might be one of the most important phases of our careers.
I am not saying this to be dramatic or clickbaity but because something subtle and irreversible is happening in the way I work. With each passing day, I find myself using AI more. I go less back and forth with it. I question it less because with increasing exchange, it has become directionally right enough most of the time.
My role is slowly moving from generating to validating.
These days, I am getting used to watching AI handle things before I work on things that I once thought required my expertise.
I often joke that I would never use ChatGPT for planning my travels. Travel planning is my playground. I love opening twenty tabs, comparing neighborhoods, reading reviews, and building an itinerary that feels just right. And yet, a week ago, I asked ChatGPT to walk me through everything a first-timer at Disney Parks should know. In seconds, I had notes of everything I should know and do, without opening any other tab.
That made me pause.
If AI can handle something I genuinely enjoy and take pride in… what does that mean for the rest of my work?
My Workflow Before AI
Not long ago, my work as an analytics consultant was long, nuanced and deeply tangible.
I would:
- Define the business problem
- Identify the right data sources
- Write code from scratch to clean messy datasets
- Manipulate and analyze the data
- Hit errors, debug for hours
- Search Stack Overflow, rewrite queries
- Explore edge cases
- Build stakeholder decks
- Translate technical outputs into business narratives
A lot of my value lived in executing this workflow.
Over time, I have worked to create a niche for myself to be able to translate data for the business and vice versa.
What It Looks Like Now
However, today, AI is often the first thing that touches my problem statements.
Initially, I was mostly about experimenting with the prompts. I would describe the business context, the schema, the limits, and the expected outcome, and I explored what AI could do for me. Now that I have seen the productivity boost, the articulation of some of my thoughts, I heavily rely on AI now to:
- Write end-to-end code for data cleaning, analysis, and visualization
- Suggest features and improve model performance
- Surface insights I hadn’t considered
- Document the entire process
- Generate executive summaries for different audiences
With that, AI has effectively become my first analyst.
And this did not happen overnight or even in a week. The subtle shift happened over months and now, if I have something that needs to get done, I’m naturally inclined to go to AI first, even before I even fully think it through myself and I find that both exciting and deeply unsettling.
Because this shift isn’t incremental. It’s exponential.
I fear that we are about to see AI replace more than one skill — coding, analysis, writing, and more. It’s not just getting better at one thing—it’s getting better at everything, all at once.
What This Really Means
AI is becoming a general layer for cognitive work.
I don’t know if AI will ever replicate deep human empathy or if trust built over years can be automated. And honestly, I don’t know where the ceiling is anymore.
But I do have a feeling that the people who will navigate this shift well are not the ones avoiding it but the ones leaning into it with curiosity.
So Where Do We Build an Edge?
I’ve been thinking about this a lot lately—when human intelligence gets normalized by artificial intelligence, how do I stay relevant? I do not want to end up watching my role slowly reshape itself without me reshaping my skills and toolkit too.
I’ve realized that the edge is becoming less visible.
In the past years, when I joined the workforce as an analyst, I thought that because I know SQL, I can build models, and I can clean messy data, I have an edge. These were tangible skills one could measure, improve, and showcase. However, a lot of that is slowly getting abstracted away. AI can do most of it fast, and increasingly well.
So the edge has to move somewhere else.
For me, it’s starting to feel like the edge is in how you think before you even open a tool.
And here’s how I am preparing to build that edge for the next few years to come as a senior analyst –
- Get hands-on with AI in your actual workflow:
I highly recommend starting to use AI seriously (not just searching itineraries and cleaning up your emails). The edge comes from leveraging AI for practical examples, not passive usage.- Don’t stop at “write me a query” or like a search engine. Use it for full problem cycles from data cleaning to analysis to storytelling with that data.
- Compare its output with yours and notice the gaps.
- Understand where AI works for you, and more importantly, where it doesn’t:
The real edge isn’t in just using AI. It’s knowing when not to rely on it. AI can generate answers, but you need to know when they’re wrong.- Always ask if the trend/pattern/insight that AI is suggesting makes sense? What’s missing? What’s biased?
- Pressure-test outputs with simple sanity checks.
- Be intentional about what you delegate
Let AI handle speed, structure, and first drafts for now as I get settled in this space, if not already. Next, move up to letting AI deal with problem framing, judgment, ethics, and accountability. But, don’t forget to validate.- Cross-check results with small samples, edge cases, or alternate queries.
- Don’t trust clean outputs blindly. Always verify those outputs.
- Prepare for your role to evolve.
We’re already moving from being query writers to prompt thinkers, data validators, and storytellers.- Go beyond “here’s what the data says” → “here’s what we should do next.”
- Tie analysis to business impact, not just accuracy.
This is where analysts start becoming decision partners - Build the habit of adapting and hone on your ability to continuously re-skill on more than any one technical skill (the best tutor in the world is now available to anyone, 24/7, for a low cost)
- Stay close to the business, not just the data
The closer you are to the problem, the harder you are to replace.- Sit in more stakeholder conversations, understand goals and constraints.
- Context will make your analysis sharper than anything AI can infer.
- Don’t feel weird about using AI
You’re not “cheating” if you are using a tool that makes your work better. We’ve always used tools to extend human capability. This one just happens to be exponential.
Final Thought
AI is not just another tool in our workflow anymore.
In many ways, it’s becoming the starting point. I believe that while we may no longer be the first analyst on the problem, we, humans, are still the ones responsible for asking the right questions, making sense of the answers, and deciding what to do next. And that part still matters more than ever.
…………
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.
