AI Research
5 ways AI is shaping packaging today
In Nestlé’s R&D department, a tool is identifying entirely new kinds of high-barrier packaging materials. It’s generative AI.
A growing number of consumer packaged goods companies are starting to deploy artificial intelligence in their packaging design processes. So too are packaging manufacturers exploring how the technology can save time and resources in design and manufacturing.
Nestlé’s researchers feed public and proprietary documents into a knowledge base. Then, they fine-tune the data using a transformer, together with IBM Research, to understand how molecular features in packaging correlate to physical properties. Finally, the AI-based model analyzes the inputs within the set parameters.
“It can identify appropriate materials that are suitable for protecting dry and sensitive foods such as coffee from moisture, temperature swings and oxygen,” a Nestlé spokesperson said via email.
While the use cases are nascent, AI is catching on. Last year, a McKinsey survey of more than 200 paper and packaging executives found that 95% said they believe their companies should invest in generative AI. And 77% said their firms have moderate to strong intentions to use gen AI in the near future.
How packaging leaders and CPGs are using AI
Tom Egan, vice president of industry services for the Association for Packaging and Processing Technologies, or PMMI, sees “a pretty enthusiastic acceptance” of AI in the industry. He said applications range from logistics to sales and marketing to manufacturing.
Here are some of the processes in which packaging companies and CPGs are starting to use AI.
New designs and prototypes
One of the notable potential benefits of AI — and among the biggest use cases being explored today — is the ability to assess packaging modifications or updates in a virtual environment.
When a client requests a new design from flexible packaging provider Tradepak, the company inputs the customer’s parameters into its systems, said President Rafael Recao. AI can rapidly pull from databases, synthesize data points, and deliver design and substrate recommendations. Then, packaging leaders make a final decision.
With simulation models and digital twins, AI can look at form factors, examine touch points and predict consumer reactions to quickly generate 10 mockups of a SKU’s packaging, Egan said, rather than physically making and testing each version.
AI can “analyze this unbelievably large number of data points and provide you with insights,” Egan said, adding that packaging leaders can refine the options from there.
Simulation models can also help to design packaging for the same product in different channels, such as one version for retail shelves and another for e-commerce. AI could determine the best design and protective properties for each channel while ensuring brand consistency, Egan said.
Simulating testing
Colgate-Palmolive is exploring how simulations could validate new designs for components such as bottles, caps and spray pumps, said Sukhdev Singh Saini, global toothbrush and devices packaging lead at the company.
Without AI, “we have to physically make a lot of samples to test,” Saini said. Then, the samples have to be shipped, tested and possibly altered before being sent out for another round of testing. “There is a lot of time involved, a lot of material involved.”
With a simulation model, though, the CPG feeds specs into a system, and AI replicates testing to provide results. Saini acknowledged the tech is still a work in progress and not a 100% replacement just yet.
Jonathan Garini, CEO and enterprise AI consultant at Fifthelement, sees brands using algorithms to simulate how a substrate performs in real-life conditions like humidity or rough handling.
“This not only accelerates the test cycle but also can lower the cost of physical prototyping significantly,” Garini said.
Packaging artwork
Recao said Tradepak uses Adobe Illustrator equipped with AI to make adjustments on colors, design placement and other elements before printing.
For artwork, Colgate-Palmolive works with Esko software, which uses automation throughout the packaging go-to-market process. The use of AI in artwork management has improved quality while saving time, Saini said.
Without the system, the CPG would have to individually search previous artwork details to make even minor modifications. But now, the person who manages artwork can receive data in minutes and make decisions more efficiently. In a major run of 50 or more SKUs, Saini said Colgate-Palmolive has cut down the development time by 60% to 70%.
Assessing recyclability
Colgate-Palmolive has partnered with Glacier on an AI-based system for sorting toothpaste tubes at recycling facilities. The CPG can view a dashboard that shows toothpaste tube materials and their recyclability, along with tube recycling behavior in various cities.
With more states adopting extended producer responsibility programs, Saini anticipates “a lot of work being done on recyclability.”
