The agricultural processing sector stands on the brink of a significant technological transformation. Norwegian startup Digel has launched a pilot project with cooperative processor Hoff SA, implementing a generative AI platform directly within its potato processing operations in Gjøvik. This move addresses a critical industry challenge: while AI has proliferated in business applications, its adoption in physical manufacturing, particularly in agriculture, has lagged. A 2024 report by the International Society of Automation noted that only 12% of food and beverage manufacturers have implemented AI at a production level, citing integration complexity and data accessibility as major barriers. Digel’s platform aims to bridge this gap by creating a digital twin of the production line, connecting real-time sensor data, equipment logs, and technical documentation into a unified, AI-readable system. This approach is particularly relevant for potato processing, where raw material quality varies significantly by season, cultivar, and growing conditions, directly impacting processing efficiency and yield.
The potential operational benefits for potato processors are substantial. The system allows operators to ask questions in plain language—such as “Why is production slower now?” or “Why are there so many faults in the compressor?”—and receive AI-generated answers based on the plant’s actual data. This capability could dramatically reduce downtime and improve decision-making. For context, studies from the European Association of Potato Processors indicate that unplanned downtime and suboptimal processing can reduce overall plant efficiency by 15-25%, with raw material variability being a leading contributor. By providing immediate, data-driven insights, the AI platform can help optimize parameters for different potato batches, potentially increasing yield and reducing waste. In an industry where marginal gains translate to significant financial impact—especially for cooperatives like Hoff SA that are owned by potato growers themselves—the ability to capture and operationalize institutional knowledge could enhance both profitability and sustainability across the entire value chain.
The pilot at Hoff SA represents more than a technological test; it signals a fundamental shift in how agricultural processing operations can be managed and optimized. By making complex industrial data accessible and actionable through natural language, generative AI has the potential to democratize operational intelligence, empower frontline workers, and create more resilient, efficient processing systems. For farmers, agronomists, and processors, the success of this initiative could pave the way for broader adoption of AI technologies that directly address the unique challenges of handling biological raw materials. As the agricultural sector faces increasing pressure to improve efficiency and reduce waste, tools that enhance real-time decision-making and preserve institutional knowledge will become increasingly vital for maintaining competitiveness.
