Will Data Analysts Be Replaced by AI? What Students Need to Know in 2026

TL;DR

• Will data analysts be replaced by AI? Not wholesale, but the job is transforming: AI now writes SQL and builds standard reports, while BLS still projects 35% growth (2025 to 2035) for data scientists, the closest official occupation.

• The most exposed work is the classic junior stuff: pulling numbers, refreshing dashboards, rebuilding the same weekly report.

• The work that's growing is defining metrics, building data models people trust, checking AI output and explaining what a number means.

• BLS has no "data analyst" category, so be skeptical of any single "data analyst growth" figure you see online.

• Your move now: get good enough at SQL to catch AI mistakes, and do one project where a real stakeholder used your analysis.

Externships are short, remote professional experience programs where you work on real projects for real companies, with guidance from people in the field. For analyst skills that hold up next to AI, look at the SQL & Database Architecture Externship with Breaking Games, the TruBridge Healthcare Data Analytics Externship or the ARCeH Data Analytics, Health Outcomes Externship. Explore all Externships.

Will AI Replace Data Analysts?

AI won't replace data analysts as a profession, but it's already taking over a big chunk of what junior analysts used to spend their week on. Our verdict is Transforming: demand for people who can define, model and explain data is holding up, while pure report-building work is shrinking.

The Short Answer

Data analysts aren't being replaced by AI, but the entry-level version of the job is being rewritten. Tools like Microsoft's Power BI Copilot now answer business users' questions directly, which eats into the "can you pull this number for me?" requests that used to fill a first-year analyst's calendar. At the same time, the Bureau of Labor Statistics projects that data scientists, the closest official occupation (it includes business intelligence analysts), will grow 35% between 2025 and 2035. Jobs overall? About 3.5%.

Having sat down one-on-one with thousands of students exploring data careers, we hear one worry more than any other: "Is it too late to get into data?" It isn't.

But the version of the job you prepare for matters a lot more than it did three years ago.

The AI Exposure Scorecard

We score every field in this series on the same five rows, so you can compare them. Here's where data analysts land.

Notice that BLS puts every occupation covering analyst work in its "Very high" exposure group. Scary? Less than it looks. BLS says plainly in its methodology note that the category isn't a job-loss forecast, and nearly every office job in this series lands in the same group. The useful signal sits in the projections and in which specific tasks are getting automated.

If you're leaning toward the modeling and machine learning side of data, that has its own answer. We cover it in Will Data Scientists Be Replaced by AI?

What Parts of Data Analysis Is AI Already Doing?

AI already handles the query-and-report layer of analytics: turning plain-English questions into SQL, drafting report pages, writing formulas and summarizing results. What it can't do reliably yet is know whether the data underneath is right, or whether anyone asked the right question.

Task by Task: What AI Does and What Still Needs You

The easiest way to see the shift is task by task. Each row below is a piece of a typical analyst week, what AI tools do with it today, and the part that still lands on a person.

Look at the right-hand column. Almost every item is about trust: which table is correct, what a metric means, whether a number should go in front of a vice president. Microsoft's own Copilot for Power BI overview (updated August 2026) describes Copilot's report tasks as work similar to what you'd ask your analyst to do. It also warns that without a prepared semantic model, Copilot "can misinterpret the data and return generic or inaccurate results."

That warning? It's basically the job description now.

Real Companies Using AI for Analyst Work

Uber is the clearest public example. In a September 2024 engineering post, Uber described QueryGPT, an internal tool that turns plain-English questions into SQL:

• Uber's data platform runs about 1.2 million interactive queries a month, and about 36% of them come from operations staff.

• The goal was to cut a query that took about 10 minutes to write down to about 3.

• Among roughly 300 daily users, 78% said the tool cut their time.

Those are 2024 numbers, and the tool has likely changed since.

So what does that mean for you? When a company makes query writing that much faster for people outside the data team, fewer quick questions ever reach the analysts. What's left for analysts is the harder part: the trusted tables, the definitions, and the "golden" reference queries Uber used to test whether QueryGPT's output was right.

What Does the Job Data Actually Say?

The data is mixed, and anyone telling you it clearly points one way is overselling. Projections for the closest occupations are strong and live data analyst postings rose through fall 2026 in our tracking, but entry-level postings are a shrinking share of hiring in AI-exposed fields.

The BLS Outlook and Its Big Caveat

Here's the caveat first: BLS doesn't track "data analyst" as an occupation. Business intelligence analysts are counted inside data scientists, and plenty of analyst titles land in market research or management analyst codes instead. So when a website quotes a single growth number for data analysts, ask where it came from.

With that in mind, these are the closest codes, per the BLS Occupational Outlook Handbook (2025-35 projections, May 2025 pay):

• Data scientists (includes BI analysts): +35%, about 24,800 openings a year, median pay $120,230 (BLS).

• Operations research analysts: +12%, median pay $88,940.

• Market research analysts and marketing specialists: +7%, median pay $78,760.

One detail surprised us. On its data scientists page, BLS treats AI as a reason firms will need more data scientists, because companies keep building AI into their workflows. That's the opposite of how BLS describes computer programmers.

