The AI Maturity Model for Mid-Sized Manufacturers: Where Do You Stand?

Everyone wants AI. For mid-sized manufacturers, the question isn’t whether to adopt it — it’s how ready you actually are to make it work for your business.

And your employees aren’t waiting. According to the 2024 Work Trend Index, 78% of AI users are bringing their own tools to work, with or without official support. Seventy-five percent of knowledge workers are already delegating some of their tasks to artificial intelligence.

So the real question is: where do you stand? That’s when the AI maturity model is necessary. Think of it like a roadmap. It helps you see where you are today — and what it takes to level up. In this post, we’ll explain it step by step.

What Is an AI Maturity Model?

An AI maturity model is a structured framework that helps you assess your company's current stage in its AI journey.

On one end, you’ve got businesses just starting to experiment with AI — maybe handling some pilot tests. On the other end, you’ve got full manufacturing digital transformation, where artificial intelligence powers almost every decision.

This model is especially practical for mid-sized manufacturers. It gives a clear, structured way to evaluate current capabilities, spot gaps, and make smarter choices about what to do next.

The 4 Levels of AI Maturity in Manufacturing

So, what does AI adoption in manufacturing look like? Mid-sized manufacturers typically fall into one of four categories. Let’s take a closer look at each:

Level 1: Ad Hoc

At this stage, AI is mostly a trendy word. Maybe you’ve tried a pilot project that didn’t go anywhere, or you haven’t started yet. Data is fragmented, systems don’t communicate, and there’s no clear plan.

But no worries. This is where many manufacturers begin.

Level 2: Experimental

At this point, things are starting to move. You might have some automation in place — maybe in your back office or production lines. There are probably a few industrial AI proof-of-concepts, but they’re disconnected, and nothing feels unified yet.

Data is being collected (perhaps you’ve got a data lake), but there is no real governance behind it. It’s experimentation without clear direction.

Level 3: Structured Adoption

This is where smart factory maturity gets more tangible. AI tools are embedded in specific workflows, like predictive maintenance on machines or automated quality checks. You’ve built a data team, set KPIs, and implemented security practices to keep data safe.

Here, AI is no longer an experiment. It’s starting to deliver measurable value in focused areas of your business.

Level 4: Strategic AI Integration

At this level, operational AI is woven into your entire business strategy. Teams across departments are using artificial intelligence to support supply chain optimization, production planning, demand forecasting, you name it.

You’ve also moved beyond out-of-the-box tools to custom models that reflect your specific needs. You’ve built retraining pipelines to keep your models sharp. And you’re probably working with trusted AI partners (like Integrio) to drive manufacturing innovation even further.

How to Evaluate Your Current Position

You’ve learned the levels. Now, let’s see how you can figure out where you stand on the AI maturity scale. The core tip? Ask the right questions about your business, your data, and your goals.

• Do you have a clear business problem AI can help solve? AI in manufacturing works best when it’s connected to real business outcomes. Have you determined the issue that’s costing you time, money, or customers — and how can AI help fix it?

• Do you have clean, centralized, and labeled data for your core workflows? If your data is spread across systems or messy, even the best AI won’t benefit you. Can you trust your data to inform smart decisions?

• Have you identified a “quick win” for AI adoption? Small, focused projects help you prove value fast. Determine where AI could deliver a measurable result in the next 3–6 months.

• Do you have partners who understand both AI and your business? Successful AI and Industry 4.0 adoption largely depends on context. Do you have internal or external partners who totally understand your industry?

• Do you have a plan to turn pilot projects into repeatable processes? AI doesn’t scale on its own. Are you ready to build the manufacturing AI roadmaps, teams, and workflows that make AI sustainable?

• Have you considered how AI can double down on what makes your business great?

AI and manufacturing industry trends are great. But have you thought about where AI could really amplify your biggest strengths — be it efficiency, product quality, or customer satisfaction?

If you answered “yes” to most of these, you’re already making progress. If not, no problem. Knowing what’s missing is the first step to moving forward.

How to Move Up the Maturity Model

You’ve figured out where you stand. Now what?

Moving up the AI maturity model doesn’t necessarily mean doing everything at once. In the majority of cases, it means making smart, steady moves that solve actual business problems. Here’s how to do it:

01.

Build a Strong Data Foundation

Effective AI starts with effective data. Without clean, reliable datasets, you can’t expect it to deliver good results.

Consider the following:

• Is your data centralized? Or is it scattered across departments and systems?

• Is your data labeled and organized? Especially for workflows like inventory, production, or maintenance.

• Is someone responsible for data quality? Data management matters.

Pro tip! Focus first on core workflows — the most important, valuable areas of your business. Get that data right before expanding.

02.

Focus on One Pilot Project with Clear ROI

Implementing AI manufacturing automation levels across your company all at once is risky. Start with one pilot project where you can prove results quickly.

Here’s what makes a great pilot:

• It solves a meaningful problem. Reducing machine downtime, improving demand forecasting, and the list goes on.

• It has clear success metrics. The outcomes are measurable, and you’ve set KPIs like cost savings, efficiency gains, improved customer satisfaction, and so on.

• It’s achievable in several months. Don’t pick something that’ll take years to see results.

Once your pilot succeeds, you can scale from it.

03.

Involve Both Leadership and Frontline Teams

The adoption of artificial intelligence in manufacturing is, first of all, a business transformation.

Your leadership sets the vision. And frontline teams? They shape reality. Make sure to involve:

• Executives and managers to align AI with business strategy.

• Frontline workers to give feedback on what’s practical and what’s not.

• Cross-functional teams — IT, operations, finance — to make sure everything’s centralized.

When everyone in your company has a voice, you get better adoption and higher AI maturity.

04.

Partner with Experts Who Know Manufacturing and Custom AI

AI isn’t some add-on you can install and use, especially in manufacturing. Custom solutions get better results than generic, off-the-shelf tools.

The right partner can help you:

• Handle digital readiness assessment.

• Identify the best use cases for your specific workflows.

• Build custom AI models that work with your data and systems.

• Avoid wasting time and money on inefficient initiatives.

• Scale from pilot to full integration with ease.

That’s exactly what Integrio Systems helps companies do. We combine our profound AI expertise with the knowledge of the manufacturing industry to deliver solutions relevant to your business.

Conclusion

Regardless of where you stand on the AI maturity model, the key is to take the next step with purpose. Build a strong data foundation, start small, and scale smart — preferably with the right partner on board.

And in case you’re looking for one, contact Integrio Systems. We’ve been promoting data-driven manufacturing for over two decades. During that time, we’ve completed more than 200 projects — using AI in particular.

Let’s turn AI from a buzzword to tangible results, together.