We analyzed the report “Building an AI Advantage in Packaging Equipment”, published by PMMI (Association for Packaging and Processing Technologies), based on interviews with experts from across the packaging value chain. Here are the highlights.

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Areas where AI has advanced the most

Knowledge transfer

When experienced workers leave, they take especially valuable knowledge with them: the tricks and informal solutions for solving problems quickly, the knowledge about old systems that were never properly documented, and that intuition that allows problems to be detected before alarms go off.

The AI solution captures this knowledge:

• Recording interviews when someone leaves the company.
• Enabling operators to record solutions by voice while working.
• Organizing all this information in databases accessible from mobile devices.

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Any worker can ask questions in natural language and the system provides relevant information immediately.

Machine Vision

Companies are moving from rigid inspection systems that generate many line stoppages to high-performance adaptive systems.

Today’s AI systems achieve defect detection rates above 99% and have reduced false rejects by up to 50%, which is crucial because every false reject means wasted product.

But the applications go far beyond that:

• Current systems enable intelligent roboticpicking that identifies individual products in cluttered stacks
• They determine, something especially valuable in food processing the best way to grip.

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Predictive maintenance

Modern AI-driven systems continuously learn from real data, understand which patterns are of real concern, predict failures and recommend specific corrective actions.

The integration of IoT sensors, the development of explainable AI that justifies its recommendations, and video capture during maintenance events have enabled these systems to prioritize failure hypotheses and send action guides directly to the appropriate engineer.

There is one major barrier: many companies are reluctant to share their data to train models because of cybersecurity concerns.

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4. Regulation and compliance

A medium-sized company may receive dozens of different questionnaires about compliance with regulations such as REACH, RoHS, PFAS or the new European packaging regulation. Managing this manually is inefficient, time-consuming and error-prone.

AI centralizes all regulatory data and automates responses, reducing response time by up to 90%, from 4-6 days to almost immediate. This frees the compliance team from administrative tasks to focus on proactive regulatory monitoring.

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5. Data Transparency

AI tools for data transparency automatically categorize documents, eliminate redundancies and create relational structures that enable advanced analytics.

This is strategically critical because clear data structures are critical for other AI solutions to work effectively:

• Predictive maintenance needs clean historical data.
• Compliance automation needs fast access to material information.
• Knowledge transfer needs information organized in a retrievable way.

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Future of AI in packaging

Specialization

We are seeing a proliferation of specialized AI vendors. Some specialize by industry, others by specific application such as predictive maintenance or machine vision.

The result is that industrial companies are starting to use multiple different AI platforms simultaneously, each optimized for a specific function.

This creates a new need: orchestration. When you have five or six different AI systems operating, you need some way to coordinate and prioritize them.

Technological evolution

In the coming years the trajectory will move from isolated optimization to coordinated orchestration. Instead of systems that optimize individual variables, we will have plant-level systems that dynamically balance performance, quality, cost and energy consumption across each stage of production.

• Predictive maintenance will evolve to prescriptive recommendations that schedule the optimal intervention window while minimizing the impact on production.
• Conversational AI interfaces will democratize sophisticated analytics: a line operator will be able to ask the system why efficiency is down and receive a clear, actionable answer without the need for a data engineer to run complex analysis.
• Machine vision will detect subtle defects invisible to human eyes, going on to identify early indicators of problems before they become visible defects.