3 Questions To Ask When AI Does Your Job Better Than You Do

What Would I Miss If AI Took This Part Of My Work?

Imagine that AI could completely take over one part of your job tomorrow. You still have your position, you still receive the same paycheck and nobody is handing you a cardboard box and escorting you out of the building. You simply do not have to do that particular task anymore, so what would you miss?

Your answer can tell you much more than the task itself. Maybe you would miss figuring something out because you liked having a difficult problem land on your desk and digging into it until you understood what everyone else had missed. Maybe you would miss talking with customers because the report was never the meaningful part of your job and the conversation that helped someone solve a problem was.

You might miss creating something, improving a process or taking an idea that existed only in somebody’s head and turning it into something useful. You might miss being the person whose experience helped somebody else make a better decision. What you miss can tell you where the meaning in the work was actually coming from.

In my research on meaning at work, I have been looking at four common sources people describe: exploration, connection, creation and influence. People can experience all four, and their importance can change throughout our lives and careers. You do not need to put yourself into a category because the value comes from understanding what you would miss and why.

Once you understand what made the work meaningful, you can start looking for other places to find that same source of meaning. The task may disappear, but the reason you cared about the task does not have to disappear with it. That distinction can help you look at AI-driven change with a much wider view of what you could do next.

What Do People Rely On Me For As AI Changes The Work?

This question is different from asking what you are responsible for. Your job description might say you manage accounts, review contracts, analyze data, prepare reports or oversee operations. After ten or twenty years, though, people probably rely on you for things that never made it into the job description.

Maybe you are the person everyone sends the difficult customer to because you can tell what that customer is really upset about before anybody else can. Maybe you can look at a report and sense that something is off, even if you cannot explain what caught your attention until you look more closely. Maybe you know which question needs to be asked in a meeting because you have watched the organization make the same mistake three times before.

That is experience, and AI may become extremely good at performing more of the visible parts of our jobs. When that happens, it can be easy to assume that because the task became easier, the years we spent learning how to do it somehow became less valuable. I think that describes our value too narrowly.

Experience is not simply knowing how to complete a task. It is recognizing patterns, understanding context, spotting exceptions, knowing when something deserves a second look and remembering what happened the last time everybody thought an idea was brilliant. Someone with twenty years of experience has probably learned far more than the mechanics of the job.

One useful way to find that hidden value is to ask people, “What do you rely on me for?” You might be surprised by the answer because they may describe something you have never considered part of your expertise. Some abilities come so naturally after years of experience that you stop noticing them, and those abilities may become even more valuable as AI handles more of the work that used to take up your time.

Where Else Could My Experience Matter As AI Changes My Job?

Once you know what you would miss and what people rely on you for, you can ask a much better question about the future: Where else could I use this? You do not have to predict what your career will look like five years from now. That was difficult enough before AI began changing work this quickly.

If people rely on you because you understand customers unusually well, look for a customer problem nobody has been able to solve. If you recognize patterns other people miss, ask where that judgment could make a difference. If you have spent years learning why certain projects succeed and others fail, consider who else could benefit from that knowledge.

You can make this practical without making a major career decision. If AI gives you five hours back every week, use one of those hours differently by talking with the customer instead of creating another report about the customer, helping somebody newer recognize in ten minutes what took you ten years to learn, or joining a project where your experience has never been used before. Small experiments can reveal possibilities that are difficult to see when you are trying to map out the next five years all at once.

I like the idea of treating this as a one-week experiment rather than a five-year career decision. Use something you already know somewhere slightly different and see what happens. That is where adaptive curiosity becomes useful because it allows you to keep learning, noticing and moving toward possibilities even when you cannot see exactly where they will lead.

People spend a great deal of time talking about how much time AI can save, but I am more interested in what people choose to do with the time they get back. Five hours of saved time is not automatically five hours of value. If somebody has spent twenty years learning your customers, your industry, your systems, your mistakes and your people, and technology suddenly gives that person five hours back, the interesting question is what those twenty years are now available to help solve.

What To Do When AI Does Your Job Better Than You Do

AI will almost certainly become better than each of us at more of the things we currently do, but that does not mean every year we spent becoming good at those things suddenly matters less. Twenty years of experience should never be reduced to the part of your job AI can now do faster, so when AI becomes better at your thing, resist the urge to immediately chase somebody else’s thing. Ask what you would miss, ask what people rely on you for and then ask where else that experience could matter, because your next thing may already be hiding inside what you have spent years learning.