Thermoforming Report

AI is Only as Smart as Your Operation

AI isn’t the solution – it is the enabler.  It is a tool to improve operations, not a savior that’s going to fix broken ones.  Like every other piece of equipment on your production floor – and every person on your team – it only delivers value when it’s used effectively. 

A short but insightful Forbes article prompted this month’s topic.  Forbes’ AI Won’t Fix Manufacturing Until Manufacturers Fix the Work (June 9, 2026) highlighted six places that need attention before AI can deliver real value:

1. Start with a Problem Worth Fixing
2. Get One Version of the Truth
3. Fix the Breaks Between Teams
4. Fix the Process Before You Automate It
5. Bring in the People Who Know the Shop Floor
6. Treat AI Like Continuous Improvement

We will dig into each one below and explore it further, but let’s discuss the upfront idea. At its core, AI still follows the old computer principle: ‘garbage in, garbage out’.  The meme below pretty much sums it up.

How will AI fix manufacturing that isn’t working well to begin with? 

It won’t. 

Not until manufacturers invest the time to correct inherent problems first and collect the right information efficiently. 

Once these fundamentals are in place, AI can dramatically improve decision-making, response time, and predict failures before they happen.

Start with a Problem Worth Fixing

BMW used AI to create a digital factory and simulated physical improvements before turning one wrench.  The article said it succinctly: “Pick a problem that is already costing the business money. Then ask what has to change for the problem to improve. A good AI use case starts with operating pain, a clear owner and a result the company can measure. Otherwise, the project becomes another technology experiment looking for a reason to exist.”

A personal example I’ve shared previously is way back when, as a Process Engineer, we were fighting a pervasive problem that we couldn’t figure out. It was a high-speed nonwovens machine and when one of the compaction rolls grabbed a random part of the fabric, it was all over in a matter of 2 seconds, and we were down. High-speed cameras weren’t typically used in this type of application, but I gained approval to try something different. We were able to identify the problem and fix it. More importantly, we applied that temporary solution across the machine so that we could begin predicting failures and correcting them before they resulted in costly downtime.  

AI – just like the cameras in my example – doesn’t fix the problem. It helps you identify it faster so you can fix it faster.

Get One Version of the Truth

“AI is only useful if the operation has a shared view of what is happening. Bosch’s Bamberg plant shows what that looks like. Cameras, sensors and test stations feed production data into AI-supported analysis.  Most manufacturers don’t have that. They’re still living with multiple versions of the truth. One number in the ERP. Another in a spreadsheet. A third in someone’s head. AI will not reconcile that by magic. But it will force the company to decide which version is real. Bad data doesn’t become good because AI touches it. It just becomes bad data moving faster.”

Forbes

Exactly. The problem in the example above could have multiple reasons for failure. Each crew had their own ideas.  The Process Engineers on the other machines had their ideas.  I had my thoughts and Corporate Engineering had theirs.  Capturing the right information in real time was what made the difference. No confusion. No discrepancy. Clear data that we could all agree on, look at, and collectively generate the right changes to fix root cause.

Fix the Breaks Between Teams

“Lenovo says the system cut schedule planning from two hours to two minutes and increased production volume by 19%. That kind of scheduling only works when teams are connected. Sales has to know what production can actually make. Purchasing has to know what materials are missing. Production has to know when priorities change.”

Forbes

I am working with a client that is struggling in this area, and it is generating frustration that needs to be corrected. Without alignment and constructive communication across all functions, things break down – and that all-important growth curve begins to flatten.  A company doesn’t run well this way “no matter how smart the software is.”

Fix the Process Before You Automate It

This is the crux of the entire article in my opinion.  The article poses the question, “Why does the work happen this way?”  It gets back to good old Lean Six Sigma principles and the seven “TIMWOOD” wastes. Every production process has flaws, and the reasons are many. Why invest the time and resources in automation when you should be investing in solutions and fixing foundational problems?

“AI should not become expensive duct tape for broken workflows. The goal should be to redesign the work so better decisions become easier to repeat.“

Forbes

Bring In the People Who Know the Shop Floor

“The people doing the job know the difference between the official process and the real one. They know which data is wrong, which alerts people ignore and which “temporary” process quietly became standard practice.” 

Forbes

Man, oh man, this is true! If you’ve ever spent any time on the production floor, you’ve lived this every day.  During my time working on that nonwovens machine in the example above, I invested a significant amount of time gathering information to develop a centerline document that every crew could use.  I had a great rapport with the manufacturing team and asked them about what worked and why. By combining their firsthand experience with my own training and engineering background, we created a practical standard that every crew could trust and follow. 

[As a side note, this is exactly what thermoform.ai strives to do: they capture tribal knowledge before it walks out the door.  Check out the link in graphic at the end of this report.]

Treat AI like Continuous Improvement

“Continuous improvement is where manufacturers excel. They know how to look at a process, find what’s slowing it down, fix what’s broken, measure the result and keep improving.  AI should be treated the same way. AI has to earn its place in the factory. It has to solve a real problem, work for the people doing the job and make the operation better in a way people can see.” 

Forbes

The challenge is that all of the fundamentals we’ve already discussed need to be in place before AI can deliver meaningful results. 

The article closes by saying, “Before AI helps a factory run smarter, it needs to show leaders what to fix under the hood.”  I would argue the opposite. Before AI is implemented, leaders need to fix what’s under the hood. Then AI can do what it’s designed to do – make a well-run operation even better.

Wrapping Up

If you aren’t figuring out how to leverage AI on your production floor, you are falling behind. Just don’t make the mistake of ‘putting lipstick on a pig’ – using AI to disguise broken processes instead of fixing them. AI isn’t superficial or a cosmetic improvement.  It’s a significant investment of time, money, and resources. 

AI isn’t the solution. It never will be. It’s the enabler.

Give it a strong process, good data, and engaged people, and it will help you build an even stronger manufacturing operation.

The ‘check engine light’ analogy in the article really hits home. AI isn’t there to replace the mechanic. It’s there to tell you where to look first. The repair is still up to you.

At Siena Group, we are your Thermoforming Talent Partner. With more than 30 years of experience in manufacturing, hiring & recruiting talent, we bring a greater understanding of the companies we partner with and the candidates we pursue.
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