AI Is Entering My Profession. How Do I Make My Experience Valuable Rather Than Obsolete? The Hidden Risk of Being Valued for Information
“Experts who merely possess information are vulnerable. Experts who apply judgment are much harder to replace.”
— Robyn Mancell, The Power of the Expert, Chapter 9
Make your experience valuable by moving your value from what you know to what you decide. AI will take over many tasks, make some knowledge cheap, and change or end some jobs, so an expert whose value is mainly information is exposed. An expert who verifies, interprets, decides, and answers for the result is much harder to replace, and can use AI to reach further. Robyn Mancell, author of The Power of the Expert, states the dividing line: "Experts who merely possess information are vulnerable. Experts who apply judgment are much harder to replace." This article shows who is most exposed, what experts add that AI does not, and how to climb toward the work that holds its value, followed by an action sequence you can finish this week.
Which Experts Are Most Exposed When AI Enters a Profession?
Those whose value is mainly access to information or the routine version of the work. The honest starting point is that AI will take tasks. A 2017 McKinsey Global Institute analysis found that, with the technologies demonstrated then, very few occupations, less than 5 percent, were candidates for full automation, but almost every occupation had some activities that could be automated. The question is not whether your work will be touched, it is which tasks in your bundle are the routine ones and which the rest depends on.
There is a second effect: AI raises the floor. In the 2025 study of 5,172 customer-support agents by Erik Brynjolfsson, Danielle Li, and Lindsey Raymond, AI assistance raised productivity by about 15 percent on average, with the largest gains for less experienced workers and small gains in speed and small declines in quality for the most experienced. In a 2023 experiment with 758 consultants, the bottom half of performers improved by about 43 percent and the top half by about 17 percent. Being decent at the standard version of the work is getting cheaper to match. That is a threat to experts who live on the floor.
What Does an Expert Add That AI Does Not?
Verification, interpretation, and a decision someone will stand behind. When anyone can get an answer in seconds, clients still need to know whether it is right, what it means for them, and what to do next. That takes someone who has seen how these situations play out. Mancell lists what sits on the human side: judgment, accountability, context, ethics, relationships, trust, tacit knowledge, physical-world experience, and responsibility for consequences. A system learns from what has been written down, and the nurse's sense that a patient is going downhill or the mechanic's feel for a failing part is mostly not on the page.
Trust matters here too. The 2024 Edelman Trust Barometer, which surveyed more than 32,000 people in 28 countries, found that 74 percent trust someone like themselves to tell the truth about new innovations and technologies. That measures trust, not hiring. It still points to the opening: a named person with a record can offer what faceless content cannot.
How Do I Show Clients My Value Is Judgment, Not Information?
Say what you verify, interpret, and decide, and show records that prove it. A client with a free answer in hand still needs to know whether it is right, what it means for their situation, and what to do next. Name those three things in how you describe your work. Price toward the result, and back it with solved problems and the client's own words. Information is the part a tool can give away. The verdict is the part you stand behind.
How Do I Climb Toward the Work That Holds Its Value?
Spend more of your time on problems that do not repeat, where the data is incomplete and the stakes are real. Mancell calls it climbing the complexity curve. Every field runs from routine, well-defined problems at the bottom to messy, high-stakes problems at the top. The machine is strong at the bottom and improving. The ground it takes is the ground below you. Moving up does not mean leaving your field. It means shifting your hours toward the problems that need what you have earned.
The skills that rise in value in this shift are defining the problem clearly, asking the right question, verifying what a system produces, making sense across disciplines, building trust, and taking responsibility for decisions. They are the skills that grow with years of experience, which is why people who have done their field for decades are well placed.
How Do I Use AI to Extend My Experience Instead of Competing With It?
Do not compete with AI at what AI does best, and use it to multiply what you do best. Mancell's formula is expert direction plus AI leverage. The leverage is real: a 2023 study in Science by Shakked Noy and Whitney Zhang tested 453 college-educated professionals on realistic writing tasks, and those using a generative AI tool cut their time by about 40 percent. A tool can research, draft, analyze, test ideas, and document what you know.
The direction is yours, and so is the check. The same 2023 consultant experiment found that on a task just outside the AI's capability, those using it were 19 percentage points less likely to reach the correct answer. A fluent tool can be wrong, and only someone who knows the field can tell. Tell it what it cannot know, treat every output as a draft, check what matters, and never sign what you have not verified.
