“Roads? Where we’re going, we don’t need roads.”
— Back to the Future (1985)
1. Letting Go of the Old Model
Sticking to your old mental model—your tried-and-tested habits—is like refusing to give up a horse and buggy when Teslas are whizzing past. We cling to the familiar because it’s comfortable, but in AI adoption, that comfort zone can be a trap. Whether it’s how you work, how you communicate, or how you solve problems, if you keep doing things “the old way,” you’ll be left behind in the digital dust.
2. Transformation Fatigue Is Real
Even when people are open to change, organizations often overload them with AI tools and mandates—without enough context or support. The result? Transformation fatigue: a quiet killer of enthusiasm where teams feel exhausted and distrustful. As one recent report put it, “AI’s problem isn’t the tech. It’s trust” (TechRadar Pro, Aug 2025). Gradual rollouts, real training, and clear communication matter far more than flashy launches.
3. The Experience Paradox
In Khadgam, Prithvi proudly says he has “30 years’ industry experience.” But if those decades were just spent replaying the same script, is that really experience? In AI adoption, true experience comes from evolution, not repetition. It’s about outgrowing your current role, experimenting with new tools, and—even if it stings a little—making parts of your job redundant so you can focus on higher-value work.
4. The Skills Gap and Adoption Lag
Across Asia—and especially in India—skill shortages remain a serious hurdle. A recent study found that 58% of Learning & Development leaders cite skill gaps and slow AI uptake as their biggest challenge (TOI, Aug 2025). Without structured upskilling, AI risks becoming another expensive tool gathering dust.
5. The Infrastructure Reality
AI isn’t just a chatbot in your browser—it’s GPUs, data pipelines, storage systems, APIs, and energy costs humming in the background. Choosing the right infrastructure—cloud, hybrid, or on-prem—can make the difference between scalable success and a costly dead end . This decision needs both technical foresight and financial prudence.
A Fun Wrap-Up
Adopting AI isn’t a one-off switch—it’s an ongoing mindset shift. You don’t have to become a machine-learning engineer overnight, but you do have to:
- Let go of outdated habits.
- Build trust, not just compliance.
- Redefine what “experience” means.
- Close the skill gap.
- Strengthen your tech foundation.
Because in the end, AI’s role isn’t to replace us, but to elevate us—if we’re willing to take the ride.