You Know "Why AI is the Easiest Language to Learn Ever"
- AIMS
- 2 days ago
- 6 min read
Updated: 18 hours ago
While I’ve been working in the field of AI for almost a decade now, I still consider myself a non-coder. Back in 2015, when I built my first AI startup, TensorFlow had just been released. Although it made AI more accessible to Software developers for implementation , but still that point time it was still a tough ask to work with AI.
Cut to today — I can safely say it has never been easier to learn and work with AI than it is right now. You don’t need to code. You don’t need complex formulas.
With ChatGPT, you can literally talk to intelligence in plain natural language.
But here’s the irony. Even as AI has become easier, the fear around it has only grown.
When I work with managers, I see it clearly —for the last decade, most non-technical professionals have been approaching AI from a place of fear and just a little curiosity.
Earlier, it was fear of coding. Now, it’s fear of being replaced. And every time a headline like “Amazon to lay off 14,000 employees” appears, that fear spikes again.
My view is simple — when something has become this easy to learn, why approach it with anxiety ? Why not approach it with curiosity ?
Because this is the easiest it has ever been — and also the most powerful.
ChatGPT today can think and reason like an IIT/MIT or Harvard graduate sitting right beside you. So now the question is do have the capability leverage that intelligence,
And that, to me, is what the AI Mindset is all about, more than technical skill and new tool It’s about clarity, curiosity, and the ability to collaborate intelligently — to use AI not as magic, but as a partner that makes your work sharper and faster.
That mindset — not technical skills — is what has helped me, as a non-technical founder, build globally recognized AI products and work confidently in this new era.
How To Develop an AI Mindset
Now the natural question is, “How do you develop an AI mindset?”
Of course, that’s a journey — and I genuinely believe you can start it with just one tool.
You don’t need to learn twenty.
Because this isn’t a course you complete; it’s a capability you build.
So let me give you a small glimpse into some aspects of the AI mindset.
Example 1 : Show better Intent , else Junk in Junk out
Last month, a sales manager from Mumbai told me,
“I tried ChatGPT once — it gave me useless answers. I stopped using it.”
So I asked him what he’d typed.
He said, “Write me a cold email to reach new clients.”
Fair enough — that’s what most people would do.
The output he got? A stiff, templated email that sounded like it was written in a boardroom.
Then we tried something different.
He added:
“We sell logistics software for small distributors in Maharashtra. Our buyers are owners who hate long tech pitches. Make it sound like a helpful introduction from a local partner, not a corporate mail.”
The new version was short, friendly, and instantly usable.
He looked up and said, “This one actually sounds like me.”
Same tool , The difference? better intent , better context , clear thinking and focussing on process to get the right output AI doesn’t reward clever prompts — it rewards clear thinking.
Once you know how to give the right background, even a single line can turn into value.
Example 2 — Collaborate till it creates value.
A few months ago, an Operations Manager, was preparing for a review with a large logistics client.
He had to analyse delivery delays across zones and propose fixes.
Normally, he’d spend half a day just cleaning up data and writing the summary.
This time, he tried something different.
He uploaded a small extract of the delay data into ChatGPT and said,
“Act as my operations analyst. I’ve shared data on shipment delays across five zones. Help me identify patterns and create three possible explanations.”
The AI responded with reasonable hypotheses — bad weather in East, vendor issues in North, resource gaps in South.
Decent — but still surface-level.
Instead of accepting it, he replied:
“These are generic. Let’s go deeper. For each cause, suggest specific metrics I should check in our reports to validate it.”
The tone of the conversation changed completely.
Now the AI listed the exact checks: vendor-wise rejection rate, per-driver delivery time, depot capacity variance.
Amit kept refining the dialogue. He didn’t just take what the AI said — he built on it, challenged it, redirected it.
By the end of that one conversation, his analysis was so sharp that in the meeting, his client told him, “This is the first time someone’s explained our delay problem with data and logic, not excuses.”
That’s what I mean by a collaboration style.
It’s not about typing once and waiting for the answer.
It’s about thinking aloud with AI — exploring, disagreeing, adjusting, and improving together.
When you work this way, AI doesn’t feel like a tool.
It starts to feel like a colleague — fast, analytical, but dependent on your judgment to make sense of it all.
Example 3 — Understanding AI helps and workaround it
A product manager, Saurabh, was trying to use ChatGPT to draft a market research summary.
He asked,
“Summarize the key trends in India’s EV industry.”
ChatGPT gave him a polished but flat answer — the kind you could find on any news site.
Saurabh smiled and said, “This sounds like it’s written by a consultant, not someone who’s read the data.”
Instead of giving up, he remembered what we had discussed earlier — that ChatGPT doesn’t know in real time; it predicts patterns from what it has seen.
So he decided to guide it. He uploaded three short articles he’d collected that morning — one from Economic Times, another from a startup blog, and a press release from Tata Motors.
Then he asked:
“Use only the data from these three articles to create a 200-word executive summary, focusing on what has changed in the last six months.”
This time, the output was sharp — it picked up the shift in consumer incentives, rising battery costs, and government policy updates. He didn’t change the question. He changed the playing field.
By anchoring ChatGPT with real context, he turned a generic model into a domain-aware assistant.
When I asked him how he figured that out, he said,
“Once I realised ChatGPT isn’t searching the web — it’s reasoning from memory — I stopped blaming it and started feeding it what it needed.”
That’s the difference between using AI and understanding AI.
In short — what to remember
Example 1 — Better intent, not magic prompts.
Don’t junk it. Brief it. Garbage-in, garbage-out still holds — only now the garbage is fluent.
Example 2 — Collaborate until it creates value.
The first answer is rarely the final answer. Iterate, refine, push. Treat AI like a teammate, not a vending machine.
Example 3 — Understand how AI works and work around its limits.
Anchor the model with real context. Feed it the right documents. Make it reason from your data — not from generalities.
Of course, none of this undermines the simple truth: learning the basics of AI helps. Knowing what tokens, context windows, hallucinations, and RAG mean will make you operate smarter and safer. It builds a solid foundation.
But the bigger point is behavioural: thinking clearly matters more than typing perfectly. Apply the principle of first thinking — be clear on the destination before you ask for directions. Your first prompt may not be perfect. That’s OK. Iteration will get you there.
If you want a practical next step: pick one weekly task (a report, an email, a deck). Use your chosen tool (even if it’s only ChatGPT).
Brief it properly.
Treat the output as a draft.
Iterate and validate.
Do this for a week and watch how your thinking — not the tool — improves.
Our main focus is to help managers understand the fundamentals of AI and not get lost in the AI frenzy and fancy words , but by understanding AI and developing an AI mindset to grow in their carrier with confidence and not fear.