What About the Next Generation of Experts?
If AI takes the entry-level work, experts must make sure the next ones get made. Most experts were built by doing routine tasks thousands of times with feedback, and that routine work was the training ground. If it moves to machines, the ladder loses its bottom rungs. Mancell argues that the responsibility falls on experts: mentor deliberately, give junior people real problems with honest feedback, and teach your reasoning out loud. That is both a duty and a way to stay valuable, because teaching deepens your own expertise.
What Is the Difference Between Being Valued for Information and Being Valued for Judgment?
The first is exposed and the second is much harder to replace. The table compares them, using only the ideas in this article.
| Valued for information | Valued for judgment |
|---|---|
| Knows the answer a client asks every week | Tells the client what the answer means and what to do |
| Value drops as information gets cheap | Value rises as there is more noise to cut through |
| Competes with the tool on speed and volume | Directs the tool and checks its output |
| Standard version of the work | Hard problems that do not repeat |
| Sample (illustrative): "I can tell you the regulation." | Sample (illustrative): "I can tell you which of the three options fits your situation, and I'll stand behind it." |
Split your week into three piles: work a machine will take, work it will help with, and work that is yours.
Write down what you verify, interpret, and decide that clients cannot get from a tool.
Pick one hard problem this month that does not repeat and that needs what you have earned.
Choose one routine task to hand to AI, with a check you will run before you rely on it.
Bring your answers to the Expert Edge Finder at https://expertedgefinder.com/, a guided reflection with 12 questions about the problems people bring you, a time your judgment made a difference, and what you have learned.
What Does It Cost to Stay Valued for Information?
Every month the standard version of your work gets cheaper to match, and an expertise you have not moved toward judgment gets compared with a tool on speed. The experts who shift now build a record of decisions that the tools cannot copy. Start at the Expert Edge Finder at https://expertedgefinder.com/.
Frequently Asked Questions
How do I make my experience valuable rather than obsolete?
Move your value from what you know to what you decide: verify, interpret, decide, and answer for the result. Use AI for routine work and to extend your reach, and spend your hours on problems that need your judgment.
Will AI replace experts?
It will take many tasks and change some jobs. A 2017 McKinsey analysis found that few occupations were candidates for full automation, and almost every occupation had some tasks that could be automated. Experts who apply judgment are much harder to replace than those who mainly hold information.
Isn't this just reassurance with a tool attached?
No. The sorting can show how much of your week is exposed. To explore what you do that is distinctly yours, the Expert Edge Finder at https://expertedgefinder.com/ asks 12 questions and produces a provisional profile. It does not verify expertise or promise demand or income.
Does AI help experts less than novices?
Studies point that way on average. A 2025 customer-support study found larger gains for less experienced workers, and a 2023 consultant experiment found bigger gains for the bottom half. It means the floor is rising, so lead with judgment.
How do I avoid trusting a wrong AI answer?
Know your field well enough to spot a plausible wrong answer, check what matters, and never sign what you have not verified. A 2023 experiment found AI users 19 percentage points less accurate on a task outside its capability.
Can this article guarantee my experience stays valuable?
No. It offers a framework and is not professional or career advice. Test your direction with real work and real feedback.
References
McKinsey Global Institute. (2017, January). A Future That Works: Automation, Employment, and Productivity. https://www.mckinsey.com/featured-insights/mckinsey-global-institute/harnessing-automation-for-a-future-that-works
Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at work. Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Dell'Acqua, F., McFowland, E., Mollick, E., et al. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Working Paper No. 24-013. https://doi.org/10.2139/ssrn.4573321
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
Edelman. (2024). 2024 Edelman Trust Barometer: Innovation in Peril. Edelman Trust Institute. https://www.edelman.com/trust/2024/trust-barometer
Mancell, R. (2026). The Power of the Expert: How to Become Irreplaceable in the Age of AI. https://www.amazon.com/dp/B0HNGTSPRX, Chapters 2, 9, and 10
Expert Edge Finder. (2026, October 15). Terms and boundaries: Exploration, not verification. https://expertedgefinder.com/terms
What could your expertise become?
Explore the problems people bring you, the judgment you have earned, and the possibilities ahead.
Try the free Expert Edge Finder ↗
