Are Entry-Level Data Analyst Jobs in Danger? The AI Tools Changing Everything

AI tools are replacing the drudgery of data work. Simple questions, spreadsheet tidying, and basic charting – tasks that were once performed by entry-level analysts – can now be done by ChatGPT, Microsoft Copilot, Google's Gemini and others. Does that kill entry-level analyst jobs? In real life, AI is automating some of the jobs but creating new ones. In 2025, data cleaning, query writing, reporting, and visualization are supported by AI-powered platforms. We analyzed real tools and user opinion in order to see how the function is changing.

AI That Cleans Your Data in Seconds – No More Manual Fixes

Most of the day most data analysts is devoted to cleaning and prepping data. The new Google AI tools can do much of that "janitor" work for them. Google put its large AI model Gemini into Google Sheets. Users can now click on a Gemini icon and give the "create a heatmap of sales by region" or "find correlations in this data" instructions. Behind the scenes, Gemini even writes and runs Python code on your Sheet, translating English commands into charts or analysis. Similarly, a plug-in called Numerous inserts into Excel or Sheets and uses GPT-based AI to automate cleaning tasks – it can remove duplicates, fix formatting, split columns, and so on, all based on a simple command.

On the enterprise side, tools like Trifacta (currently Alteryx Designer Cloud) and Alteryx Designer use AI to suggest data transformations. They are graphical interfaces where users can clean and combine data without coding. When used, they greatly speed up prep work.

One reviewer of Alteryx explains:

His team used it to combine payroll and survey data; a job that took days in Excel was automated, enabling them to "eliminate repetitive manual work" and handle structured and unstructured data seamlessly.

Another user stated:

Alteryx "significantly improved efficiency across multiple departments" and enabled workflows to be shared among groups. (As someone puts it cynically, Alteryx is capable but "expensive" – it’s a paid enterprise product.)

All of this matters because AI is now able to perform chores of cleaning that previously occupied most of an analyst's time. In fact, one industry report estimates that data preparation – long 50–80% of an analyst's job – is rapidly being performed by automated AI wrangling software. That frees junior analysts up to do more interpretation and less dull stuff. But it also means that those who learned to do manual cleaning only may need to adapt.

Just Ask, Get SQL: How AI Writes Queries and Analyzes for You

Generative AI is also entering dashboards and reports. OpenAI's chatbot ChatGPT is now a sought-after query-builder: you can type in a dataset or describe your data, and it generates SQL or Python for you.

Capterra customers comment:

ChatGPT "gives a real-time response" and is "24/7 available" – making research faster and often easier than traditional searching.

A user said:

It "makes finding knowledge easier" and that it is "easier than searching on my own." (Aside: ChatGPT does have a free model, with paid "Plus" and "Pro" models for extra abilities.)

Essentially, entry-level analysts will write questions using ChatGPT to draft queries, summarize findings, or generate pivot tables, and reviews say:

'It's saving hours.'

Microsoft has done similarly with Power BI Copilot and Excel. The new Office Copilot (formerly known as "Microsoft 365 Copilot") can interpret your command as data actions. For example, in Excel Copilot you can ask it for a forecast or a pivot-table summary, and it "can format data, create graphs, generate pivot tables and guide users through creating new formulas and macros". It even uses Python behind the scenes for advanced analysis. Such capabilities are part of Microsoft's Copilot paid subscription (~$30/user/month), but there is a free trial or the regular Excel remains free. Since it is too new for many user reviews, tech watchers note that this means basic analytics (like ad-hoc pivot tables and charts) can be done by AI prompts.

Natural-language analytics tools have come along as well. ThoughtSpot, for instance, allows users to type English queries (such as "top customers by region") and directly get charts and insights. (It's billed as "AI-driven analytics" with search-based querying.)

Reviewers indicate:

ThoughtSpot and similar software can create helpful charts on the fly, although they're enterprise-level products (generally requiring a paid license).

The bottom line:

AI assistants are now built into most query/reporting tools, minimizing the need for manual writing of simple SQL or manual summaries.

Instant Dashboards: AI That Builds Reports While You Watch

Even generating queries and basic analysis can be AI-assisted. Domo, an enterprise intelligence platform, features AI "Samurai" and Copilot native capabilities that aid in analysis and building dashboards. Users like Domo for accelerating dashboard development for individuals who are not developers.

