Driscoll’s adopts AI to increase efficiency and reduce food waste

Commercial AI is outcome focused. It requires models to be built with a business objective in mind. Sitting across an organization’s entire dataset, commercial AI is uniquely placed to optimize across the value chain. It is helping some of the world’s leading brands, including PepsiCo, Nike and Molson Coors to reduce inventory, reduce stock movements and maximize customer service.

The route to commercial AI

There are three requirements every business needs to be successful with commercial AI:

1. An AI-ready data set
2. An intelligence customized to the business built on this data
3. An interface in which line-of-business users can engage with a model

This third step is crucial. Even the most accurate and complex predictive models cannot drive value alone, they need iteration based on feedback from the commercial teams using them – a step most AI strategies are missing.

Commercial AI allows for the gradual development of a connected intelligence. This means businesses can focus on building an application to meet one objective, and later add in additional solutions that serve other teams or departments.

Driscoll’s have many core business systems that hold valuable data, they are working with the Peak team to leverage this data to enhance decision-making across multiple business functions. The initial focus of the partnership will be optimizing business critical decisions on customer stock allocation. This will help the Driscoll’s team to maximize customer service, minimize waste, reduce food miles and cut out hours of manual data manipulation.

How is commercial AI being leveraged in agriculture?

While this is the first application of commercial AI to the fresh produce sector, the technology is already being leveraged successfully within agriculture. Machinery manufacturer Fotenix uses machine learning enabled cameras to monitor crop health, utilizing Peak’s AI platform to assess images of plants at scale.

The Fotenix system analyzes numerous data sources, among them historical images of diseased crops, to determine the needs of individual plants and anticipate crop issues in real time, providing actionable decisions for farmers to take. The company utilizes AI across a range of use cases.

For one soft fruit producer in the UK, its solution resulted in a 20% reduction in cost per hectare and a 10% total increase in crop yields. If deployed at scale, these solutions could help to address food insecurity by enabling farmers to grow more food more efficiently; using fewer resources to feed more.

Peak’s solution for Driscoll’s is powered by AWS