We’re all being encouraged to work smarter with AI. But what happens when the machine learns to do the work we’ve spent years mastering?
I came across an interesting question on LinkedIn recently. Someone asked whether teaching an AI agent to work the way you do means you’ve created your best tool or trained your own replacement.
I thought it was a fair question. After all, we’re constantly being encouraged to use AI to become more productive, automate repetitive tasks, and free ourselves from tedious work. Sounds wonderful, especially when you’ve spent years doing things manually and would happily never do some of them again.
But there’s something rather amusing about the arrangement. We’re teaching machines how to perform our work, documenting our processes, and refining the results until they resemble something we’d produce ourselves. Then we celebrate how much time we’ve saved.
And somewhere in the back of my mind, a small voice asks whether we’ve just made ourselves optional.
The Knowledge We Carry
As a freelancer working with digital systems, I can see the appeal of AI agents. If I can build something that handles repetitive tasks, checks information, or follows a workflow I’ve designed, that’s time I can spend on more interesting problems.
But there’s a difference between knowing how to perform a task and understanding why it’s done a particular way. Take a business automation. Someone might learn how to connect a form to a CRM, trigger an email, and move a contact through a pipeline. Those steps can be documented, repeated, and increasingly automated.
What isn’t always obvious is why the business needs that particular workflow. What happens when a customer doesn’t respond? What if the information is incomplete? What if the process works technically but creates more confusion for the people using it?
Those questions require understanding the business, its customers, and the decisions behind the process. Of course, AI is becoming increasingly capable of helping with those questions too. I wouldn’t assume that strategic thinking will remain exclusively human territory forever. That would be a rather comfortable prediction to make about my own livelihood.
Still, I think there’s value in being the person who understands how the pieces fit together, especially when circumstances change.
What About the Business Owner?
Now, if I were running a business and paying someone to perform a repetitive process, I’d probably be interested in making that process more efficient.
Why wouldn’t I?
If an AI agent can reduce errors, save time, and allow employees to focus on other responsibilities, that could be good for everyone involved. But I also think business owners should consider what they’re actually automating.
A process might look straightforward until something unexpected happens. An employee who’s been handling it for years may recognize problems that never made it into the written instructions. They might know which customers need a different approach, which exceptions matter, or why a particular shortcut causes trouble later.
If a business captures only the steps without understanding the judgment behind them, it may end up with a very efficient system for making the wrong decisions. And that’s not much of an upgrade.
There’s also the question of what happens to the people whose knowledge helped build the system. Will they be given opportunities to take on different responsibilities? Will productivity gains benefit them too? Or will the business simply decide it no longer needs as many people?
I don’t think there’s one answer. Different businesses will make different choices, and employees and freelancers won’t all have the same bargaining power.
Who Owns What You’ve Taught It?
Another question worth considering is ownership.
Suppose you’ve spent years developing a way of working. You then use that experience to create an AI-assisted process that performs much of the work for you. Who owns what you’ve built? The answer may depend on your contract, the platform you’re using, the information involved, and whether you developed the system independently or as part of your paid responsibilities.
Using an AI platform doesn’t automatically mean the platform owns everything you create. But neither should we assume that building something for a client makes it ours to reuse however we please.
It’s worth understanding those arrangements before sharing confidential information, uploading business processes, or handing over a system that contains years of accumulated knowledge.
Perhaps not the most exciting part of working with AI, but neither is discovering the fine print after the fact.
Perhaps the Role Changes
Personally, I don’t see myself trying to compete with AI at every task it can perform. I’d rather learn how to use it, understand where it fits, and help businesses build systems that actually work.
If that means spending less time doing repetitive implementation and more time planning workflows, making decisions, and overseeing how things operate, I’d consider that a worthwhile direction. But I also recognize that not everyone has the freedom to make that transition. Some people may lose opportunities before they have a chance to develop new ones. Others may find that AI gives them more independence and bargaining power.
That’s why I hesitate when people confidently declare that AI will either liberate workers or replace them. Both possibilities deserve consideration, and much depends on the decisions people and businesses make along the way.
So, are we training our replacements when we teach AI our jobs?
Possibly. In some situations, that’s exactly what may happen. In others, we may be building tools that make our work more valuable or allow us to do things we couldn’t manage before.
I suppose the question I’d ask is this: Once the machine knows how to do the work, what happens to the person who taught it? Because that part of the story is still being written.
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