Worried About Being Replaced by AI?

Here are the top 3 data jobs that are at the least risk of AI replacement.

A lot of data jobs are at risk.

The unfortunate reality of AI is that if your work is repetitive, reactive, or easy to describe in a prompt, it will eventually be automated.

The above sentence might feel easy to shrug and say “nah, my work is not repetitive or easy to describe in one prompt! I am safe!” but I urge to you take a very critical look at what your role entails and evaluate how future-proof your job is.

It might even be the case that it is not currently automatable (especially with a single prompt), but if your job could be theoretically automated by a roving gang of AI agents, now is the time to be so so fr with yourself.

Roles at Risk

This is not an exhaustive list, but when I look at the data landscape, here are the types of roles I see most at risk:

Data Analysts

If you’re a data analyst whose job revolves around report building, building simple dashboards, and templated analysis, I am sorry but your job is at risk.

When your work is answering the same set of questions with slight variations, AI can do it faster and at scale. In fact, I am seeing more and more teams adopting tools that generate charts and insights automatically. Self-service analytics is real and it is here. Copilot tools can write SQL, clean data, and visualize results in one step.

Data Engineers

If you are a data engineer building pipelines that follow the same logic every time, you are at risk.

Modern data stack tools are becoming low code, and AI copilots can now generate Airflow DAGs and dbt models with minimal input. So if your work is reactive and focused only on moving data from one place to another, AI will probably replace you. Again, I am seeing teams already leveraging AI to automate data cleaning, simple schema mapping, simple data monitoring, and documentation generation.

Data Scientists

Machine learning roles focused on repetitive tasks, shallow modeling, or generic applications are at risk.

If you are training churn models or running basic classifiers on public datasets without thinking about feedback loops or explainability, your work is already being done by foundation models and AutoML.

The Bigger Picture for the Future

There is a category of work that AI depends on and still cannot do…

The infrastructure that makes AI systems usable, accountable, and safe.

Think of all of the people it takes just to make AI work. Someone has to make sure it has access to the data, that the data is legally compliant and stored properly, that the metadata exists to explain to the model WTF it’s even looking at, and someone needs to wrangle the AI when it starts hallucinating and spouting off B.S.

What we still need are humans in the loop in the layer beneath the models.

Humans need to manage what goes into the model and humans need to evaluate what comes out.

Roles not at Risk