AI theatre rarely begins with obvious nonsense. Obvious nonsense is easy to reject. The dangerous version begins with plausible words, attractive diagrams, confident consultants, and a proposal that feels modern enough to silence the basic questions.
The first act is language.
The project is described as intelligent, autonomous, transformative, innovative, scalable, strategic, and possibly visionary, depending on how many adjectives were left unattended. The proposal avoids the dull questions because dull questions are where theatre goes to die. What exactly will the system do? What data will it use? Who reviews the output? What decision improves? What risk is introduced? What happens when it is wrong? How will success be measured?
Instead, the audience receives possibility.
Possibility is cheap. Implementation is where invoices become educational.
The second act is the demo. The demo usually works because demos are theatre with a clean shirt. The data is tidy. The scenario is controlled. The user journey is rehearsed. The awkward exceptions are not invited. The machine produces something that looks useful, the room becomes impressed, and someone says the word “potential” with the expression of a person about to spend someone else’s money.
The third act is internal momentum. Once a project has been described as strategic, it becomes awkward to ask whether it is actually necessary. People begin defending the idea before the idea has proved itself. Nobody wants to be the person who “doesn’t understand AI”. This is how many weak projects survive: not through evidence, but through social pressure.
The fourth act is the pilot. A pilot can be a good thing when it is designed to test a clear hypothesis. But an AI theatre pilot often tests nothing. It simply exists. It produces meetings, dashboards, feedback sessions, user excitement, user confusion, revised scopes, and polite emails about learnings. The organisation confuses activity with progress.
Then comes the invoice.
By this point, the project has created enough internal history to be difficult to stop. Money has been spent. People have presented slides. Senior stakeholders have nodded. The vendor has learned the company’s vocabulary. The system may not be clearly useful, but it has become organisationally real. Killing it now requires admitting that the theatre was theatre.
This is why the best time to detect AI theatre is before the proposal becomes a project.
A serious AI initiative can answer practical questions. It can explain the task, the data, the workflow, the risks, the human review, the limits, the expected benefit, and the measurement. It can distinguish AI from automation, analytics, database work, process improvement, and ordinary software. It does not need fog to survive.
AI theatre behaves differently. It hides behind generalities. It treats uncertainty as a branding opportunity. It avoids measurable claims. It offers impressive outcomes without operational detail. It uses the word “agent” as if responsibility can be summoned by costume. It assumes that the presence of AI automatically creates value.
The antidote is not hostility to AI. The antidote is adult supervision.
Ask what the system will do. Ask what problem it solves. Ask why AI is required. Ask what data it needs. Ask who checks it. Ask what happens when it fails. Ask how the result will be measured. Ask whether a simpler tool would do the job.
If the answers are weak, the project is not ready.
It may not be innovation. It may be an invoice rehearsing its entrance.
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