towards a broader perspective
What good looks like kept changing for me, every time I got exposed to something new. A lot of what I know came from the job, and over the years I started adding structure to it – cloud, machine learning, human behaviour and design thinking..
So in 2010 I took my first job in consulting, in an office in HITEC City, and basically my idea of what good looks like started changing. It hasn’t stopped since.. Every time I got exposed to something new, new people, new kind of work, new tech, I wanted to open my mind a bit broader and challenge myself and my goals again.
That first firm hired from all over India, you know, IITs, IIMs, people who thought in ways I didn’t yet. I grew up believing engineering or medicine were the only paths worth taking, and within a month there, I didn’t believe that anymore.
So that kind of exposure opened my mind more than anything else. Back then it just happened to me, and since then, I’ve gone looking for it.
For most of my career, I basically learned on the job. I’d read something and use it at work that same week.
Don’t Make Me Think is probably the clearest example. I read it, and the way I critiqued an interface changed, and after a while I stopped doing it on purpose. I’d look at a screen and know what was wrong before I could say why. I think that’s how you know you’ve actually learned something, you don’t have to remember it anymore.
Krug and Norman gave me usability and taste. The Mom Test and Teresa Torres gave me discovery and research, years later, after I learned the hard way that I needed them, and then Sprint, Inspired and a lot of blogs.
A lot of this only comes with the job and with experience, you learn it by doing it. Certifications don’t replace that, what they add is structure..
The first time I really felt that was with cloud, about seven years back. So I did Azure AZ-900, the cloud training, and it was a cloud learning pathway around how do you move from data centers to all the layered stack of cloud. I learned that first, and then maybe went a little bit deeper into Azure and Google Cloud platforms, and it greatly helped me understand next-gen software..
Then I moved into leadership, and the work started changing a lot faster. You know, it was a different kind of engagement every few months, a different domain, different constraints, a different idea of what good looks like, and at that level you’re expected to have a point of view on day one.
So from 2023, I started to really appreciate structured, foundation-led learning, especially in areas where I had to expedite my learning and keep the learning curve smaller. I went after three things, machine learning, human behaviour and design thinking.
None of it was full-time, I was working the whole way through. The executive general management program was long distance with weekends in person, and everything since has been remote and self-paced, in evenings and on weekends.
Machine learning first. I started with AI For Everyone, which is an overview, and I wanted to understand how it actually works. So I did the Stanford machine learning specialization, all four courses, about four months, alongside the job. Not all of it was worth the time, honestly, unsupervised learning is the part I wouldn’t do again.
All thanks to the certification, I started thinking about data all the time – the feature set, the predictor and target variables, looking at large language models through fundamentals like the weights and parameters…
Human behaviour next, so the Behavioural Design Academy. I already knew what I wanted from a flow, the fewest steps, clear intent, nothing on the screen that doesn’t need to be there. What I didn’t have was a technique to get there every time, instead of by feel.
Now I can say why a screen creates choice paralysis instead of just sensing that it does, and I can cut information that’s only there out of habit. So it didn’t really change what I was aiming for, it changed how often I get there.
And then design thinking, because it’s a skill that is fundamental to the work I do. I did it from MIT Sloan in 2023, and again from Kellogg in 2026, basically to understand how AI impacted design thinking.
In my own work, I see AI being pushed into every part of the design process – synthetic personas, interviews, research, insights, user flows, information architecture, visual design. There’s a tool for all of it, and honestly, almost everyone with a strong opinion on it has something to sell.
So I wanted an academic view, from people who aren’t selling anything. Not on how to use AI – I use it every day. On where it belongs in the process, and more useful, where it doesn’t. The tools are answering “can this be automated?”. The questions I care about are “should it be?” and “what do you lose if it is?” I wanted to unlearn and re-learn what academia thinks.
So yeah, I think the Kellogg class basically didn’t change much for me, it was a good refresher. But definitely from an AI standpoint, I got to know to use AI as an extension to your initial thoughts, compared to AI getting you started. So it’s more of a validation of my thinking rather than me learning something completely new.. It’s also how I write these posts, my ideas first, and then AI expands them.
I see this in my own building. The tools got good – I built my whole site with them. But that didn’t make the knowledge underneath worth less. If a tool can make the thing, what’s left is knowing what to ask it for, and knowing if what came back is right. That’s where all of this learning actually shows up for me..
So yeah, it’s just my path, it’s not advice. I know what good looks like will keep evolving for me, so I’ll keep looking for that exposure, and keep challenging myself and my goals…