Unlocking Success: How AI Will Transform Food Processing by 2025

In his book, “Artificial Intelligence Will Revolutionize Manufacturing,” Markus Guerster opens with a reminder that we’re still at the beginning of the Industry 4.0 journey. A decade ago, companies started to embrace digital technologies to improve production. This shift led to a wealth of enterprise data that organizations now need to manage.

As we look ahead to 2024, food manufacturers are eager to leverage Industry 4.0 for their AI strategies. However, this progress is happening rapidly, echoing the initial excitement of 10 years ago.

Guerster’s book, released in 2024, provides a clear guide to AI technology, covering topics like machine learning, deep learning, and generative AI, including ChatGPT. He also discusses the importance of understanding the Industry 4.0 flywheel, return on investment (ROI), and different learning methods in AI.

We had a conversation about his insights on AI in the food industry and what the future might hold.

FOOD ENGINEERING: What’s the current state of AI adoption in the food industry as we move into 2025?

Markus Guerster: The push for AI in manufacturing feels like a collective effort. There’s pressure to adopt AI, but many organizations struggle with how to start. My book aims to provide guidance in this area.

Recently, I’ve observed a shift in how executives view AI investments. Initially, there was excitement, but now companies are more cautious after trying various AI products that didn’t meet expectations. We’re moving past the hype phase, and companies are beginning to see what works and what doesn’t.

FE: How are food manufacturers approaching AI technology with agile methods?

MG: I believe in tackling small, specific problems first. Start simple and keep iterating until you find a solution. When plant managers see the success of small pilot projects, the mindset begins to change.

It’s also becoming more affordable to experiment with AI. Gone are the days when you needed large teams or major investments. Now, tools like ChatGPT are accessible for trial, which can help teams learn quickly, even if they don’t immediately solve operational issues.

FE: Data governance is crucial for implementing effective AI. How should companies prepare?

MG: Quality data is the cornerstone for anything built on AI. If the data is flawed, the decision-making will be too. When we started MontBlancAI, we thought manufacturers had solid data sources. But we learned that while machines collect data, the systems for cleaning and storing that data often fall short.

So, we developed technology that focuses on effective data collection from machinery. This approach has proven useful for launching small projects.

Larger companies often have the resources for extensive data teams, but if they take too long to establish their data framework, they risk wasting time and money without immediate returns.

FE: What’s happening with ROI for data governance and AI investments?

MG: A solid data foundation is critical for both large and medium-sized food manufacturers. Initially, boards were patient with long-term investments in data governance. Now, however, they expect quicker results from their data investments.

Smaller companies are learning from this trend and focusing on cleaning and organizing their data faster. They’re realizing that understanding what data is needed upfront can lead to quicker and more effective use cases.

Ultimately, food processors want insights into their processes and machinery. Machine learning can help identify what “good” looks like for their operations, allowing them to detect potential issues before they escalate. Embracing AI means staying ahead of challenges and maximizing efficiency in production.

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artificial intelligence (AI),data management,data,AI/ML,Industry 4.0