someone at work brings up a version of this question: will AI take my job? I will admit that I have asked another version of that question myself. But after talking to AI experts, the creators of some of these AI agents, they've seen the evolution of AI, and after integrating AI into the way I work, the question if AI is going to take my job it doesn't scare me anymore. I want to know more and I am more intentional about what I spend my time reading.
When I started my analytics career in 2021, I thought writing SQL or Python code and building dashboards were essential skills, and they really were. I quickly realized that translating a complex business problem into a data problem, and presenting meaningful insights to people is a real skill to hone. But now with the AI boom, I don't know how long I can call that in my power.
When ChatGPT became the talk of the house in 2022, I felt that AI was overrated in the short term and underrated in the long term and I feel this is even more true.
The industry is moving faster than most of us would agree, and the people who build these systems know exactly where it's headed.
AI tools are getting better every month at absorbing the kind of information that used to live only in the heads of adults, like the business situations you often find after a few years on the job. When that information is recorded and transmitted to an AI system, it is available to anyone who needs it, rather than sitting in the heads of subject matter experts.
When tribal knowledge is written down, the lines between roles blur.
A data analyst is expected to take the scope of a data engineer. A software engineer can interpret the results of an A/B test—a task that used to sit squarely with a data scientist. With the help of AI agents, a person with absolutely no technical background can produce a dashboard that, five years ago, would have taken a trained analyst an entire afternoon.
I watched this happen very closely last week: a scrum master needed to integrate project delivery data from two platforms and, with help from Copilot, he was able to design a data pipeline and create a functional Power BI dashboard without relying on a basic work data analyst. When I was brought in, he only needed help automating the process and improving 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 more widely accessible.
None of this means that the statistics go away. It just goes to show that the barriers to execution are falling and our value will increase exponentially in judgment, context, influence, and the ability to turn information into meaningful decisions.
My educated guess is that in the next five years, the direct career progression from data analyst to senior analyst to principal analyst may not exist as we know it today. A typical entry-level role of writing queries, building dashboards, running reports will likely require 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. There is no one. But based on the way I see things, here what I'm doing today is to make sure my stats aren't eaten by AI
- I stopped treating question-writing, charting, and report generation as my value proposition. AI allows more people to do that work themselves, without needing me in the process. If that's all I have to offer, I'm silently competing with the tool instead of using it. With that understanding, I work grow up especially for me at the intersection of business intelligence, analytical judgment, and AI system design.
- I'm trying understand how the systems actually work: how AI agents think, how they can organize context, how they can create connective tissue between AI and my data. This will soon no longer be a nice-to-have, but a staple in the analyst's toolkit.
- Twice down to the AI judgment it still struggles to replicate with things like:
- Know when AI is silently lying to you by making up details
- Recognizing survival bias before it affects decision making
- Toeing the line between correlation and causation
- Discovering your confirmation bias before it catches up with you
- Telling the difference between observation and real understanding
- Discussing what the metric should mean in the first place, before I start measuring it
- I also continue to build on human skills. I love learning about cognitive science and how people adapt to change, and I've learned that human (soft) skills aren't sold the way SQL queries are made. They need to stay ambiguous, understand the business well enough to know what the number should look like before you see it. Also, hard skills get you a job but soft skills get you a promotion, so that's where I'm putting most of my energy right now.
- I am trying to build a a strong sense of judgment in systems that scale, rather than keeping it locked away in your head, you end up with something of real value.
- I have started using AI agents at three levels of work: to be killed, optics, again impact. With the right information, I try to make AI faster by automating research, analysis, and content creation, while improving optics by turning work into clear, compelling stories for stakeholders. The result of this effort has allowed me to effectively communicate business impact and provide better visibility into the value being created.
Looking Back, Looking Forward
Five years ago, I thought being good at analytics meant being good with data. But today, I think that being good at this job means being good at judging. It's mostly about asking the right questions, knowing when a number is telling the truth and when it's not, and knowing which parts of the problem need someone in the loop.
The tools we use in data science and analytics have changed exponentially over the years, and I wouldn't be surprised if the pace of that change accelerates with AI. But the real value of the parser was never the SQL query itself; it was about understanding the business problem, building trust, and giving decision makers the confidence to take action. As AI takes over most of the work of technology, the clear human skills of judgment, context, communication, influence, and empathy will be more important than ever. Those are the skills I bet my career on.
That's it from my end of this blog post. Thanks for reading! I hope you found it an interesting read!
Rashi is a data wiz from Chicago who loves analyzing data and creating data stories to communicate insights. He is a full-time healthcare analytics consultant and likes to blog about data on the weekends over a cup of coffee.
