How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI
, someone at work brings up a version of this question: is AI going to take my job? I’ll admit that I have asked some version of that same question myself. But having talked to the AI experts, the creators of some of these AI agents, having seen the evolution of AI, and after actually integrating AI into how I work, the question if AI is going to take my job no longer scares me. I am just more curious and a lot more deliberate about what I spend my time learning.
When I started my analytics job in 2021, I thought writing SQL or Python code and building dashboards were valuable skills, and they really were. I quickly realized that translating a messy business problem into a data problem, and then surfacing insights that actually made sense to people is the real skill I should hone on. But now with the AI boom, I don’t know for how long I can even call that my strength.
When ChatGPT became a household conversation in 2022, I had a feeling that AI is overrated in the short-term and underrated in the long term and I feel this has become more and more true.
The industry is moving faster than most of us can admit, and not even the people building these systems know exactly where it’s heading.
AI tools are getting better every month at absorbing the kind of knowledge that used to live only in the heads of senior people, like the business context you’d normally only pick up after several years on the job. When that knowledge gets documented and handed to an AI system, it becomes available to anyone who needs it, rather than residing in the heads of the subject matter experts.
When tribal knowledge gets written down, the lines between roles blur.
A data analyst is expected to take on a data engineer’s scope. A software engineer can interpret an A/B test result—a task that used to sit squarely with a data scientist. With the help of AI agents, someone with no technical background at all can produce a dashboard that, five years ago, would have taken a trained analyst a full afternoon.
I watched this happen so closely just last week: a scrum master needed to combine project delivery data from two platforms and, with help from Copilot, he was able to design a data pipeline and build a working Power BI dashboard without relying on a data analyst for the foundational work. By the time I was brought in, he only needed help automating the process and enhancing the storytelling. This could be a normal Tuesday for anyone but for me, it was a reminder that AI is rapidly blurring the lines between roles, making many technical skills broadly accessible.
None of this means analytics is going away. It simply indicates that the barriers to execution are falling down and our value will increasingly come from judgment, context, influence, and the ability to turn information into meaningful decisions.
My educated guess is that in the next five years, the straight line career progression from data analyst to senior analyst to principal analyst may not exist in the shape we know it today. The traditional entry-level role of writing queries, building dashboards, running reports probably will demand much more than that. What we will see instead are hybrid roles, sitting at the intersection of AI, business, data analytics, and software engineering.
I can’t pretend to know exactly what that looks like yet. Nobody does. But based on how I see things, here what I’m actually doing today to ensure that my analytics doesn’t get eaten by AI
- I’ve stopped treating query-writing, chart-building, and report-generating as my entire value proposition. AI is enabling a lot of people to do that work themselves, without needing me in the process. If that’s all I offer, I’m quietly competing with the tool instead of using it. With that understanding, I’m working to grow myself even more at the intersection of business knowledge, analytical judgment, and AI system design.
- I’m trying to understand how the systems actually work: how AI agents reason, how to structure context for them, how to build the connective tissue between AI and my data. This will soon no longer be a nice-to-have knowledge, but a staple in an analyst toolkit.
- Double down on the judgment AI still struggles to replicate for things like:
- Knowing when AI is quietly lying to you by making up insights
- Spotting survivorship bias before it shapes a decision
- Holding the line between correlation and causation
- Catching your own confirmation bias before it catches you
- Telling the difference between an observation and an actual insight
- Negotiating what a metric should even mean in the first place, before I start measuring it
- I’m also continuing to build on human skills. I love to read about cognitive science and how humans adapt to change, and I’ve learned that human (soft) skills don’t get commoditized the way a SQL query does. They require sitting with ambiguity, understanding a business well enough to know what a number should look like before you’ve even seen it. Also, hard skills get you the job but soft skills get you the promotion, so that’s where I’m putting a lot of my energy right now.
- I’m trying to build a strong sense of judgment into systems that scale, rather than keeping it locked away in your own head, you end up with something genuinely valuable.
- I’ve started using AI agents across three levels of work: execution, optics, and impact. With the correct prompting, I am trying to get AI to accelerate execution by automating research, analysis, and content creation, while improving optics by turning work into clear, compelling narratives for stakeholders. The result of this effort has allowed me to effectively communicate the business impact and provide better visibility into the value being created.
Looking Back, Looking Forward
Five years ago, I thought being good at an analytics job meant being good with data. But today, I think being good at this job means being good at judgment. It is largely about asking the right questions, knowing when a number is telling the truth and when it isn’t, and knowing which parts of a problem actually need a human in the loop.
The tools we use in data science and analytics have changed repeatedly over the years, and I won’t be surprised if the pace of that change accelerates with AI. But the real value of an analyst was never the SQL query itself; it was in understanding the business problem, building trust, and giving decision-makers the confidence to act. As AI takes on more of the technical work, the distinctly human skills of judgment, context, communication, influence, and empathy will become more important than ever. Those are the skills that I’m betting my career on.
That’s it from my end on this blog post. Thank you for reading! I hope you found it an interesting read!
Rashi is a data wiz from Chicago who loves to analyze data and create data stories to communicate insights. She’s a full-time senior healthcare analytics consultant and likes to write blogs about data on weekends with a cup of coffee.
