AI has answers, experience has judgment

AI provides abundant answers, while experience delivers valuable judgment. Ask an AI system how to improve a factory, clinic, logistics company, or retail business, and it can generate ideas, explain trends, draft plans, compare options, and create a persuasive proposal in minutes. However, it's easy to mistake useful information for good advice.

An AI can suggest numerous ways to improve a factory, but a person with 10 years of experience on that factory floor likely knows which 19 of those suggestions will fail by Friday. This is not because the experienced person knows more facts; AI likely possesses more facts than either of you can read in a lifetime. Rather, experience grants people judgment.

Information access used to be an advantage. With knowledge of where to look, who to call, and how to write a decent first draft, one could move faster than someone lacking such skills. AI is rapidly lowering this advantage, making the cost of a first attempt approach zero. This is beneficial for those with limited resources, such as graduates exploring ideas independently, small businesses conducting research without extensive teams, and retrained managers developing plans for new services or products.

Nevertheless, when everyone can obtain answers, the value shifts to determining which answer is most relevant. Experience acts as a filter built through repetition, guiding a nurse to prioritize symptoms, a procurement manager to identify reliable suppliers, a mechanic to recognize critical sounds, and a founder to discern genuine market signals from mere opinions.

While AI can present an expert with more options, it cannot fully comprehend local constraints without someone who understands them providing the necessary context. Consequently, experienced individuals become increasingly valuable rather than less so.

Asking a well-crafted question, rather than a vague one, leads to more useful AI responses. For instance, instead of asking, "How can I improve my logistics business?" a more targeted query like, "How can I reduce failed same-day deliveries in Jakarta during peak rain, without adding drivers or reducing margins?" yields more practical guidance.

Thus, a domain expert utilizing AI can transform broad suggestions into actionable, real-world tests, identifying ideas that are likely to succeed. While creation has become affordable, judgment remains scarce. As producing a first draft becomes cost-free, the true value lies in determining what is worth creating. This is crucial for graduates, retraining professionals, and SMEs.

Building practical knowledge and gaining real-world experience is invaluable, as it forms the basis for service improvements, product innovations, and inventions. Moreover, if this expertise is captured, documented, and developed, it can lead to better products, trade secrets, patentable inventions, or enhanced working methods. Ultimately, AI serves as a powerful tool, augmenting the capabilities of experts rather than replacing them.

By assisting experts in exploring new ideas and refining existing processes, AI enables practical knowledge to reach a broader audience. The future will benefit those who understand how to effectively leverage AI, as well as those who can discern which questions AI should answer.