I posted a shorter version of this on LinkedIn today.

The point is simple.

If you do not know where to start with AI, pick one real use case and run it through.

The Problem With Readiness Work

I have run AI readiness sessions across four entities this year.

Frameworks, maturity grids, vendor checklists. They usually produce the same artifact: a long slide deck, a list of gaps, and a sensible set of recommendations.

That is useful work. Real work.

It is just not the work most SMBs have bandwidth for.

An SMB does not have a quarter to spend assessing itself. It has two people already wearing five hats each, a backlog that never gets smaller, and a P&L that needs the next win in weeks, not in six months.

That is not a reason to skip preparation.

It is a reason to invert the order.

Start With Friction, Not With Theory

Pick one scenario.

Quoting customers. Reconciling invoices. Routing tickets. Processing claims. Summarizing site visits. Matching records across subsidiaries.

Not the most exciting scenario. The most painful one.

The right use case usually has three characteristics.

  • Real friction
  • A real owner
  • A history of complaints long before AI entered the conversation

That last point matters.

If nobody has been annoyed by the workflow for years, the odds are good that AI is being asked to solve a problem that is still theoretical.

A boring workflow with clear ROI beats an exciting workflow with fuzzy outcomes every time.

Why One Workflow Teaches More Than an Assessment

The moment you run AI through a real process end to end, abstraction disappears.

You stop talking about readiness in general and start seeing the system as it is.

Where does the data actually live?

Who really owns the process?

What permissions are missing?

What breaks when the model is wrong?

What has to be reviewed by a human?

What governance is actually needed, not imagined?

Those questions become concrete because the system is concrete.

A deck can tell you that data quality matters. A shipped workflow shows you which field is empty, which export is manual, and which team thought the other team owned the process.

That is a much better teacher.

Preparation Still Matters

I am not arguing against preparation.

Preparing for AI is a key factor to success.

But for most SMBs, preparation is easier to do once the work has shape. Once a workflow exists, people stop debating AI in the abstract. They can see the failure modes. They can measure the gain. They know which controls matter because they know what is at risk.

The assessment gets better after the first implementation, not before it.

We did not remove discipline. We gave it a real object.

What To Measure

If you take this path, measure the workflow like an operator, not like a conference speaker.

Track cycle time.

Track rework.

Track exception rate.

Track whether the team trusts the output enough to keep using it.

Track whether the process now depends on one person who understands the prompt, or whether the team actually owns it.

The goal is not to prove that AI is magical.

The goal is to learn whether this process can be integrated, governed, and sustained.

That is the real readiness test.

The Hard Part Starts After The Demo

Most demos work.

Production is where the truth shows up.

Integration. Ownership. Monitoring. Fallback paths. Auditability. Cost control. Security boundaries. Change management.

That is where many teams discover that the hard part was never picking a model. It was deciding how the workflow would live inside the company.

The scenario matters more than the model.

A model can be changed. A broken process with no owner cannot.

Where The Gap Really Is

The gap is rarely between “no AI” and “AI demo.”

The real gap is between demo and production.

Between something clever and something owned.

Between a good answer in a meeting and a workflow the team can trust on a Tuesday morning when nobody is presenting slides.

That is the gap we close at Fardon.

If you are staring at it, we should talk.