Another generative AI use case that McKinsey noted in its report is to enhance visual inspection systems for waste, in order to improve the quality of recycled paper and cardboard.
Manufacturing, production and maintenance
Many CPGs use computer vision, optical systems and imaging technology on production lines. It’s not a new phenomenon, but machine learning is “taking it a couple of notches up” with AI-based features that improve quality management systems, Saini said.
At Nestlé, high-resolution imaging technology monitors lines for quality assurance.
“Through machine learning, these technologies can anticipate issues on the production line, and provide appropriate recommendations, such as preventative maintenance or cleaning,” Nestlé’s spokesperson said.
Recao uses augmented reality glasses to connect with manufacturing facilities in Europe so Tradepak can monitor any equipment issue in real time.
“We save a lot of time,” Recao said. “We resolve the problem quickly.”
What’s next with AI in packaging
It’s still early days for AI in packaging, and many areas remain untapped to their fullest potential. Garini sees AI being used to predict how consumers perceive packaging, and how that affects sales, including the use of packaging as a communication tool.
“Models now are being trained to predict how form factor, texture and color all play roles in shelf appeal and online conversion,” Garini said.
There are also some barriers to adoption.
Implementing new technology requires change management and for employees to embrace and trust the system. Cybersecurity could be a concern with AI models receiving proprietary data, and software subscriptions can be expensive, Recao noted.
AI outputs are only as good as their inputs, which means organizations need a solid foundation of data, Egan said. In fact, McKinsey’s survey found that limited access to a modern data tech stack was the top limitation in AI implementations.
But CPGs and packaging manufacturers remain bullish on AI’s potential.
Nestlé envisions its technology will help to scale packaging solutions across more categories. Saini believes the tools Colgate-Palmolive uses will save resources and improve recyclability. Recao said there’s always more advanced AI that can apply to robotics and machinery.
“Right now, the potential of AI in our industry is enormous,” Recao said. “The sky is the limit.”
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(Policy Address 2025) HK earmarks HK$3B for AI research and talent recruitment – The Standard (HK)
AI Research
[2506.08171] Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
View a PDF of the paper titled Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models, by Daniel Koh and 4 other authors
Abstract:Large language models (LLMs) have demonstrated strong performance on coding tasks such as generation, completion and repair, but their ability to handle complex symbolic reasoning over code still remains underexplored. We introduce the task of worst-case symbolic constraints analysis, which requires inferring the symbolic constraints that characterise worst-case program executions; these constraints can be solved to obtain inputs that expose performance bottlenecks or denial-of-service vulnerabilities in software systems. We show that even state-of-the-art LLMs (e.g., GPT-5) struggle when applied directly on this task. To address this challenge, we propose WARP, an innovative neurosymbolic approach that computes worst-case constraints on smaller concrete input sizes using existing program analysis tools, and then leverages LLMs to generalise these constraints to larger input sizes. Concretely, WARP comprises: (1) an incremental strategy for LLM-based worst-case reasoning, (2) a solver-aligned neurosymbolic framework that integrates reinforcement learning with SMT (Satisfiability Modulo Theories) solving, and (3) a curated dataset of symbolic constraints. Experimental results show that WARP consistently improves performance on worst-case constraint reasoning. Leveraging the curated constraint dataset, we use reinforcement learning to fine-tune a model, WARP-1.0-3B, which significantly outperforms size-matched and even larger baselines. These results demonstrate that incremental constraint reasoning enhances LLMs’ ability to handle symbolic reasoning and highlight the potential for deeper integration between neural learning and formal methods in rigorous program analysis.
Submission history
From: Daniel Koh [view email]
[v1]
Mon, 9 Jun 2025 19:33:30 UTC (1,462 KB)
[v2]
Tue, 16 Sep 2025 10:35:33 UTC (1,871 KB)
When artificial intelligence (AI) can help you retrieve literature, support your research, and even act as a “super assistant”, university education is undergoing a profound transformation.
