AI Is Cheapening Expertise. Judgment Sets the New Price

By Olivia Hart

Microsoft’s 2025 Work Trend Index found that 81% of leaders expected AI agents to be moderately or extensively integrated into their companies’ AI strategies within 12–18 months. In an Entrepreneur contributor essay, the author treats that expectation as a warning: expertise alone no longer guarantees a commercial premium.

The author contrasts a profession built on scarce answers with this warning: “Today, it might just make you the most expensive search engine.” The essay offers no price, revenue or hiring data to establish how broadly that reversal applies.

What is AI already changing?

For decades, the source argues, customers paid for expertise because acquiring it was difficult, expensive or time-consuming. AI can now, according to the author, analyze markets, explain financial concepts and develop marketing strategies that previously required a specialist.

The author uses a consultant with 20 years of industry expertise to illustrate the pressure on explanation fees. The consultant’s knowledge has not vanished, the source says, but customers may question paying for information AI can provide in seconds.

The World Economic Forum’s Future of Jobs Report 2025 identifies analytical thinking as employers’ most important core skill. Creative thinking, resilience and technological literacy also feature prominently, according to the report, though it does not measure willingness to pay for expert judgment. The author describes the premium as shifting from possessing knowledge to applying it in context.

The author describes technology as increasingly capable of producing information and completing tasks, while businesses still need people to evaluate those outputs, connect them to commercial realities and decide what comes next. The Microsoft figure records expectations, not realized productivity or prices, so the evidence shows rising AI expectations, not a proven shift in expert pricing.

Where does judgment still create value?

Drawing on 25 years in business, the author says they founded the international public relations agency NettResults in 1999. Its early advantage was specialist knowledge of the Middle East technology market, including the media landscape, local business practices and cultural differences.

International clients entering the region also needed help communicating with unfamiliar audiences. They relied on NettResults’ knowledge and on relationships developed over years, which they could not search for or replicate overnight.

According to the author, AI can now explain the regional media landscape, identify publications, research competitors and help develop communications strategies in minutes. The harder task, in the author’s account, is deciding which journalist is likely to care, how regional sensitivities may affect reception or whether a story should be pitched at all.

That distinction rests on experience, relationships, context and judgment, which the author regards as harder to automate. No data in the essay measures whether AI can replicate those relationships or whether clients pay more for them; information is cheaper, but judgment still must prove its worth.

What should experts solve instead?

The author says traditional expertise tends to stay within disciplines: marketing specialists understand marketing, finance specialists understand finance, and technology specialists understand technology. Business problems rarely arrive neatly labeled, so useful diagnosis often reaches beyond the area where symptoms first appear.

The author describes working with an owner who believed hiring more people would resolve pressure from rising demand. The examination instead found inefficient processes, unclear responsibilities and the owner’s involvement in too many everyday decisions; adding staff might have increased capacity without fixing those constraints.

The proposed response was to examine workflows, clarify accountability and identify where technology could remove unnecessary work before adding employees. The essay presents this as one anonymized case, without measures of cost, revenue or the results of the intervention. The author frames the problem as operational design, leadership and technology, rather than a pure staffing decision.

That case supports the author’s broader argument: cross-functional knowledge of technology, psychology, finance, operations and customer behavior may create more value than mastery of a single field. The entrepreneur must recognize which information matters and make decisions for the whole business.

The source preserves a limit: deep expertise remains essential when decisions carry significant financial, legal or operational consequences. Expertise without context becomes easier to commoditize; the opportunity is broader judgment, not the disappearance of specialists.

How should services be repositioned?

The author recommends a four-question “expertise value audit” for businesses selling knowledge, advice or specialist services. It requires neither an expensive consultant nor a six-month strategic review, according to the source.

1. What do customers currently pay us to know? Identify the specialist knowledge, information and capabilities underpinning your products or services.
2. What could AI now provide them directly? Be realistic about which parts of your offering are becoming easier, faster or cheaper for customers to access independently.
3. Where does our judgment create value? Look for the decisions, relationships, interpretation and experience that produce better outcomes than information alone.
4. How should we reposition what we sell? Consider whether you should be charging for access to knowledge, time spent delivering it or the commercial outcomes your expertise helps customers achieve.

The audit shifts attention from accumulated information toward decisions, relationships, interpretation and measurable customer outcomes. It does not quantify how to price the difference; the source offers no fee structure or performance standard.

The exercise does not require abandoning expertise, according to the author. It aims to avoid building tomorrow’s business around yesterday’s competitive advantage.

The prescription is blunt: “Stop selling answers. Start selling outcomes.” AI can increasingly describe what is possible, the source argues, while experienced professionals judge what is sensible, commercially viable and worth doing. The author places responsibility for the result at the center of that work.

For entrepreneurs, the near-term test is whether clients will pay for measurable results once comparable information becomes cheaper. The essay leaves that proof to each business.