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"If AI Eliminates the Effort, What Creates Value Now?"

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If AI Eliminates the Effort, What Creates Value Now?

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June, 2026

Author: Natalia Makedonska

Over the past few months, I have found myself asking founders the same question more and more often.During our consulting conversations they tell me about their AI-first products, AI-powered software studios, lean organizations, and how they expect small teams of highly experienced people to accomplish what previously required dozens of specialists. 
Eventually, I ask them something much less technical: how are you planning to compensate those experts? Interestingly, very few people have a clear answer.
For decades, effort was a reasonable proxy for value. AI has broken that proxy.
Historically, it was easy to assume that if a piece of work took a week to produce, it was worth more than something that took a day. That assumption made sense because effort and value were usually closely correlated. The more knowledge-intensive the work, the more time it demanded.
In the AI era, that relationship is changing faster than most companies realize. The challenge is that we haven't yet found a better proxy. Most companies still buy hours because they don't yet know how else to value expertise.
A good example comes from my own work.
Organizations often ask me to assess talent markets before they expand into a new country. Research is only one part of that work. Nobody hires me to collect information - they can increasingly do that themselves with AI. They bring me in to interpret the information, challenge it, validate it and help them make decisions they can still defend months or even years later.
Not long ago, preparing such a report could easily consume a full week or two. I had to identify reliable sources, compare contradictory information, recognize patterns, validate assumptions and eventually formulate recommendations.
Today, AI allows me to complete much of that process in a single day: the research is much faster, the analysis is faster, and even producing a well-structured report is significantly easier than it was only a year ago.
However, one thing has not changed: the client is still making exactly the same business decision based on my recommendation. They are not paying for a document; they are paying for confidence that the recommendation is sound.
That confidence doesn't come from AI. It comes from knowing which questions AI should answer in the first place, recognizing when its conclusions don't fit the client's reality, validating critical information through independent sources, interpreting everything within the business context and, ultimately, taking responsibility for the recommendation. That is the part AI cannot automate.
This became even more apparent during a recent conversation with the founder of an AI software studio in the Bay Area.
He was explaining his business model: instead of large engineering teams, he wanted to hire two or three exceptional software architects who, together with AI, could build highly customized products for clients in just a few days.
It sounded like an obvious productivity story until we reached a much more interesting question: how should those architects be compensated?
If an experienced architect can create a product in two or three days that later generates tens - or even hundreds - of thousands of dollars in revenue, does it still make sense to pay for three days of work?
My next question was even more practical. Why would these architects accept that model? If they are capable of creating products with significant commercial value in just a few days, why should they build them for someone else at an hourly rate instead of building them independently?
That conversation made me realize that AI is changing more than the way work gets done. It is changing what is actually scarce. Research, coding, presentations, analysis and documentation are becoming dramatically faster and cheaper to produce.
What remains scarce is the ability to transform information into good business decisions.
Good decisions require much more than judgment. They require enough context to understand the client's business, enough experience to ask questions that AI would not ask on its own, to recognize patterns AI cannot see, enough critical thinking to know when AI is confidently wrong, and enough confidence to take responsibility for the recommendation instead of hiding behind the technology.
Perhaps that is becoming the new definition of expertise.
One of my colleagues recently said something that deeply resonated with me: “AI is only as good as the person who's using it.” I fully agree, but I would probably take it one step further.
The better AI becomes, the more valuable human expertise becomes - not because experts produce information faster, but because they remain accountable for deciding what information deserves to be trusted.Companies have always believed they were buying expertise. In reality, many of them were buying time because time was the easiest thing to measure. AI is forcing us to distinguish between the two.
I don't really think the question is whether experts should continue charging by the hour. What I'm more interested in asking the market is whether companies should continue buying expertise by the hour at all.If AI has separated effort from value, perhaps it is finally forcing us to answer a question that has always been there: what are we actually paying experts for?
I'd genuinely love to hear how founders, business leaders and fellow consultants think about this, and what approaches they choose today.
If you're building an AI-enabled company today, how are you thinking about compensating senior experts?Are you still buying hours, or are you already experimenting with something else?

Natalia MakedonskaBusiness transformation architect, catalyst and enabler

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Created by Kateryna Tsvietkova

Natalia MakedonskaBusiness transformation architect, catalyst and enabler

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