We now often see phrases like \u201cproficient in AI tools\u201d on r\u00e9sum\u00e9s, in job postings and on LinkedIn profiles. But that phrase means less and less, because opening and using an AI tool has become as basic a skill as sending an email. What really makes a difference is not \u201cI can use AI\u201d but \u201cI know what I need to ask AI.\u201d<\/p>\n\n\n\n
Ten years ago, saying \u201cI know Excel\u201d was an advantage. Today it is a minimum expectation in the business world. AI tools are on the same path: interfaces are getting simpler, mobile apps are spreading, and anyone can learn the basics in a few days. This means the value of \u201cbeing able to use AI\u201d as a competency is falling quickly, because everyone reaches the same threshold in a short time.<\/p>\n\n\n\n
The result: what sets one person apart from another in business is no longer being able to open and use the tool, but what they use it for, with what kind of question and to what end. Two people can open the same tool; one gets a useful result, the other is left with generic text. The difference is not knowledge of the tool but the quality of the question.<\/p>\n\n\n\n
This skill has three parts. The first is being able to define the problem clearly: knowing exactly what needs solving, who it matters to and which constraints apply. The second is being able to choose the right context: telling which information AI needs and which it does not... because too much information can do as much harm as too little. The third is being able to evaluate the answer: deciding whether it is useful and, if necessary, reshaping the question.<\/p>\n\n\n\n
All three parts are thinking skills that existed before AI. The questions a consultant asks in a client meeting, the questions an investor asks at a startup pitch, the questions a doctor asks a patient: all are the same skill in different fields. AI did not create this skill; it only made it more visible and more measurable.<\/p>\n\n\n\n
Hiring criteria in the business world are slowly catching up with this reality. A candidate whose r\u00e9sum\u00e9 says \u201cI know ChatGPT, Midjourney, Copilot\u201d and a candidate who can give a clear, structured answer in an interview when asked \u201cwhat kind of question would you ask AI to solve this problem\u201d carry very different potential. The first has memorized a list of tools; the second has demonstrated the ability to solve problems.<\/p>\n\n\n\n
This difference is even more pronounced in roles that work directly with customers, especially consulting and sales. Someone who cannot ask a customer the right question cannot offer that customer a useful solution, even with the most advanced AI tool at hand. Because a solution starts with understanding the right problem. And that is a human's job, not an AI tool's...<\/p>\n\n\n\n
The good news: asking questions is a skill that existed long before AI, and it can be developed. The most practical way is to build a habit of pausing briefly before every AI interaction: \u201cWhat am I really trying to solve, who will use this result, and what information would leave this question incomplete?\u201d Once asking yourself these three questions becomes a habit, every request you make of AI automatically becomes better.<\/p>\n\n\n\n
Another method is to compare successful and unsuccessful questions: when a result does not work, analyze in hindsight where the question fell short. Over time this builds an intuition that works almost automatically, just like the intuition an experienced consultant develops over years of client meetings...<\/p>\n\n\n\n
Saying \u201cI can use AI\u201d means less and less, because it is a threshold anyone can reach in a few days. The real difference comes from being able to say \u201cI know what I need to ask AI.\u201d That is a skill rooted not in knowledge of tools but in defining problems and clarity of thought. And that is exactly why, in the age of AI, the most valuable skill is still \u201cbeing able to find where the solution is hiding\u201d.<\/p>\n","protected":false},"excerpt":{"rendered":"
We now often see phrases like \u201cproficient in AI tools\u201d on r\u00e9sum\u00e9s, in job postings and on LinkedIn profiles. But that phrase means less and less, because opening and using an AI tool has become as basic a skill as sending an email. What really makes a difference is not \u201cI can use AI\u201d but […]<\/p>\n","protected":false},"author":1,"featured_media":4159,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19,18],"tags":[],"class_list":["post-4158","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-blog"],"_links":{"self":[{"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/posts\/4158","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/comments?post=4158"}],"version-history":[{"count":1,"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/posts\/4158\/revisions"}],"predecessor-version":[{"id":4160,"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/posts\/4158\/revisions\/4160"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/media\/4159"}],"wp:attachment":[{"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/media?parent=4158"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/categories?post=4158"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mareo.com.tr\/en\/wp-json\/wp\/v2\/tags?post=4158"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}