In today’s fast-moving food production environment, automation has evolved from a competitive advantage to a business imperative. Food manufacturers are leveraging automation to speed up production and transform operations from the ground up.

This transformation is increasingly being powered by artificial intelligence (AI), which allows machines to adapt, learn and support human labor in ways that conventional automation never could. From machine vision and predictive maintenance to digital twin simulations and connected worker platforms, cutting-edge automated systems are redefining what’s possible in food manufacturing.

The Rise of Smart Automation

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Traditional automation offered consistency, but not adaptability. Today’s intelligent systems do both.

AI-enhanced machine vision, for example, enables quality control even in highly variable product environments, according to “The AI Advantage in Equipment: Boosting Performance and Bridging Skills Gaps” white paper from PMMI, The Association for Packaging and Processing Technologies. Yango Robotics implemented AI-based palletizing and depalletizing using computer vision, enabling a warehouse to identify and move tens of thousands of stock-keeping units — regardless of stacking pattern or box shape — cutting cost per pick by 42% and doubling throughput.

In another case highlighted in the PMMI white paper, Sollas, with support from Siemens, used an AI-powered digital twin to simulate machine performance. This virtual model allowed engineers to optimize conveyor timing and eliminate stop-start problems in a packaging process. The result? A 33% increase in output — boosting throughput from 120 to 160 boxes per minute.

Closing the Labor Gap with Technology

The food production industry faces a well-documented labor challenge. As skilled technicians retire, manufacturers struggle to replace headcount and institutional knowledge.

As the white paper points out, connected worker platforms such as Augmentir can help bridge that gap. In one global packaging company, this AI-powered platform guided less-experienced employees through complex tasks, cut onboarding time by 72%, and improved overall workforce productivity by 18%. These platforms also use real-time data to assess proficiency, tailor training programs, and offer digital support assistants — ensuring each worker has what they need to succeed, regardless of their experience level.

Predictive maintenance, another tool in the automation arsenal, uses AI to monitor machinery and flag anomalies before failure occurs. A packaging firm, using Senseye’s platform, prevented over 100 hours of downtime per site annually without installing additional sensors. The software “learned” normal performance using existing machine data, enabling scalable maintenance across multiple facilities.

Integration Overhaul: Meeting the Reality of Existing Infrastructure

For many food processors, the barrier to advanced automation isn’t lack of interest — it’s physical and financial constraints. Legacy systems, tight floor plans and high upfront costs can discourage adoption.

This is where modular, scalable automation steps in. Increasingly, manufacturers are turning to mobile robotics and open-architecture platforms that can adapt to existing equipment. Such systems lower the cost of entry and allow companies to automate incrementally, evolving alongside their operational needs.

At the same time, companies are prioritizing upskilling. Partnerships with technical schools and in-house training programs are cultivating a workforce fluent in legacy systems and new tech. This dual fluency is essential for maximizing ROI and minimizing resistance during implementation.

Navigating the Challenges

Even the most forward-thinking automation strategies face hurdles, chief among them being cybersecurity, data integrity and user trust.

AI systems thrive on data — but managing that data safely and effectively is challenging, especially for applications such as predictive maintenance that require continuous monitoring. AI outputs can be skewed or unreliable without a robust infrastructure to store and organize this data. There’s also the issue of “data hallucination,” where AI models generate incorrect conclusions — an error that could result in costly downtime or even safety risks.

Another challenge is cultural resistance, particularly among older employees. For workers used to manual controls, relying on algorithms may trigger skepticism. This is where transparency and training are vital. Emphasizing how automation augments rather than replaces human expertise helps build trust and adoption.

Experts recommend starting small. Identify one or two high-impact areas where ROI is measurable — like using a generative coding assistant to streamline engineering workflows or optimizing a machine layout using a digital twin. Once early wins are achieved, momentum builds, and adoption becomes more scalable.

OEMs and the Next Frontier

For original equipment manufacturers (OEMs), automation’s potential goes beyond operational improvement — it’s a growth opportunity.

OEMs like Siemens are developing proprietary AI software to integrate into their systems, according to the PMMI white paper. Some are even piloting “Machinery as a Service” (MaaS) models, where predictive maintenance allows them to guarantee uptime while maintaining long-term service relationships. This shift from product to platform thinking could redefine how equipment is sold and supported.

In the long run, AI will allow for the creation of adaptable, self-optimizing systems that can handle varied inputs, unpredictable product types and changing environments — without manual intervention.

As the food industry continues to evolve, PACK EXPO Las Vegas 2025 (Sept. 29-Oct. 1) provides an opportunity to see the latest automation innovations in action. With more than 2,300 exhibitors spread across 1 million net sq. ft., the event showcases cutting-edge solutions for food and beverage, baking, snack and other vertical markets. More than 35,000 professionals are expected to attend, representing more than 40 vertical markets.