From Extern's Data: Postings, Employers and Pay

Extern tracks job postings daily, and the analyst numbers moved up this fall. In US job-posting counts Extern tracks daily (Adzuna, July to October 2026), live "data analyst" postings went from about 16,800 to about 19,100 (monthly averages). That's not what a collapsing field looks like.

On the internship side, the employers posting the most data internships in Extern's posting tracker (Aug to Nov 2026) were American Express, Procter & Gamble, MSD, Booz Allen Hamilton, PwC, Xcel Energy and FedEx, out of 1,112 listings from 365 employers. These are employers in the job market, not Extern partners. If two of those names interest you, our FedEx internship guide and Xcel Energy internship guide cover programs and deadlines.

But pay is thinner data. Only 15 of those listings published a wage, but among them the median employer-published hourly pay for internships was $27.50, with the middle half between $22.50 and $39.50. Treat it as a rough marker, not a promise.

The Entry-Level Warning Sign

Now the less comfortable part. Indeed Hiring Lab reported in September 2026 that in the most AI-exposed occupations, the entry-level share of job postings fell from 29% in 2021 to 10% in 2026, while the senior share climbed from 22% to 47%. Indeed calls the split suggestive, and it only covers postings that list a salary. Still, that's a steep tilt.

And across all jobs, Revelio Labs counts US entry-level postings down about 35% since early 2023. Neither source breaks out data analysts, and we couldn't find a primary source that does, so the evidence for analysts specifically is indirect.

The degree data is steadier. In the New York Fed's college labor market data (2024 figures for ages 22 to 27), business analytics majors had 5.0% unemployment, 28.7% underemployment and a $72,000 early-career median wage. Comparing majors more broadly? Our ranking of the best paying AI-proof majors is a good next stop.

Which Data Analyst Jobs Are Safest, and Which Are Most at Risk?

The closer a role sits to "produce the number," the more exposed it is. The closer it sits to "decide what the number means and make it trustworthy," the safer it looks. Here's how five common analyst paths compare.

The pattern is consistent across the table: producing reports is automating; defining metrics, building trusted data models and deciding what a number means aren't. An ad hoc SQL analyst and an analytics engineer might use the same tools, but only one of them is the person who decides what "revenue" means in the company's data.

Two related reads if you're weighing options. If your target title is business analyst, the story is similar but not identical, and we cover it in Will Business Analysts Be Replaced by AI? And for a cross-field view of which careers hold up best, see our guide to jobs AI won't replace.

How Is AI Changing Entry-Level Data Analyst Work?

The first-year analyst job is moving from building reports to checking, modeling and explaining them. Employers still hire juniors, but they expect them to use AI tools from day one and to notice when those tools get something wrong.

Then vs Now: A First-Year Analyst's Week

Then (a few years ago): a typical junior analyst week was built around requests. Pull last quarter's numbers for marketing. Refresh the Monday dashboard. Rebuild the monthly deck because someone changed a filter. Useful work, honestly, and a decent way to learn how the business runs.

Now: a lot of those requests never arrive, because the person asking can type the question into a BI tool. What lands on the junior analyst instead is the layer under the tool. Preparing the semantic model so Copilot doesn't misread it. Writing down what each metric means. Testing that numbers match across reports. Reviewing an AI-drafted query before it ships.

The dbt Labs 2026 State of Analytics Engineering survey (363 respondents, April 2026) shows where teams' heads are. "Trust in data" as a priority jumped from 66% to 83%, and 71% named hallucinated or incorrect output reaching stakeholders as a top concern.

And yes, we said earlier the projections look strong. They do, for the field as a whole. For the specific job of "person who builds the weekly report," the picture is weaker, and it's worth being honest about that.

What Employers Expect on Day One

According to NACE's spring 2026 employer survey, more than one-third of entry-level jobs now require AI skills, nearly triple the share from fall 2025. Just over a quarter of employers say AI has reduced the need for entry-level tasks. More than half say it hasn't.

So AI fluency is now the floor, not a bonus. But fluency isn't blind trust. In a pre-registered public-sector field experiment (Fitzpatrick et al., February 2025), the group using generative AI on a data analysis task produced answers whose quality was 12% lower than the control group's, with no significant time savings. It's one small study in one setting, and we wouldn't stretch it far. Honestly, nobody has great data yet on how AI changes analyst quality at scale.

But it does tell you why employers want juniors who check. An analyst who can say "Copilot's answer was off because the date table wasn't joined right" is worth more than one who pastes the answer into a slide.

What Skills Make a Data Analyst Hard to Replace?

The skills that hold up are the ones AI tools depend on: clean data models, clear metric definitions, sound statistics and the judgment to explain a result. Each one below comes with a way to prove it on a resume, because a skill nobody can see doesn't help you get hired.

1. SQL, plus the habit of checking AI-written SQL. Proof: a GitHub repo of queries on public data with a short "how I validated this" note under each one.

2. Data modeling and metric definitions. Proof: a dbt project with tests and documented metrics (a star schema on open city data works well), plus the free dbt Fundamentals course badge.

