Artificial intelligence, the amplifier of who you are

For the past few months, social media has been overflowing with promises. Some claim that all you now have to do is ask an artificial intelligence to draft a report, create a marketing strategy, write code, or prepare a business plan to instantly obtain a result worthy of an expert. The idea is appealing. It is also deeply misleading.

Artificial intelligence is not a machine for turning inexperience into expertise. It replaces neither judgment, nor experience, nor an understanding of a problem. Rather, it accelerates the work of those who already know where they want to go. The distinction may seem subtle. It is nonetheless essential.

Imagine a manufacturing shop where a digital grinding machine capable of machining a part with exceptional precision is installed. That machine does not turn an apprentice into an experienced toolmaker. If the part is poorly designed, if the dimensions are wrong, or if the material is unsuitable, the machine will quickly produce... a bad part, but with remarkable precision.

The same is true in a professional kitchen. A Thermomix can automate many operations, control temperatures to the degree, and faithfully reproduce a recipe. Yet it does not decide which ingredients to choose, which flavors to balance, or which dish will suit the guests. It executes what it is asked to do.

Artificial intelligence works in a similar way, accelerating, organizing, rephrasing, and synthesizing, but it possesses neither your context, nor your objectives, nor your accountability. That is why learning to work with an AI does not begin with learning to write a good prompt. It begins with learning to structure your own thinking.

Even before opening a conversational assistant, you must be able to answer a few simple questions, such as:

• What problem are we really trying to solve?
• What information is already known?
• Which assumptions rest on facts and which remain to be demonstrated?
• What results will make it possible to conclude that the answer is satisfactory?

Without that preparation, AI becomes just one more generator of hypotheses. It can produce convincing, elegant, sometimes even brilliant answers… but ones that rest on an incomplete or mistaken understanding of the problem at hand. This is the modern application of a well-known principle in computing: garbage in, garbage out. If the question is confused, the answers are likely to be confused as well. They will simply be better worded. This reality takes on particular importance in business.

Every interaction with an artificial intelligence model consumes resources: time, computing capacity, energy and, very often, money. An organization that multiplies attempts because its teams do not know precisely what they are looking for does not gain productivity. It simply replaces part of its thinking time with a series of costly iterations.

The best teams do not use AI to think in their place. They think first. Then they use AI to accelerate their work, explore scenarios, test certain assumptions, structure a document, or identify angles they might not have considered. The tool then becomes an amplifier of their intelligence rather than a substitute for their thinking.

This is also why it is dangerous to treat an artificial intelligence's answers as established truths. A model may rely on incomplete data, unreliable sources, or outdated information, or produce what are known as hallucinations: plausible but factually false answers.

If you do not know your field well enough to recognize an error, you will be no better able to recognize an excellent answer. You then risk making an important decision on a basis that appears credible without actually being so.

Artificial intelligence therefore does not replace knowledge. It extends its reach. An AI does not replace experience but accelerates its application. It does not replace judgment but offers more material on which to exercise it.

The organizations that get the most out of this revolution will not be those that use the most artificial intelligence. They will be those that have invested in the quality of their thinking, in the structuring of their knowledge, and in developing their teams' judgment.

Consequently, the value will never come from the machine but very much from the quality of the human intelligence that guides it.