improvement and AI

The question manufacturers get wrong about AI, and the one that works

Most manufacturers ask 'how should we use AI?' It's a reasonable question, but it's the wrong one. Here's the question that produces actual results.

by Danie Grobbelaar, Verbeter 8 min read · 8 June 2026

There’s a conversation we’ve had with operations leaders in manufacturing and distribution about getting their AI strategy right. It tends to go like this.

They tell us they know AI is significant. They’re seeing it everywhere: at conferences, in the trade press, in their competitors’ announcements. They’ve had conversations with their ERP vendor about it. They may have even run a pilot or two. Their question: how should we be using AI?

It’s a good question. It comes from exactly the right place: a smart operator who can see that something significant is happening and wants to be deliberate about how they approach it rather than reactive.

And yet we believe it’s the wrong one.

The gap between what you expect and what actually matters

A recent CohnReznick study found that about three-quarters of manufacturers are already investing in and applying AI. When asked what they’re most worried about, the answers are predictable and sensible: cybersecurity and privacy (60%), cost (46%), employee concerns about AI.

These are reasonable concerns. If you’re about to bring a new capability into your operations, you should be thinking about security, cost, and how your people will respond.

What’s interesting is what happens when those same companies actually implement. The challenges that turn out to matter most aren’t the ones they anticipated. The Manufacturing Leadership Council found that the single biggest challenge manufacturers face in AI and digital adoption is “developing a cohesive strategy,” cited by 42% of respondents. Not the technology. Not the budget. The clarity about what they’re actually trying to do.

MIT Sloan’s research on AI adoption in manufacturing firms found something similar. On average, companies see a short-term decline in productivity after they begin using AI, before experiencing longer-term gains. The researchers link this pattern specifically to the time required for process redesign, organisational restructuring, and workforce learning, not the technology itself.

Companies go in worrying about security and cost. They come out having discovered that their real constraint was knowing clearly enough what they wanted to change, and being willing to change the processes around the technology.

That’s the gap between the question most companies are asking and the question that actually determines whether AI produces competitive advantage.

The right question for your AI strategy

Here’s how we’d rephrase it.

Instead of “how should we be using AI?”, ask: “Where in our operation does the improvement constraint sit, and where can AI help us address it in a way that wasn’t possible before?”

The difference is bigger than it looks.

The first question starts with the technology and works backwards to a problem. You end up looking for places where AI can be applied and applying it there. The constraint your operation actually has may or may not be anywhere near those places. There’s no mechanism that routes AI toward the problems that most need solving.

The second question starts with the constraint and works forward to the tool. You identify where improvement creates the most competitive value. Then you ask: is there a way to address this constraint using AI that produces a materially better outcome than what was possible before?

The answer is sometimes yes and sometimes no. When it’s yes, the improvement is real and measurable: you designed the engagement around the problem rather than around the tool. When it’s no, you’ve avoided an expensive pilot that produces good-looking reports and no bottom-line movement.

What this looks like in practice

Most operations in manufacturing and distribution have a short list of where the real constraints sit. Unplanned downtime that disrupts production schedules. Inventory positions that are either too heavy or too lean at the wrong time. Quality escapes that get to the customer. Demand signals that don’t reach the production plan fast enough. These aren’t abstract problems; they show up in your margin and in your customer relationships.

Now ask, for each one: is there a way AI addresses this that wasn’t previously practical?

For inventory positions, the answer is often yes. The challenge is rarely that businesses don’t know a stock-out risk exists. It’s that the signal arrives too late to act on. AI can read demand patterns, supplier lead times, and current stock levels across a full product range simultaneously, and surface replenishment needs before the disruption materialises. The process redesign is straightforward: replenishment decisions move from reactive firefighting to what the model flags in advance. Without that redesign, the model identifies the risk and no one acts until the customer is already on the phone.

For the quality escape problem, it depends entirely on where in the process the escape is occurring and what data exists around it. AI may be the right tool. It may not. The point is that you can’t know until you’ve defined the constraint precisely enough to ask whether AI helps.

This is what’s meant by “developing a cohesive strategy,” the challenge that 42% of manufacturers identify as their biggest barrier. It’s not a document. It’s a clear enough picture of where your operation most needs to improve that you can evaluate any technology or methodology against that specific problem.

The practical version

When a manufacturer asks us how they should be using AI, here’s what we ask them back.

Pick the three operational outcomes that would create the most competitive value if you could move them meaningfully. Not “we want to be more efficient.” Something specific. Cycle time on the line. Inventory days. Customer complaint rate. Equipment availability.

For each one: what’s currently stopping you from moving it? Is it a data problem, a process problem, a skills problem, a decision-making problem?

For each constraint: is there a role for AI that addresses it in a way that wasn’t previously available? Not marginally better than what you’re doing now. Materially better.

That exercise surfaces two or three AI opportunities worth pursuing seriously, rather than a list of everywhere AI could be applied. The ones it surfaces will be the ones where, when you redesign the process around what AI makes possible, the constraint actually moves.

The manufacturers who get this right don’t have better access to AI tools than the ones who don’t. They have a clearer picture of where the constraint is. Everything else follows from that.

If you’re working through where AI addresses a specific constraint in your operation, that’s exactly the kind of conversation we’re built for.

Danie Grobbelaar

Verbeter

Danie Grobbelaar is an industrial engineer whose career has been built inside South African manufacturing and distribution operations. He brings practitioner credibility earned across hundreds of operational environments: the kind that comes from having seen what sustains and what does not once the consultant leaves.

Frequently asked questions

Why is 'how should we use AI?' the wrong question for manufacturing operations?

It starts with the technology and works backwards to a problem. You end up looking for places where AI can be applied and applying it there. The constraint your operation actually has may or may not be near those places. Manufacturing Leadership Council research found that the single biggest challenge manufacturers face in AI adoption is developing a cohesive strategy, cited by 42% of respondents. Not cybersecurity, not cost, not the technology itself. The question is wrong because it has no mechanism to route AI toward the problems that most need solving.

What's the right question to ask when building an AI strategy for manufacturing?

Where in our operation does the improvement constraint sit, and where can AI help us address it in a way that wasn't possible before? This starts with the constraint and works forward to the tool. You identify where improvement creates the most competitive value, then ask whether AI produces a materially better outcome than what was previously available. When it does, the improvement is measurable: the engagement was designed around the problem rather than the tool. When it doesn't, you've avoided a pilot that produces good-looking reports and no bottom-line movement.

How do we find the two or three AI opportunities actually worth pursuing?

Pick the three operational outcomes that would create the most competitive value if you could move them: specific ones, not general efficiency goals. Cycle time, inventory days, customer complaint rate, equipment availability. For each, identify what's currently stopping you from moving it: a data problem, a process problem, a skills problem, a decision-making problem. Then ask: is there a role for AI that addresses this in a way that wasn't previously available? Not marginally better than what you're already doing. Materially better. That exercise surfaces the opportunities worth pursuing seriously.

Why do manufacturers see a productivity dip when they first adopt AI?

MIT Sloan research on AI adoption in manufacturing firms found that on average companies see a short-term decline in productivity after beginning to use AI, before experiencing longer-term gains. The researchers link this to the time required for process redesign, organisational restructuring, and workforce learning, not the technology itself. The implication: AI implementations that skip the process redesign step often produce good-looking outputs without bottom-line movement. The constraint has to be identified and the process has to change around what AI enables.

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