A common misconception about AI, work and organizations, tested against the evidence.
AI often helps less-experienced people more. That does not mean expertise stops mattering.
A 2026 randomized experiment with 1,174 adults found that generative AI sharply narrowed an education-based productivity gap on a business problem-solving task. That is real evidence for capability leveling.
But another 2026 field experiment complicates the story. In a three-month trial with 133 practicing patent lawyers, AI improved assisted drafting quality, with larger immediate gains among junior lawyers. When participants later performed an expert judgment task without AI, however, the durable improvement was concentrated among senior lawyers. The authors conclude that foundational expertise may help people extract durable skill from AI-assisted practice.
AI can reduce the advantage of expertise in producing an answer while increasing the value of expertise in judging what to do with it.
If AI can explain the domain, draft the work and catch errors, expertise looks like something the tool is replacing.
But knowing information is only one component of expertise.
Current evidence suggests different effects at different layers: AI can raise immediate task performance and narrow gaps, while judgment, transfer and durable learning may still depend on deeper knowledge.
The balance will vary by occupation and task. The myth is treating "AI helps novices" as equivalent to "experts no longer matter."
Ask: Which part of expertise is AI commoditizing, and which part becomes more valuable because AI makes production cheaper?
In many knowledge jobs, the scarce skill may shift toward framing, verification, exception handling and judgment.
AI adoption is rising faster than enterprise value because companies keep installing new intelligence inside old operating models.
A successful pilot proves that one team could make something work. Organizational capability begins when a different team can reproduce the useful result without inheriting the original team's exceptional conditions.
Many of the questions that helped us orient ourselves around generative AI are now too blunt to be useful. The harder work is no longer asking what AI is in the abstract, but specifying where it works, where it fails, what authority it should have, and what the whole system costs.
RealityLiteracy helps individuals use AI. Organizational capability also requires workflows, data, evaluation, ownership, decision rights, integration, incentives and the ability to repeat results without the original champions.
RealitySaved time is released capacity. It becomes value only when the organization captures that capacity as better output, lower cost, higher quality, more revenue or less risk.
RealityUsage measures activity. Enterprise value depends on whether AI changes throughput, quality, revenue, cost, risk or the economics of an important workflow.