Every year, I teach a one-week course through the American Enterprise Institute’s Honors Program on how to think about your education and career. I recently caught up with a former student who joined a business consulting firm in 2024, just as artificial intelligence tools were going mainstream. While much of America ricochets between utopian dreams and Terminator-like fears about AI, in his experience, AI is far from taking over industries or disemploying workers. Instead, the technology appears to be fulfilling its early promises to help workers leverage their knowledge and time in order to do more and better work.
That word — leverage — is crucial. In a consulting context, leverage refers to producing more value per unit of effort. For junior consultants, it means accelerating teamwork, boosting quality and compressing more productive activity into less time. “AI is like an extra set of hands,” my former student told me. “My manager gives me a task that creates leverage for him. I do the same with AI.”
In our emerging AI-infused economy, technology is helping workers across the knowledge sector frame problems, design research methods, automate and improve data analysis, and support breakthrough thinking— all helping to leverage human effort and skill. In most cases, the technology is helping to sharpen human insight and judgment rather than replacing human work.
This trend toward leverage has deep consequences, especially for younger workers trying to break into well-paid knowledge-sector occupations. Today’s job market is not friendly to new graduates. In a classic illustration of skill-biased technological change, AI’s automation of basic tasks is shifting employer skill demands and pushing workers away from routine, technical tasks and toward broader, tacit ones that require insight.
The research, spreadsheet construction and client-delivery tasks that once served as apprenticeships for new analysts remain important but now act mostly as background knowledge. The new high-value work relies on intangibles: judgment, discernment, ethical awareness and interpersonal communication required to persuade clients to follow through on recommendations. AI doesn’t “think” or “know” but it is becoming a crucial support to the human workers who do.
This emerging reality redefines what “entry-level” means and introduces new forms of workplace anxiety. Recent hires no longer build products cell by cell in spreadsheets, yet they’re expected to maintain full comprehension of their work as if they had. “I’d be terrified to bring an AI-only recommendation to my manager,” my former student said. “You have to understand every piece.”
A junior analyst must defend every number, anticipate every question and take ownership of every conclusion. This means having the judgment necessary to differentiate between AI’s insight and its hallucinations. Those who adapt fastest treat AI not as a shortcut but as a partner and collaborator — another mind to test assumptions, explore scenarios and structure analysis. AI prompting, my student observed, is like coding or modeling: it rewards precision, iteration and curiosity. Fast learners climb quickly.
Onto their daily workflows: Surveys find that half of all consulting firms use AI in at least one business area. AI is no longer a side tool; it’s becoming a foundational backbone of the modern consulting practice, developing new ways to create value.
These internal shifts mirror changes on the client side. As firms adopt AI to improve speed and quality, some clients are experimenting with “at-risk” contracts that tie consulting fees to measurable outcomes like cost savings, implementation speed or risk reduction. In effect, the leveragers in consulting are being leveraged by their clients who are aware of AI’s impact and are demanding more insight rather than more elaborate slide decks.
What does all this mean for the rising generation of knowledge workers? When I asked my former student what advice he’d give to others preparing for work in the AI economy, his response was immediate.
“First,” he said, “learn continuously. Treat curiosity as a professional habit. Read one thing every day outside your field, whether it be about the arts and literature, the natural sciences or public affairs. The breadth you build today becomes the adaptive muscle you’ll need tomorrow.” When I asked which classes outside of his business major proved most useful, he talked about a sociology course that expanded his understanding of human and organizational behavior, knowledge essential to working with colleagues and clients.
His second piece of advice was equally practical: “Get fluent in AI workflows. Don’t just prompt for answers. Use AI end-to-end to frame problems, refresh methods, structure analysis and synthesize results. Then, focus on explaining what you did and why. The differentiator won’t be who uses AI, but who uses it well — and understands its limits.”
As AI eats routine tasks, the work left on the table is that of meaning-making, explanation, persuasion, and the integration of knowledge and judgment across knowledge domains and operational systems. Technical skills are necessary but insufficient; interpersonal awareness and the ability to “read the room” may soon be worth more than any degree or certification. As technical work recedes, learning speed, contextual breadth and moral clarity about its limits will pull ahead.
In the age of AI, the ultimate form of leverage is learning itself. Those who cultivate curiosity and discernment will not only keep up but help shape what comes next.