Artificial intelligence is gaining a firmer foothold in packaging operations as lower costs, better functionality and growing familiarity push companies beyond small-scale trials and toward wider use, according to a new white paper from the Association for Packaging and Processing Technologies (PMMI).
The 2026 report “Building an AI Advantage in Packaging Equipment” draws on 14 interviews with AI vendors, automation companies, packaging machine OEMs, system integrators, end users and investment bankers. It says AI use in packaging has broadened since PMMI’s 2024 paper, with companies moving past proof-of-concept work and exploring tools that address practical production and administrative problems.
In materials sent with the report, PMMI said lower costs and increased accessibility, higher awareness, stronger confidence in the technology and greater worker acceptance are helping drive that shift.
“Manufacturers across the packaging value chain are recognizing that AI can help address some of their most pressing challenges, from workforce knowledge gaps to operational efficiency,” Jorge Izquierdo, PMMI’s vice president of market development, said. “What we’re seeing now is a shift from isolated pilots toward broader adoption, where AI supports smarter, more connected production environments.”
The report identifies five areas where AI has made the most significant strides in packaging: knowledge transfer, machine vision, predictive maintenance, regulation and compliance and data transparency. It says knowledge transfer and predictive maintenance were cited most often by interviewees as the tools likely to have the strongest positive impact on the sector over the next few years.
Knowledge transfer tools are drawing attention because they can capture and organize operational know-how that might otherwise leave with experienced workers. PMMI cites earlier research showing 95% of end users struggle to find skilled operators and technicians, while nearly 60% expect those workforce challenges to become somewhat or more difficult.
Machine vision is also emerging as a more important use case. The paper says AI-powered visual inspection can improve quality control, reduce nuisance stoppages and detect smaller irregularities than earlier systems, helping operations improve throughput and cut waste.
Predictive maintenance remains a major focus and PMMI cites its 2022 report, “Challenges and opportunities for packaging and processing operations,” showing 43% of consumer packaged goods companies currently use predictive maintenance and 45% plan to adopt it within three years. The paper says AI is strengthening those systems by learning from real machine data rather than relying only on narrow thresholds for what counts as normal performance.
Even so, the report makes clear that wider adoption is not without friction. It ranks internal attitudes toward AI as the top barrier in 2026, followed by accountability for errors, cybersecurity, return on investment and latency challenges.
That accountability question appears to be growing more important as AI moves closer to daily operations. The report says smaller and mid-sized companies in particular are wary of taking on legal and financial risk when responsibility for AI-generated mistakes remains unclear. It also says many end users favor software-as-a-service models because they see those arrangements as shifting more responsibility to the provider.
Looking ahead, the paper argues the next phase of adoption will involve more connected AI systems working across production environments rather than isolated tools handling single tasks. For packaging companies, the report suggests the challenge is no longer whether AI has a place in operations, but how quickly they can put the right foundations in place.
