it can be one of the most important stages of our work.
I'm not saying this to be dramatic or cliche but because something subtle and irreversible is happening in the way I work. With each passing day, I find myself using AI more and more. I go back and forth a bit with it. I ask you a little bit because with the increasing exchange, it has been right enough most of the time.
My role is slowly evolving constructive to to confirm.
These days, I'm used to watching the AI handle things before I work on things I thought needed my expertise.
I often joke that I will never use ChatGPT to plan my travel. Planning a visit to my playground. I love opening twenty tabs, comparing neighborhoods, reading reviews, and creating an itinerary that feels right. However, last week, I asked ChatGPT to walk me through everything a first-timer to Disney Parks should know. In seconds, I had notes of everything I needed to know and do, without opening any other tab.
That kept me quiet.
If AI can handle something I really enjoy and am proud of… what does that mean for the rest of my career?
My Career Journey Before AI
Not so long ago, my career as a math consultant was long, friendly and deeply insightful.
I would:
- Define the business problem
- Identify relevant data sources
- Write code from scratch to clean up dirty datasets
- Manage and analyze data
- Hit mistakes, correct mistakes for hours
- Search Stack Overflow, rewrite queries
- Check the edge cases
- Build participant decks
- Translate technical results into business stories
A lot of my value was spent on making this app.
Over time, I worked to create a niche for myself so that I could translate business data and vice versa.
How It Looks Now
However, today, AI is often the first thing that hits my problem statements.
At first, I was trying hard to get the instructions. I could define the business context, schema, constraints, and expected outcome, and explore what AI could do for me. Now that I've seen productivity growth, the expression of some of my thoughts, I rely heavily on AI now to:
- Write end-to-end code for data cleaning, analysis, and visualization
- Upgrade features and improve model performance
- More details I hadn't thought of yet
- Document the entire process
- Produce superior briefs for diverse audiences
With that, AI has effectively become my first analyst.
And this did not happen overnight or even in one week. A subtle change has happened over the months and now, if I have something to do, I naturally tend to go to AI first, even before I think about it myself and find that interesting and deeply confusing.
Because this change does not increase. That's right 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 everythingeverything at once.
What Does This Really Mean?
AI is becoming a common layer of cognitive function.
I don't know if AI will ever replicate the deep human empathy or if the trust built over years can be automated. And honestly, I don't know where the ceiling is anymore.
But I feel that the people who will best navigate this change are not those who avoid it but those who lean into it with curiosity.
So Where Do We Build an Edge?
I've been thinking about this lately—when human intelligence is normalized by artificial intelligence, how do I stay relevant? I don't want to keep watching my role slowly change without me reshaping my skills and toolkit.
I noticed that the edge is becoming less visible.
Years ago, when I joined the workforce as an analyst, I thought that because I knew SQL, I could build models, and I could clean dirty data, I was limited. These were tangible skills that one could measure, develop, and demonstrate. However, much of that is being phased out. AI can do much of it quickly, and increasingly well.
So the edge has to go somewhere else.
To me, it's starting to feel like the edge is in the way you think before you open the tool.
Here's how I'm preparing to build that edge over the next few years as a senior analyst –
- Meet AI in your environment the original workflow:
I highly recommend that you start using AI seriously (not just searching for itineraries and cleaning your emails). The edge comes from using the AI of practical examplesnot just use.- Don't stop at “write me a question” or as a search engine. Use it for the full cycle of problems from data cleaning to analysis to storytelling with that data.
- Compare the output with yours and note the gaps.
- Understand where AI works for you, and more importantly, where it doesn't:
The real edge isn't just in using AI. It's knowing when not trust in it. AI can generate answers, but you need to know when they are wrong.- Always ask if the trend/pattern/insight the AI is suggesting makes sense? What is missing? What is bias?
- Stress test results with a simple mental health test.
- Be intentional about what you post
Let the AI handle the speed, composition, and first draft for now as I settle into this space, if not. Next, move on to letting AI handle the problem of framing, judgment, ethics, and accountability. But, don't forget to confirm.- Test the results with small samples, edge cases, or other questions.
- Don't blindly trust clean output. Always verify what you are outputting.
- Prepare your role to improve.
We're already moving from question writers to inform thinkers, data validators, and story tellers.- Go beyond “here's what the data says” → “here's what we should do next.”
- Analytics focus on business impact, not just accuracy.
This is where analysts become decision partners - Build a habit of adaptability and sharpen your ability to continue and train more than any single technical skill (the best instructor in the world is now available to anyone, 24/7, at low cost)
- Stay close to the business, not just the data
The closer you get to the problem, the harder it is to replace it.- Sit in on multiple stakeholder conversations, understand goals and constraints.
- Context will make your analysis clearer than anything AI can say.
- Don't feel weird using AI
You're not “cheating” when you use a tool that makes your work better. We have always used tools to increase the power of people. This just happens to be exponential.
A Final Thought
AI is no longer just another tool in our workflow.
In many ways, it becomes the starting point. I believe that although we may be the first analysts in this problem, we the people are still the ones who have to ask the right questions, have a sense of the answers, and decide what to do next. And that part is more important than ever.
……………
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.
