 AI assessment. Not a vague "digital transformation" engagement, a tight, repeatable product with a fixed shape: a 45-minute discovery call, a report with 3 to 7 specific tool recommendations, and a 30-minute walkthrough. In and out inside a fortnight.

The clever bit is that AI does the expensive part. This is Corey Ganim's model, which he has published in full, so we are passing on a working machine rather than a theory:

• Phase one, the discovery call. You record a 45-minute conversation with the owner using an AI note-taker like Fathom. You are mining for time drains: the repetitive admin, the copy-paste jobs, the things they hate doing on a Sunday night.
• Phase two, the analysis. You feed the transcript to Claude, which maps each pain point to specific off-the-shelf tools and estimates the hours each one claws back. The judgement that used to take a consultant a day happens in minutes.
• Phase three, the report. It assembles from a reusable template in about half an hour: an effort-versus-impact matrix, the recommended tool stack, a four-day quick-wins plan, and a financial-impact slide that puts a dollar figure on the hours saved.
• Phase four, the review call. You walk them through it live, and this is where the real business starts.

Guarantee 5+ hours back a week or a full refund, which makes the $999 feel free. Then quote the implementation work the assessment just uncovered at $3,000 to $10,000. Roughly half of clients say yes, because you have already shown them exactly what to build and why it pays for itself.

📊 Key Numbers

Top-down (the market)

• The AI consulting services market sits at around $11 billion in 2025 and is forecast to hit roughly $91 billion by 2035, a ~23% CAGR, with small and mid-sized enterprises the fastest-growing segment. Almost all of that spend today flows to firms serving enterprises, not the corner-shop economy.
• The realistic serviceable market is the ~6.3 million US employer small businesses. If even 5% of them buy a single $999 assessment over the next few years, that is a ~$315 million pool from the audit alone, before a penny of implementation revenue.
• Layer implementation on top. If a third of audited clients take a $5,000 build, the implementation market attached to that same 5% is comfortably north of $500 million. The audit is the tripwire; the builds are the business.

Bottom-up (one operator)

• Corey's own maths: 4 assessments a month at $999 is ~$48,000 a year from what he calls two-hour afternoons, before any upsell.
• Add implementation. Convert half of those 4 monthly clients into a $5,000 build and you add ~$120,000 a year. That turns a $48k side project into a ~$170k solo business.