The past three years have been all about the AI buzz—more specifically, LLMs and Gen AI. A natural follow-up question I often get in most of the forums I speak at is: “What’s next?”

I can’t explain it better than through a conversation I had with my 9-year-old.

During our lazy-time chats, conversations are usually random. Yesterday, a casual talk about school quickly turned into a deep discussion about her interests and future.

I used to love geography (was always the class topper in that subject), but like many, I ended up focusing on math and science—better career opportunities, you see. If genes work the way they do, it’s no coincidence that she loves geography too. But what surprised me was her next statement:

“I know, you would want me to focus on science. AI and all the cool stuff are science, right?”

That hit me hard. How quickly we condition kids to think in binary terms.

It reminded me of a common question I get from mid-career professionals:
“How do I move into an AI role? Should I learn Python? How do I build my own AI agent?”

As the dust settles on LLM model performance race, we are starting to see that the real impact of AI will come from the use cases it transforms. That’s where every professional should focus.
âś… If in finance, explore how AI is transforming fraud detection, risk assessment, algorithmic trading, and personalized banking.
âś… If you are in marketing, understand how AI is transforming customer engagement.
âś… If you are in healthcare, learn how AI is optimizing diagnostics and patient care.

Your true value isn’t in building AI models—it’s in leveraging AI tools smartly to make processes faster, better, and more reliable.

There will always be a core group of innovators pushing AI technology forward. But the bigger impact will come from the professionals who evaluate, implement, and scale AI across industries. That’s when we will start seeing the real value of this technology. I believe the next phase of AI will be exactly that—focused on real-world applications rather than just model performance.

👉 And as for my conversation with my daughter…
I reassured her that she has the absolute freedom to follow her passion. And she can be rest assured—AI will play a role in whatever field she chooses.

Geography is an exciting space for AI. AI for geoscience is a niche field tackling some of humanity’s biggest challenges:
đź“Ť Climate change modeling
đź“Ť Geological data analysis & hazard forecasting (earthquakes, landslides, volcanic eruptions)
đź“Ť Remote sensing & mineral deposit identification
đź“Ť Groundwater resource identification

So, instead of asking, “How do I transition into AI?”, start asking:
“How can I use AI to transform my work?” That’s where the real opportunity lies. 🚀