3. Statistics and experiment basics. Proof: an A/B test write-up that states its assumptions and what the data can't tell you.

4. BI tools and their AI features. Proof: a published Power BI or Tableau dashboard with notes on where the AI assistant's answer was wrong and why. Microsoft's PL-300 certification is the formal version.

5. Business framing and writing. Proof: a one-page recommendation memo written for a real stakeholder, with the decision it supported.

6. Python for analysis. Proof: a notebook project someone else can rerun from start to finish.

From the recruiting side (our author spent years in recruitment process outsourcing), the difference shows up fast on a resume. "Built a Tableau dashboard" reads like everyone else. "Found and fixed a metric definition that made two dashboards disagree" reads like someone a team would trust with its numbers.

So if you're starting from zero, a structured program helps with the basics in point six. Our Google Data Analytics Certificate review covers what the current version teaches and what you'll still need to add.

How Can You Get AI-Ready Data Analytics Experience as a Student?

Three moves cover most of it: apply for internships early, get at least one project with a real company's data, and pick one credential instead of collecting five. Here's how each works right now, in November 2026.

Internships: Where to Look and When

Summer 2027 data internships started opening in late summer and fall 2026, and many review applications on a rolling basis, so applying in November still beats waiting until spring. Our data analytics internships guide lays out timelines and company lists by industry. The employers named in our posting data above are a good place to start. And if you have no experience yet, the data analyst internship guide walks through how to get a first yes.

Externships With Real Company Data

Personal projects show you can do the work. A project for a real company shows you can do it with a stakeholder, a deadline and messy data you didn't choose. That second kind is what the "experience required" line on postings is really asking for.

Externships run 4 to 11 weeks (most often 8) at about 5 to 10 hours a week, remotely, with an extern manager guiding you. Three open November 2026 cohorts fit analyst skills well:

• The SQL & Database Architecture Externship with Breaking Games, for database design and SQL, the layer AI tools depend on.

• The TruBridge Healthcare Data Analytics Externship, for analytics on healthcare data, where getting definitions wrong has real consequences.

• The ARCeH Data Analytics, Health Outcomes Externship, for turning public health data into findings someone can act on.

Data analytics is also the field students most want to explore next in Extern's post-program survey (21.1% of responses, June 2026). In an internal Extern review from April 2025, 77% of submitted project work met or exceeded expectations. One student who stacked experience this way is Giles Nitunga from Portland State, who completed three Externships, including consumer insights and data analysis with Beats by Dre, and went on to work as a data analyst, then an account manager. You can browse current Externships to see what else is open.

Pick One Credential, Not Five

One recognized credential is enough to signal the basics. For beginners that's usually the Google Data Analytics Certificate; for people already working in Power BI, it's Microsoft's PL-300. After that, every extra hour goes further on projects than on another badge. Recruiters read projects. They skim certificate lists.

Extern is not affiliated with, sponsored by or endorsed by any company, firm or organization named here. This article is based on publicly available information as of November 2026. Job data and company plans change, so check the original sources linked above.

Frequently Asked Questions

Will AI replace data analysts in the next 10 years?

Probably not as a profession, though the job will look different by 2035. BLS projects 35% growth for data scientists, the closest official category, between 2025 and 2035. Expect fewer roles built only around running reports and more built around data modeling, validation and advising the people who make decisions.

Is data analytics still a good career in 2026?

Yes, if you aim past report-building. Business analytics majors had 5.0% unemployment and a $72,000 early-career median wage in the New York Fed's 2024 data. The catch is the way in: you'll need proof that you can model data and check AI output, not only make charts.

Can ChatGPT do data analysis?

It can handle a lot of the mechanics: writing SQL or Python, cleaning a file, making a chart, summarizing results. What it can't tell you is whether your data is complete, whether a join quietly dropped rows, or whether you asked the right question. Treat it as a fast assistant whose work you always check.

Are entry-level data analyst jobs at risk?

Some are. Roles built mostly on pulling numbers and refreshing dashboards are the most exposed, because business users can now ask AI tools directly. No primary source tracks junior analyst hiring on its own yet, so the evidence is indirect. Entry roles centered on data quality and modeling look steadier.

Is the Google Data Analytics Certificate still worth it with AI?

For beginners, it's still a low-cost way to learn spreadsheets, SQL, Tableau and Python, and the current version adds some AI tools. It won't stand out on its own, though. Pair it with original projects where you validate results and explain a decision, which is the part AI can't fake for you.

What skills do data analysts need to stay relevant with AI?

Strong SQL, data modeling, basic statistics and clear business writing top the list. Add hands-on use of the AI features in Power BI or Tableau, plus the habit of writing down how you checked an answer. Employers now ask for AI skills on many entry postings, but judgment is what keeps you hired.

Will AI replace business analysts?

Not entirely, and the pressure looks a lot like it does for data analysts: drafting and routine reporting move to AI, while requirements, process and stakeholder work stay with people. BLS has no business analyst code either, so you have to read proxies like management analysts. Our business analysts guide breaks it down.