In a review at G2, one Domo customer described:

The product enables them to "design dashboards to answer questions on the fly in real time" and noted the addition of AI has made "creating calculated fields less time consuming and reachable for non programmers".

A second user quoted:

Domo's real-time data is "a game changer," referencing the time saved provided by its use of AI. (Domo is not free, though there are free trials, targeted at businesses; it's subscription-based.)

Tableau has also included AI (like "Ask Data" and their new "Tableau Pulse" assistant) to offer suggestions on visualizations and even answer simple questions. Tableau has been praised by users for years for its ease and solid visuals.

A 2024 Capterra reviewer awarded Tableau 5/5 stars and described it as:

"Very suitable" for data visualization, "easy to use," and an "industry standard," with the ability to handle large datasets. (Tableau Desktop is a paid product – approximately $42–59/user/month subscription for full Creator functionality – though Tableau Public exists free of charge and students/instructors can have a free academic license.)

In practice, the AI of Tableau might suggest chart types or natural-language explanations, but the underlying user feedback is that it's still the analyst who designs out the end dashboard.

Microsoft's Power BI also features natural-language Q&A (enter a question to get a chart) and will support integration with Copilot. While we don't have customer testimonials specifically related to Power BI, observers note that BI software from all vendors (Google, Microsoft, Tableau, etc.) is adding AI chat or recommendation assistants. These features suggest that some generic report construction – typing SQL, choosing visualization types – can be automated. Yet human analysts must still refine results, spin yarns, and flag errors.

Human vs Machine: What Skills Still Belong to You?

So is AI replacing entry-level analysts? Not in bulk, but it's altering their work. AI excels at boring, mechanical work (data cleaning, plain query, routine charting).

As one article explains:

It used to take 50–80% of an analyst’s day (like wrangling messy data) can now be handled by “automated analytic agents”.

That certainly does threaten old-school entry-level work that's all about executing scripts or cranking out boilerplate reports. But it frees up analysts to spend more time on high-value work. What AI cannot do is understand business context, challenge the integrity of the data, or replace human imagination. Early evidence is that the analysts who thrive will be those who combine technical competence with business acumen.

One recent industry report explained it this way:

As machines build and validate models, today's analysts must become "model stewards and explainers," translating AI output into actionable insight.

The report determined:

The professionals who thrive will be the ones who combine technical acumen with business domain knowledge and the ability to question AI-driven results.

That is, AI is a wonderful helper, but business companies still need human assistance to make sure conclusions are insightful and aligned with business needs. In fact, most users of such AI tools convey just that. Even enthusiastic reviewers like those above note downsides – ChatGPT can give incorrect answers on rare occasions, complex tools like Tableau Prep or Alteryx still have learning curves, and most AI dashboards still rely on a human guiding the process. Analysts also worry about data privacy, bias and the need to verify AI’s suggestions.

The bottom line:

In 2025, AI is augmenting entry-level analysis to a greater extent than replacing it en masse. Tasks like data tidying, generating boring reports, and creating visualizations are less time-consuming and partly automated. But companies still need to have analysts set the proper questions, interpret the results of AI, and report out.

As one of the users summed it up:

Tools such as Alteryx and Domo have "enabled us to design dashboards to answer questions on the fly," and made life easier. But that does not remove human – it moves where humans generate value.

How to Be the Analyst AI Can’t Replace

If you are a entry-level analyst or would like to be one, how do you keep up?

Here are some tips:

- Master the new tools

Experiment with AI capabilities in Excel, Power BI, Tableau, and others. Knowing how to take advantage of Copilots and chatbots will make you more productive.

- Practice business acumen

AI can process numbers, but only a human would know to question whether or not the numbers are significant. Establish your domain knowledge (marketing, finance, healthcare, etc.) so you can identify garbage data, ask the right questions, and deliver a great story.

- Build soft skills

Improve communication, visualization, and critical thinking skills. Companies will value analysts who do not just produce charts, but also present results concisely to non-technical audiences.

In brief:

AI is something that can make the drudge work automatic but needs humans to steer it. AI-supported analysts who use automation to handle routine work, and then concentrate on insight, interpretation and strategy, will remain in demand. Based on one industry handbook, analysts should not "bet on the future of manual data prep" – instead, utilize AI to work smarter and move into more strategic roles.