---
title: AI in Packaging Design – C.A.P. S4E2 | ICPG
description: Explore how AI in packaging design supports material screening, cost modeling, testing and defect detection, and where human expertise still matters.
image: https://blog.icpg.co/hubfs/cap-s4e2.jpg
---
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[](https://icpg.co) 
### October 13th, 2026
# Crazy About Packaging Season 4, Episode 2: AI in Packaging Design and Material Selection
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AI has made its way into just about every corner of the business world, and packaging is no exception. Depending on who you ask, it’s either going to change how the industry works or it’s wildly overhyped.
We’ve covered AI on the podcast before, back in [Season 3, Episode 9](https://blog.icpg.co/crazy-about-packaging-s3e9-ai-and-recycling) with a guest from AMP Robotics. That conversation looked at the end of the packaging life cycle and how AI is being used to improve sorting and recycling rates. This time we’re looking at the other end: AI in packaging design, material screening, cost modeling and everything that happens long before a package reaches a consumer’s home.
This episode gets into where AI is making packaging development faster, where it still needs a human with real experience sitting on the other side of it, and what all of this might mean for the people doing the work. Watch the full episode above, or listen on [Spotify](https://open.spotify.com/show/3pA2sXoOQCPeiTXYauGMNo), [Apple Podcasts](https://podcasts.apple.com/us/podcast/crazy-about-packaging-conversations-with-the-c-a-p-pack/id1670403607), or on [our website](https://icpg.co/podcast/).
## Sneak Peek for Season 4, Episode 2
Here’s a preview of a few moments from the conversation.
### AI or Old School? A Lightning Round to Start
We opened with a game called “AI or Old School.” Natalie read out packaging scenarios, and the rest of the team had to decide whether we’d hand the task to AI or stick with human brains and hands-on methods.
Here’s what was on the table:
- Narrowing 50 possible materials down to 5 worth investigating
- Predicting the shelf life of a new food package
- Troubleshooting why a material isn’t running correctly on a form fill seal line
- Identifying defects coming off a production line
The answers weren’t as clean as you might expect, and at least one of them split the group. But a pattern showed up almost immediately: AI to get from a big pile of options down to a short list, humans to decide what makes the short list worth pursuing.
“To get to the five options, AI is a terrific vehicle,” Mike said. “If used well and organized well and prompted well, I would say AI **then** old school.”
Watch the episode to hear where each of us landed. Where do you fall on these?
### How Is AI Being Used in Packaging Design Today?
A few use cases came up that are already in practice rather than theoretical.
Market and competitive research is the most obvious one. Reports that used to cost thousands of dollars and take months to commission can now be produced in a morning. Companies are also building internal repositories of specifications and quality system documents, then using large language models to search and interpret them, which turns a week of digging into a few minutes.
On the equipment side, AI has quietly improved how operators are trained to run machines, which means customers get more value out of equipment they’ve already bought.
And defect detection has been around longer than most people realize. High speed cameras checking seal integrity on every container coming down a line were in plants twenty years ago, well before anyone was calling it AI.
“It’s starting to speed up the development process, and that’s exciting, because that’s a big challenge within the industry,” Jonathan said, pointing to how often the ball gets tossed back and forth between supplier and customer during development.
Trade coverage backs this up. Packaging Dive has reported that large consumer goods companies are using AI in prototype development, simulated testing and recyclability assessment. The common thread is using AI early in the [packaging development process](https://blog.icpg.co/packaging-development-process-explained-icpg) to screen possibilities before teams invest in physical trials.
### The Output Is Only as Good as What You Put In
Ask an AI tool to find you a chemically recyclable alternative to a conventional food packaging polymer and it will give you an answer. But is it a good one? If you only ask for a material type, but neglect to ask whether the material can be sourced, used on the line, and effectively collected and recycled, you may find that the answer you get isn’t usable.
Researchers at Cornell used AI and machine learning to find chemically recyclable replacements for conventional food packaging polymers. The material the AI landed on was poly(p-dioxanone), or poly-PDO. None of us had ever heard of it.
On paper, it may well be a strong candidate. In the real world, a material that nobody produces at scale isn’t going to get recycled chemically or any other way, because your package would be the only one of its kind in existence. The AI wasn’t wrong so much as it was never told what “recyclable” has to mean in practice: existing infrastructure, existing volume, existing end markets.
That’s the whole lesson in one example. If you don’t tell the model about your shelf life requirements, your pricing constraints, your processing conditions and the recycling standards your package has to meet, it will happily optimize for something you can’t actually buy.
For AI-assisted material screening to be useful, the model needs [the same constraints a packaging team would consider in a real project](https://blog.icpg.co/food-packaging-design-sustainability-cost-performance-shelf-life), including:
- Shelf life requirements
- Processing conditions
- Pricing constraints
- Regulatory requirements
- Recycling infrastructure and end-market realities
- Performance requirements for the intended application
“If it’s garbage on the way in, it’s going to be garbage on the way out,” Mike said. “But if it’s solid on the way in, you can do something with it.”
The corollary is that well organized inputs produce useful outputs. Jonathan has spent years building detailed cost models in Excel, and because those models were consistent and well structured, AI tools could read and work with them. When the team tried to rebuild one of those cost models with AI from scratch, though, they got close and couldn’t quite get there. Too many inputs live in someone’s head rather than in a file.