On 9 September, XJTLU’s Centre for Knowledge and Information (CKI) hosted its third AI Learning Day, themed “AI-Empowered, Ecosystem-Co-created”. The event showcased the latest milestones of the University’s “Education + AI” strategy and offered in-depth discussions on the role of AI in higher education.
In her opening remarks, Professor Qiuling Chao, Vice President of XJTLU, said: “AI offers us an opportunity to rethink education, helping us create a learning environment that is fairer, more efficient and more personalised. I hope today’s event will inspire everyone to explore how AI technologies can be applied in your own practice.”
Professor Qiuling Chao
In his keynote speech, Professor Youmin Xi, Executive President of XJTLU, elaborated on the University’s vision for future universities. He stressed that future universities would evolve into human-AI symbiotic ecosystems, where learning would be centred on project-based co-creation and human-AI collaboration. The role of educators, he noted, would shift from transmitters of knowledge to mentors for both learning and life.
Professor Youmin Xi
At the event, Professor Xi’s digital twin, created by the XJTLU Virtual Engineering Centre in collaboration with the team led by Qilei Sun from the Academy of Artificial Intelligence, delivered Teachers’ Day greetings to all staff.
(Teachers’ Day message from President Xi’s digital twin)
“Education + AI” in diverse scenarios
This event also highlighted four case studies from different areas of the University. Dr Ling Xia from the Global Cultures and Languages Hub suggested that in the AI era, curricula should undergo de-skilling (assigning repetitive tasks to AI), re-skilling, and up-skilling, thereby enabling students to focus on in-depth learning in critical thinking and research methodologies.
Dr Xiangyun Lu from International Business School Suzhou (IBSS) demonstrated how AI teaching assistants and the University’s Junmou AI platform can offer students a customised and highly interactive learning experience, particularly for those facing challenges such as information overload and language barriers.
Dr Juan Li from the School of Science shared the concept of the “AI amplifier” for research. She explained that the “double amplifier” effect works in two stages: AI first amplifies students’ efficiency by automating tasks like literature searches and coding. These empowered students then become the second amplifier, freeing mentors from routine work so they can focus on high-level strategy. This human-AI partnership allows a small research team to achieve the output of a much larger one.
Jing Wang, Deputy Director of the XJTLU Learning Mall, showed how AI agents are already being used to support scheduling, meeting bookings, news updates and other administrative and learning tasks. She also announced that from this semester, all students would have access to the XIPU AI Agent platform.
Students and teachers are having a discussion at one of the booths
AI education system co-created by staff and students
The event’s AI interactive zone also drew significant attention from students and staff. From the Junmou AI platform to the E
-Support chatbot, and from AI-assisted creative design to 3D printing, 10 exhibition booths demonstrated the integration of AI across campus life.
These innovative applications sparked lively discussions and thoughtful reflections among participants. In an interview, Thomas Durham from IBSS noted that, although he had rarely used AI before, the event was highly inspiring and motivated him to explore its use in both professional and personal life. He also shared his perspective on AI’s role in learning, stating: “My expectation for the future of AI in education is that it should help students think critically. My worry is that AI’s convenience and efficiency might make students’ understanding too superficial, since AI does much of the hard work for them. Hopefully, critical thinking will still be preserved.”
Year One student Zifei Xu was particularly inspired by the interdisciplinary collaboration on display at the event, remarking that it offered her a glimpse of a more holistic and future-focused education.
Dr Xin Bi, XJTLU’s Chief Officer of Data and Director of the CKI, noted that, supported by robust digital infrastructure such as the Junmou AI platform, more than 26,000 students and 2,400 staff are already using the University’s AI platforms. XJTLU’s digital transformation is advancing from informatisation and digitisation towards intelligentisation, with AI expected to empower teaching, research and administration, and to help staff and students leap from knowledge to wisdom.
Dr Xin Bi
“Looking ahead, we will continue to advance the deep integration of AI in education, research, administration and services, building a data-driven intelligent operations centre and fostering a sustainable AI learning ecosystem,” said Dr Xin Bi.
By Qinru Liu
Edited by Patricia Pieterse
Translated by Xiangyin Han
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