What’s happened to our understanding of competence?
Every age believes it understands the meaning of its most important words, even as the realities they describe quietly change beneath them. “Freedom”, “responsibility”, “authority” and “trust” have survived centuries of transformation, yet none means precisely what it meant to previous generations. “Competence” may have undergone a similar transformation, although one particularly difficult to…
Throughout history, the concept of competence has evolved, but it remains a difficult quality to define. Traditionally, competence was understood as the ability to act responsibly when rules, knowledge, and experience were no longer enough to provide a clear answer. This idea remained relevant even as generations passed and the meaning of words like "freedom," "responsibility," "authority," and "trust" transformed.
As societies grew larger and more complex, institutions sought to identify, certify, and evaluate competence at scale. Medical education introduced standardized exams, corporations developed performance metrics, universities formalized assessments, and regulators demanded demonstrable standards. While these efforts improved transparency and reduced arbitrary authority, they also entangled the indicators of competence with the concept itself.
The signals used to recognize competence, such as consistency, procedural correctness, measurable performance, and confident communication, gradually came to define what competence was. This shift created a problem: professionals who produced clear answers, followed established processes, communicated fluently, and performed consistently left a strong evidence trail of competence. However, those who recognized the limitations of existing frameworks and adapted to new situations might leave less tangible evidence.
As organizations increasingly rely on observable signals to identify expertise, there is a risk of privileging how competence appears rather than its actual impact. Generative AI systems can now create many of the signals associated with competence, such as articulate language, structured reasoning, rapid synthesis, polished presentations, and plausible explanations. These capabilities can be produced at a rapid pace, potentially rendering traditional forms of expertise less valuable.
The real concern is that AI may expose the limitations of our current definition of competence. If eloquence, consistency, information retrieval, and plausible answers can be generated effortlessly, perhaps these qualities were never the essence of competence. Instead, we may be entering an era where visible signs of competence become abundant, while the true intellectual process behind them remains elusive.
Rather than fearing AI's impact on traditional expertise, we should focus on identifying which aspects of competence become more crucial as its traditional indicators lose their relevance. Competence is not merely about producing correct answers; it also involves recognizing the right questions, distinguishing relevant information from available data, understanding when general rules apply to specific situations, identifying uncertainty, and accepting responsibility for the consequences of decisions.
AI can complement and enhance human expertise, but it should not replace the critical thinking, adaptability, and responsibility that define true competence. By reevaluating the dimensions of competence that matter most in an AI-driven world, we can ensure that our understanding of this essential quality remains robust and relevant.
Written by urgent.news from The National Business's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