### Where Real World Validation Still Wins
Shelf life is a good example of the limit. AI can interpret transmission rate data and give you a reasonable prediction of how a product holds up under a given set of conditions. It cannot open the package and smell it. Sensory testing, physical validation and the old standby tests are still doing work that no model replaces.
Form fill seal troubleshooting is similar, though with a twist. Mike explained that he expected this validation to remain firmly in the old school camp until he actually tried it, fed the output to the most experienced people he knows, and found that it pointed everyone in a useful direction faster. The complication? The actual machines on the line. Two identical units from the same manufacturer don’t behave identically, and the person who’s run one for fifteen years knows things about it that were never written down anywhere.
“I often refer to it as a fingerprint,” Mike said. “It’s so unique that the performance of a machine is unique, even within its type category and manufacturer.”
Natalie’s summary of AI’s benefit stuck with us: “It’s like a compass. It can point you in the right direction, but it’s not going to draw the map for you.”
### Design Optimization and Lightweighting
Design optimization is one of the more promising front-end use cases, and lightweighting is the clearest example of it.
The question a packaging team actually wants answered is how much weight can come out before performance starts to suffer. Performance modeling and 3D modeling can give you a defensible starting point, and from there the path is fairly direct: take the specs, generate a CAD drawing, 3D print a mold, thermoform some containers in the recommended [material](https://icpg.co/materials-and-applications/) and run real tests on them.
What the model can’t tell you is whether pulling that weight out created a problem somewhere else. Stiffness, sealing, stacking, line performance, drop resistance. Optimizing one variable tends to move several others, and those have to be physically validated.
### What This Means for the People Doing the Work
AI is everywhere, and lots of people have questions about the future of our industries. Are the robots coming to take over? And if they do, what happens when they don’t know the answers?
Here’s the thing: every argument for using AI for package design and development ultimately depends on someone being qualified to check its work. The roles that involve judgment, risk assessment and decision making look fairly secure. Roles built primarily around processing information are more likely to change in how the work gets done, and that applies across finance, laboratory analysis and design alike.
The harder question is about experience. Someone who has spent decades working hands-on with form fill seal equipment can look at an AI recommendation and immediately tell whether it’s credible. That judgment came from years of doing the work.
“Somebody that just reads an AI paper on ‘okay, I should have pressed that button,’ is not going to really know that you just impacted five other variables,” Jonathan said.
So if AI absorbs the entry-level work where people used to build that foundation, where does the next generation of expertise come from? We didn’t land on an answer, but it’s a question that the industry needs to consider.
Related to that is the tribal knowledge problem, which the plastics industry has been discussing for years. As experienced people retire, organizations lose knowledge that was never documented anywhere. There’s a real opportunity to capture that in a repository before it walks out the door, and it reduces key person risk considerably. It also requires people to agree to hand over what they know, which is its own conversation entirely.
### Use It, but Don’t Outsource the Decision
AI is a tremendous tool, and by all accounts we’re still scratching the surface of what it can do. We may also be scratching the surface of how to misuse it, which is worth keeping in mind.
The practical takeaway from this episode is fairly simple. Be deliberate about the information you put in, constrain it with your actual requirements, and treat everything that comes back as input rather than as an answer.
“You can let the large language models inform you,” Mike said. “But you can’t let them make the decisions for you.”
Thanks for getting a little crazy about packaging with us! Watch the full episode above or catch it on [Spotify](https://open.spotify.com/show/3pA2sXoOQCPeiTXYauGMNo), [Apple Podcasts](https://podcasts.apple.com/us/podcast/crazy-about-packaging-conversations-with-the-c-a-p-pack/id1670403607) or [our website](https://icpg.co/podcast/), and be sure to subscribe so you don’t miss the rest of Season 4.
## Working Through a Material Decision?
AI can help you narrow the field. Validating that a material will actually run on your lines and hold up in your application is a different kind of work. If you’re evaluating materials or planning a transition, [talk with the ICPG team](https://icpg.co/about-us/contact-us/) about what you’re trying to solve.
## Frequently Asked Questions About AI in Packaging Design and Material Selection
### How is AI used in packaging design and material selection?
AI can help narrow material options, support research and modeling, search specifications, and identify defects during production. It’s also used in quality assurance, where camera systems identify defects and seal integrity issues on high speed lines.
### Can AI replace material testing and qualification?
No. AI can predict and prioritize, but material qualification still requires physical validation. Shelf life, sensory results, line performance and food safety all have to be confirmed through real testing, and qualification timelines are still measured in months to years.
### Why does input quality matter so much when using AI for material screening?
AI evaluates against whatever constraints it’s given. If shelf life requirements, processing conditions, pricing limits, regulatory requirements and recycling infrastructure realities aren’t specified, the model can return technically valid materials that are commercially unusable. The value of the output depends directly on the quality and completeness of the input.
### Can AI troubleshoot a form fill seal line?
AI can be a useful starting point for generating possible causes, and it works best when paired with someone experienced who can validate the suggestions. Individual machines have performance quirks that aren’t documented anywhere, so hands-on knowledge is still required to interpret and act on what AI suggests.
### Will AI replace packaging industry jobs?
Roles centered on judgment, risk assessment and decision making are unlikely to be replaced. Roles built around processing information are more likely to change in how the work is performed. A longer-term concern is how newer professionals build the hands-on experience needed to evaluate whether AI output is trustworthy.
## Interested in reading more?
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