Sundeep Vaish ♕

Internet Marketer, Affiliate Marketing, Internet & Online Marketing Education

[Sundeep Vaish ♕ @SundeepVaish]

You can buy the best AI in the world.

You cannot outsource the internal conviction to use it.

The plants that get AI right always have one thing: someone inside who owns it.

[Sundeep Vaish ♕ @SundeepVaish]

Two things a manufacturer can do while deploying AI

Building capability — your team gets smarter over time.
Building dependency — the vendor becomes harder to leave.

Most vendors build dependency. That’s where the recurring revenue is.

Which relationship are you actually in?

[Sundeep Vaish ♕ @SundeepVaish]

Most manufacturing AI is designed around a false assumption: that operators work in a linear, predictable way.

And the gap between the assumption and the reality is where implementations quietly fall apart.

Observe and understand the work before you build the system.

[Sundeep Vaish ♕ @SundeepVaish]

Operator trust in AI doesn’t die in one moment. It dies in the accumulation of false alarms nobody explained, feedback nobody acted on, and a system that treated them as users rather than participants.

Bring them into the process. Their scepticism is a resource.

[Sundeep Vaish ♕ @SundeepVaish]

Most plants spend more time evaluating an AI vendor before signing than managing them after.

That’s backwards.

One internal owner. Operational reviews, not system reports. Quarterly contract check-ins.

Vendor management is an operational discipline. Treat it like one.

[Sundeep Vaish ♕ @SundeepVaish]

Five questions to ask an AI vendor before you sign:
1. Who’s on-site during implementation?
2. What does our data need to look like?
3. Who measures success at 90 days?
4. Show me a failure. What happened?
5. What’s the handover plan?
The answers tell you more than the demo.

[Sundeep Vaish ♕ @SundeepVaish]

@TheIncNews Modi

[Sundeep Vaish ♕ @SundeepVaish]

Most AI vendors who sell to manufacturers have never run a plant.

They can outsource the technology. They can’t outsource the judgement about which problems matter, which data is reliable, or whether the output is usable on the floor.

That judgement has to stay inside.

[Sundeep Vaish ♕ @SundeepVaish]

A one-page AI scorecard for your plant.

Section 1: Baseline — what were the numbers before?
Section 2: Operational outcomes — did the plant perform differently?
Section 3: Adoption — are your people actually using it?

Reviewed monthly. Owned by operations, not the vendor.

[Sundeep Vaish ♕ @SundeepVaish]

Your AI vendor tracks model accuracy, uptime, alert volume.

None of those tell you if your plant is running better.

Vendor metrics measure the AI. You need operational metrics that measure what changed because of the AI.
Don’t confuse the service level agreement with the point.

[Sundeep Vaish ♕ @SundeepVaish]

Most plants can tell you if their AI is running.

Almost no one can tell you if it’s working. Availability metrics tell you the system exists. But nothing about outcomes.

Before you deploy: write down the one number that would have to move for this to be worth it. Then track it.

[Sundeep Vaish ♕ @SundeepVaish]

@CJP_for_India Yes

[Sundeep Vaish ♕ @SundeepVaish]

One question to ask every AI vendor:

“Show me an implementation that failed. What went wrong and what did you learn?”

A vendor who can’t answer that honestly isn’t ready for your plant.

[Sundeep Vaish ♕ @SundeepVaish]

A real AI roadmap for manufacturing doesn’t start with use cases.

It starts with constraints.

What’s your data quality? Which processes are stable enough to model? Where do you have the capability to act on outputs?

Sequence by readiness, not ambition.

[Sundeep Vaish ♕ @SundeepVaish]

A project asks: did it work?

A programme asks: how do we make it work better every month?

Most manufacturers have run AI projects. Almost none have built AI programmes.

That’s the gap.

[Sundeep Vaish ♕ @SundeepVaish]

Every AI implementation that stuck had one thing in common.

Not the best tech. Not the biggest budget.

One person on the floor who made it their mission.

Find that person. Then get out of their way.

[Sundeep Vaish ♕ @SundeepVaish]

Your best engineers resist AI the most. That’s not a problem. That’s expertise expressing itself as caution.

Don’t override them. Recruit them.

Their knowledge should be the input, not the obstacle.

[Sundeep Vaish ♕ @SundeepVaish]

The people who feel most threatened by AI in manufacturing aren’t on the shop floor.

They’re in the middle. And they control implementation.

If your AI rollout has stalled, that’s where to look.

[Sundeep Vaish ♕ @SundeepVaish]

You can buy the best AI in the world.

You cannot outsource the internal conviction to use it.

The plants that get AI right always have one thing in common: someone inside who owns it.

[Sundeep Vaish ♕ @SundeepVaish]

AI readiness isn’t a technology problem. It’s a foundation problem.

Three gaps most plants haven’t looked at honestly: data quality, process stability, organisational clarity.

Fix those first. The technology is the easy part.