Mateusz Mucha is CEO of Omni Calculator, a platform helping 15M+ monthly users make better decisions through expert-reviewed calculators.

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​I want to address the (seemingly) ever-increasing anxiety about AI replacing jobs, because I don’t see it that way.

For many companies, AI offers the most value as a layer that integrates into existing workflows, allowing teams to streamline mundane tasks and increase output. For instance, as I've written about previously, I've found value in using AI to challenge my assumptions and create prototypes.

​However, humans still have an edge over AI in areas where judgment or accountability matters. AI can generate plausible answers, but it can't guarantee they're correct or take responsibility for them.

This doesn't even account for when AI takes your prompt too literally, which has led to AI generating refunds when it wasn't supposed to or creating "several hundred thousand excess" beer cans due to a misunderstanding, according to CNBC.

As companies begin to understand these risks, they will likely outsource routine tasks to AI while moving into roles that require more supervision, validation, decision-making and creativity. ​

That said, I also recognize that not all roles are affected equally.​ Jobs built around repetitive, structured tasks (e.g., basic customer support) are more at risk because AI can already perform a large share of this work faster and cheaper.

This leads me to a related concern: How will junior-level employees learn if AI removes the grunt work, since most of us built our foundations through tasks, such as data entry, basic research and repetition, that developed pattern recognition over time?​

How Career Development Will Evolve

Instead of learning by doing everything from scratch, junior-level team members can now grow by reviewing AI outputs, identifying gaps and refining results, which requires understanding how and why something works.

This moves them earlier into roles that involve evaluation, decision-making and responsibility for the outcome.

For example, a junior working in Google Sheets previously built reports from scratch and learned by writing formulas and cleaning data, while now they can generate the report faster but must review it, check the logic and spot errors, which requires understanding how things work.​

​At the same time, I acknowledge this shift also makes it harder for juniors to get started. Many of the tasks that once justified hiring someone with no experience are now automated, which means fewer entry-level roles and higher expectations from day one.​

Where To Help Junior-Level Employees Get Started With AI

Since junior-level employees will often lack the contextual background necessary to oversee AI, leaders now play a major role in guiding them to develop these skills.

The most effective way to build this capability is to start with a single, low-stakes workflow and make the learning visible. For instance, ask them to pick a simple, repetitive or frustrating task in their role, and then teach them how to use AI to improve it. This can be drafting emails, summarizing documents or preparing reports. A general tool like Claude, Gemini or ChatGPT is often enough to get started.​

If they have found emails to be particularly frustrating, ask them to use AI to say what they want to say in their own words. Importantly, explain what context is necessary to provide (e.g., who the email is for and what they want to communicate) before they draft the message. The most important skill to help them build is not to expect a perfect answer from the first prompt, as the quality comes from follow-up instructions.

Another practical habit that's helpful for everyone, but especially those in early in their career, is to ask the AI to clarify requirements before it starts. This can reduce guesswork and often surfaces constraints or edge cases that they might not consider.

Finally, teach your team to be as specific as possible with AI. Instead of a vague instruction like “help me with a presentation,” something like, “I need help preparing slides for my first team update. Ask me questions to understand the audience, what I’ve worked on and what I should highlight,” can help avoid some of the issues where AI does what it's supposed to do but in the wrong way.

Over time, this approach builds the skills necessary to direct, evaluate and improve AI outputs.​​

​Conclusion

AI isn't likely to replace most professionals, but it is already changing how work is done by shifting value from execution to judgment, direction and accountability.

Those who adapt by learning to work with it rather than against it will be able to do more, move faster and take on higher-value work.​​​​

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