AI is changing what it means to demonstrate competence.

Bloomberg frames AI as making interviews less trustworthy. A more interesting interpretation is that AI has revealed interviews were never measuring what companies thought they were measuring.

Hiring is fundamentally a prediction problem. Companies are trying to answer one question: will this person create value in the role? Interviews have always been an imperfect measurement tool. They capture a combination of ability, communication skills, confidence, preparation, background, and sometimes luck.

That creates two classic statistical errors.

A Type I error is a false positive: hiring someone who appears capable but fails to perform. AI can increase this risk by helping candidates create a stronger impression than their actual abilities justify.

A Type II error is a false negative: rejecting someone who could have been exceptional because they failed to demonstrate their ability in an artificial interview setting. This has always been a problem. Some people are outstanding operators but poor interview performers. They may struggle with pressure, self-promotion, language, or simply explaining their thinking in a format that does not resemble the work itself.

AI changes both sides of the equation.

It can allow an unqualified candidate to appear more capable. But it can also help a highly capable candidate communicate their knowledge, structure their thinking, and overcome barriers that previously prevented them from being recognized.

The most interesting response from companies is not to fight AI, but to rethink what they measure.

McKinsey recently introduced an AI-powered interview preparation tool that gives candidates unlimited practice with quantitative case studies. The reasoning is revealing. Preparation has always influenced outcomes. Wealthier candidates could hire coaches, buy expensive courses, or access insider knowledge. AI may reduce that advantage by making high-quality preparation available to more people.

At the same time, McKinsey has also started incorporating AI into its own evaluation process, asking candidates to work with AI tools while assessing their judgment, critical thinking, and ability to challenge the output.

This represents a fundamental shift.

The future of work will involve AI. The question is whether someone can use AI effectively while applying judgment, asking better questions, and creating valuable outcomes.

Every generation of technology forces institutions to reconsider their assumptions. Calculators changed mathematics education. Search engines changed what it meant to know information.

AI is changing what it means to demonstrate competence.

Rather than the end of interviews, I see this as an evolution of how we identify talent. It gives organizations the opportunity to move beyond evaluating who performs best in an artificial setting and toward recognizing the people who can learn, adapt, and create value in the real world. Until next time,

Ram

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Ram Srinivasan

MIT Alum | Author, The Conscious Machine | Global Future of Work and AI Adoption Leader published in Business Insider, Fortune, Harvard Business Review, MIT Executive Viewpoints and more.

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Disclaimer:

Ram Srinivasan currently serves as an Innovation Strategist and Transformation Leader, authoring groundbreaking works including "The Conscious Machine" and the upcoming "The Substrate Shift."

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