An analyst I know went through three days of number-crunching for a churn report last spring. Half a year later, an AI system managed to do the same task in around four minutes. Understandably enough, she freaked out. Then she got promoted.
This is usually the part of the story that the media doesn’t care about. Of course, AI can manage to process data quicker than anyone has ever done before. And of course, this didn’t mean that the job is over with.
Let’s try to understand what exactly is going on.
What the Hiring Data Actually Shows
Here’s what blows people away: there isn’t less demand for data analysts. It’s increasing. Recent data on hiring for 2026 from a number of leading staffing companies has put data analysts and data scientists at the top of growth areas in tech, with postings increasing significantly year over year. Companies aren’t downsizing analyst jobs; they’re changing expectations about them.
A recent analysis of 700+ data science job postings found that more than 60% now require some kind of AI capability within the job description, not an “AI person,” but the job itself.
So the honest read to this is that it isn’t disappearing. Its job description has quietly rewritten itself.
The Work That Actually Got Automated
It would be good to state specifically what has been altered because generalizations will be of no use for career planning.
Modern AI systems perform, in an effective manner and quickly:
- Data cleaning and standardization in a messy spreadsheet
- Generation of SQL queries in response to the text of the request
- Creation of draft charts and dashboards
- Data summarizing in a few sentences
- Identification of anomalies or outliers in a dataset
These things happen. If your entire job consists of those five tasks, you should start being worried because such a position is doomed to decrease sooner or later. This is the same situation that occurred with bookkeeping clerks with the advent of spreadsheet software.
However, there is something that AI is not capable of performing and this aspect is important. AI cannot answer the question on its own. It cannot be in the room with a doubtful VP and explain why the numbers that he is seeing do not look correct. AI cannot notice that the spike in sales is caused by a mistake of the new intern who just started working. The context here is provided by humans.
So What Does the Job Look Like Now?
Surveys of people actually doing the job tell a different story altogether. Once we survey data scientists on what they actually do at work, it turns out that they spend not 65% of time on machine learning – as the job description suggests – but rather fifth of the time on data cleaning, fifth on discussing stuff and meeting and the rest on data collection, modeling and documentation. In other words, the job was never so purely technical, but rather had always a component of communicating and judging. AI just highlighted this communication and judgement by doing the rest.
There is one more thing the successful analysts have in common – they no longer compete with AI in terms of speed, but rather use it as a tool that gives them more time for thinking. They come up with better questions. They challenge requests when they do not make sense. They explain the numbers so that even non-technical stakeholder could make a decision based on the number.
It is quite a big job, but arguably, an interesting one.
Where This Is Heading — Agentic AI Changes the Picture Again
And here is where things get trickier, and where, I think, most of the “will AI replace X” pieces tend to fall flat. It’s not just AI doing its analysis job when told to do so. It’s the AI agents who work through the whole process independently — from data gathering and issue identification to report creation and follow-up action, without any need for manual input at each step.
Adoption of such agent-based systems by enterprises has been surprisingly rapid. By far the majority of large companies have adopted agent-based systems in production mode and consider agentic AI a strategic initiative rather than an experiment for CIOs.
What this implies: the analyst who knows how to work with the process and create, maintain and audit those agent workflows will definitely have a leg up on those who can produce reports by hand. And this is no longer a future-proof skill — we see it being demanded now, in addition to the analyst one.
If you’re already comfortable with the analytics side, this is worth taking seriously. Getting a working grasp of agentic systems through something like an Agentic AI Course puts you in a small group of analysts who can speak both languages — the data and the automation layer sitting on top of it. That combination is where the pay jumps tend to show up.
If You’re Just Starting Out
The straightforward nature of something which most career advice tiptoes around is that if you are new to the discipline then do not get to the advanced part right away simply because it seems future-proof. What you will need is the basics: SQL, the logic of spreadsheets, statistics and structuring of a business question prior to any data handling. This attempt to get into the “futuristic” AI aspect of things almost always ends in failure because you won’t be able to verify the results provided by the tool.
A structured Data Analyst Course is a reasonable way to build that foundation properly, rather than piecing it together from scattered YouTube videos and hoping it adds up. Once the fundamentals are solid, layering AI and automation skills on top gets a lot easier.
The Bottom Line
Is AI going to take over the role of the data analyst? Well, wrong question actually. What we should be asking ourselves is – is AI going to take over analysts who are capable of little more than running a report?
That’s probably going to happen in time, and most likely so. But will AI take over analysts who are capable of framing questions well, spotting poor assumptions and are also familiar with AI agents? Probably not for quite some time to come.
And, if anything, such people have become even more rare than ever before, and businesses are aware of it.
