AI plans often sound convincing — until you describe how they would actually work. I have been exploring AI 2040: Plan A, a new scenario from the team behind AI 2027.

The most useful idea in it is not a prediction. It is a method the authors call scenario scrutiny.

Take a proposal and turn it into a sequence of real events:

Who acts first? What incentives do they have? What could go wrong? How would the system detect failure? What happens when competitors respond? Who ultimately remains in control?

A plan can look great in a presentation and become much less convincing when every step has to survive contact with reality.

This approach is useful far beyond global AI policy. It can also help us evaluate AI products before development begins.

It is not enough to say:

“The AI will qualify leads.”

Run the full scenario:

A prospect submits a form. The system enriches the company data, evaluates fit, drafts a personalized reply, and assigns a confidence score. Low-confidence cases go to a person for review. The final decision is logged so the workflow can be evaluated and improved.

Now the important questions become visible: What data can the system trust? What counts as a qualified lead? Where is human review required? How do we know the workflow is actually working?

The deeper lesson is simple:

Do not evaluate an AI idea by how impressive it sounds. Evaluate the sequence of decisions, incentives, checks, and failure modes around it.

That is how a promising AI idea becomes a reliable system.

In the next posts, I will continue sharing practical lessons from AI 2040.

Read the original scenario: https://ai-2040.com/

I help founders and teams turn AI ideas into reliable full-stack products, automations, and Webflow-powered workflows.