AI and Problem Analysis

One of the strongest points I always emphasize in sales conversations is that sellers often don’t truly understand what it means to comprehend the buyer's problem. The classic recommendation is that you display an understanding of the problem when you show active listening, you summarize the problem statement, and you paraphrase what the client says, showing that you are adding your insight instead of simply repeating what the customer has said. Problem understanding equates to active listening.

Here, the recommendations for the use of AI are simple. Use AI to support active listening. Use AI to help you summarize in real time the problem of the client. Use AI to assist in asking questions. Use AI to analyze the sentiment of the client so that you can better relate to their problems.

These are weak uses of AI.

Equating problem understanding with active listening is absolutely wrong. Problem understanding means being able to discern the components of the problem, what the likely causes of the problem are, and what the consequences of the problem can be. And if you even quantify some of these elements, instead of being purely logical and theoretical, you would be able to build a powerful connection.

The real goal of problem understanding for a seller is not to show clients you understand their pain points. It’s helping them better understand their problems. They will do business with you not because you barely listen, but because you help them see what they would otherwise not see.

Here is where AI becomes strong. AI helps you understand and navigate the problem space. Before you meet with the client, you should already have a clear idea of the possible components of the problem, its causes, and its effects. AI helps you decompose problems more effectively, giving you a chance to engage in a powerful conversation with customers.

AI makes you understand problems before you talk to customers, so that when you talk with them, you can make them better understand their own problems.

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