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Many startup founders have spent years inside large organisations before starting out on their own.

So when they build their companies, they consciously avoid the things they disliked most: layers of approvals, slow decisions, and processes that get in the way of execution.

In the early days, that works brilliantly.

But growth changes the equation.

As teams expand, customers increase, and responsibilities multiply, startups inevitably need systems and processes. The challenge is no longer whether to introduce them, but how to do so without slowing down innovation.

The founders who seem to navigate this transition best understand that processes shouldn't create bureaucracy. They should create clarity, accountability, and the ability to scale.

Interestingly, we've seen a very similar pattern in SMEs once they cross certain growth milestones. Different businesses, different contexts - but often the same underlying questions:

• How do you build an organisation that is structured enough to scale, yet agile enough to keep evolving?
• At what stage did formal systems and processes become necessary in your organisation?
• And what worked - or didn't work - during that transition?

No tool - machine or software - can fix a broken process.

We’ve been solving the wrong problem.

Transactional systems remove redundancy - for efficiency.
Data warehouses add it back - for insight.

Two systems. Two truths.
Neither complete.

AI doesn’t bridge this gap.
It exposes it.

If data can’t move from transaction to insight to action,
it’s not a data problem.

It’s an architecture problem.

The “Rule of Three” is simple.

Do the three most important tasks before accessing email or WhatsApp.

It forces focus.

That’s how good businesses operate too.

They don’t optimise for activity - they prioritise outcomes, and systemise the rest.

Process automation and ERP systems do exactly that.

Focus on what matters.
Automate everything else.

The first time I remember seeing a drone in action was at a friend’s wedding in Ahmedabad, back in January 2015. It was being used for photography - and for most of us, it felt novel, almost futuristic.⁣
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Over time, drones became common in photography. Today, they’re used routinely by creators and influencers. We’ve seen them deliver critical supplies to remote terrains.⁣
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More recently, I saw one being used casually at a family outing.⁣
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And then, of course, there is the other side - their role in modern warfare, from Ukraine to West Asia.⁣
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Same technology.⁣
Completely different outcomes.⁣
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That’s how most technologies evolve.⁣
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What starts as novelty becomes utility.⁣
What becomes utility eventually becomes infrastructure.⁣
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AI is somewhere between novelty and utility today.⁣
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Its impact may be overestimated in the short term.⁣
But over time, like the internet, like drones - it may reshape far more than we currently imagine.⁣

Businesses often overestimate what technology can achieve in the short term,⁣
and underestimate what it delivers over the long term.⁣
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Technology evolves rapidly.⁣
Human behaviour adapts slowly.⁣
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Real transformation happens when the two align.⁣
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The internet is a good example.⁣
It didn’t change everything overnight - but over time, it reshaped how we work, communicate, and build businesses.⁣
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AI won’t change everything overnight - but it may leave very little unchanged.

Most people collect data.
Few understand the context that turns it into value.

I don’t know how things work in Silicon Valley - I’ve never been to any place west of London.⁣⁣
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But here in India, across acres of the software support and systems maintenance ecosystem - call it IT services, outsourcing, offshoring, or GCCs - the most respected people are the ones who can debug.⁣⁣
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Not the ones who write the most code.⁣⁣
The ones who can fix what breaks.⁣⁣
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These are professionals who have seen so many systems fail, behave strangely, and recover that they’ve internalised how technology really works. Give them a problem and, within minutes, they can trace the root cause and suggest a fix.⁣⁣
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Early in my career, I worked with one such professional.⁣⁣
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The probability of finding him at the sutta-tapri outside the office was often higher than finding him at his desk. But when something broke, someone would walk up to him, explain the issue, and he would calmly point to the exact place in the system where things had gone wrong.⁣⁣
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And, he was almost always right.⁣⁣
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That’s the beauty of real technology expertise - people who have seen systems behave in the messy complexity of real-world environments.⁣⁣
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Will AI replace such experts?⁣⁣
Maybe. Maybe not.⁣⁣
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But will AI create the need for many more such experts?⁣⁣
I would say - yes.
Someone will still have to debug the future.

Every factory visit teaches me something new. Every warehouse reveals something most reports don't.

I've had the opportunity of spending time on shop floors and navigating through pallet racking across many facilities - and what strikes me, every single time, is how much a physical space reveals about the health of a business.

The layout of a warehouse. The rhythm of a production line. The way material moves - or doesn't.

You notice things that never make it into dashboards or MIS reports. Where bottlenecks quietly form. Where workarounds have become routine. Where a small process fix could unlock something much larger downstream.

These aren't just operational details. They are the story of how efficiently a business truly runs - told not in numbers, but in motion, space, and the behaviour of people doing real work.

And that story is almost always more honest than any presentation in a boardroom.

For anyone serious about manufacturing or supply chain - or about building systems that actually serve these environments - there is no substitute for time spent on the ground.

Observe first. Then design.

The AI conversation in tech feels increasingly polarised.⁣
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One camp believes AI is the end game - that software engineers, analysts, designers, maybe even founders, will soon be optional.⁣
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The other camp dismisses it - citing non-determinism, hallucinations, and the lack of long-term proof.⁣
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Both positions, in my view, are intellectually lazy.⁣
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AI is neither apocalypse nor illusion.⁣
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It is a tool - a powerful one. It accelerates some tasks dramatically. It performs brilliantly in narrow contexts. It is unreliable in others. And like every tool before it, it will evolve.⁣
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History offers perspective.⁣
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When Canva emerged, it didn’t eliminate designers - it expanded the design market. When Figma accelerated collaboration, it didn’t reduce opportunity - it increased the velocity of product creation.⁣
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Tools reduce friction.⁣
Reduced friction expands ambition.⁣
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AI will likely democratise information processing and lower the barrier to building. But building systems is not the same as generating outputs.⁣
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Architecture. Accountability. Trade-offs. Long-term consequences. Context.⁣
These are not prompt-level decisions.⁣
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The real question isn’t “Will AI replace us?”⁣
It’s “What becomes more valuable when execution gets cheaper?”⁣
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That’s where the real debate should be.