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MIT found that 95 percent of corporate AI pilots delivered no measurable return. Gartner expects over 40 percent of agentic AI projects to be canceled by the end of 2027 - for escalating costs, unclear business value, or inadequate risk controls.
Read that list again. Not one of those three is a statement about the technology. Each is a decision somebody did not make before switching it on.
In February 2024 a payments company announced its AI assistant was doing the work of 700 people, and it handled roughly 2.3 million conversations in its first month. Fifteen months later the company was hiring humans again, and its chief executive was explaining that the AI had been cheaper and the work worse.
The same month, a tribunal ruled an airline responsible for what its chatbot told a customer. The airline had argued the chatbot was a separate legal entity, responsible for its own actions. That argument lost, and has been losing ever since.
What your agent says, you said. That is not a technical constraint - it is one you have already agreed to.
Customer support, sales and outbound, finance and admin, marketing and content, recruiting and HR, operations and scheduling. Each with the trap specific to that function, a case where it went wrong, and a full worked walk-through of all five steps on one real task.
No code, no product recommendation, no framework named in the method. The tools change every few months; the decisions do not. What is this allowed to touch? How will we know whether it worked? Who is answerable when it gets something wrong? None of those gets easier when the technology improves.
Five fill-in instruments: a nine-question task screen, a brief template with a worked example, a three-budget permission matrix, a twelve-point pre-launch checklist, and a one-page incident note - plus a chapter on surviving the sales meeting and a thirty-day plan that starts Monday.
George Chu holds a Master of Engineering in Software Engineering from Peking University and is a Senior Member of the IEEE.
