The global economy is fueled by a “take, make, and dispose” model that relies on extracting and consuming large quantities of finite materials and fossil fuels. This linear economic model has delivered unprecedented prosperity over the past 200 years—but it has also damaged our environment. For example, every $1 spent on food production means $2 in economic, social, and environmental costs. Given the pressure on already severely depleted soils to provide food for an ever-growing global population and the fact that roughly a third of food is never eaten, innovation in our industrial agricultural system is a must.
Artificial intelligence (AI), the group of technologies that perform human-like cognitive functions such as reasoning and learning, has the potential to help reshape the world’s food system. A recent report by the Ellen MacArthur Foundation and Google, with research and analytical support from McKinsey, found that AI can create—rather than extract—value and even protect and regenerate biological systems. The report found three areas where AI can have the biggest impact on the transition to a circular food system: sourcing food grown regeneratively and locally where appropriate, designing out avoidable food waste, and designing and marketing healthier food products.
AI is one of the great technological developments of our time, and using its power to transition the food economy from a linear to a circular model is largely an untapped opportunity.
Designing artificial intelligence into the food system
Currently, most crops are grown in a way that withdraws more from natural systems than it returns, leaving waterways polluted and soils and agrobiodiversity depleted. With the help of AI, conventional agriculture practices such as mono-cropping, blanket application of synthetic chemical fertilizers, and intensive land use can be replaced with more regenerative agriculture practices.
Data from drones, remote sensors, satellites, and smart farm equipment provides farmers with valuable real-time information on soil, crop health, and weather conditions. This intelligence helps farmers make smarter decisions on where to grow crops, how to optimize crop rotations, and when to sow, compost, and harvest those crops.
For example, some ag-tech solutions analyze images to determine when fruit is ready to be picked. Others include algorithms that identify microbes that promote crop growth without synthetic fertilizers. Data-driven software and AI solutions can help farmers manage their work more effectively by providing outcomes for regenerative agricultural practices without expensive and time-consuming field trials.
AI can also help farmers at the outset by designing out avoidable food waste and preventing edible food from being thrown away. Farm-based food supply chains can become more efficient using visual imagery technology during food inspections. AI-enabled tracking can help retailers sell food before it goes bad, and AI algorithms can forecast and predict sales to allow restaurants and retailers to more effectively connect supply to demand when ordering food, thereby reducing avoidable food waste. By using these techniques to design out food waste, we found that AI can generate an estimated economic opportunity of up to $127 billion a year in 2030, calculated as growth in top-line revenue.
Even convenient and processed foods can be designed in a more circular way. AI tools can help source regeneratively grown ingredients, reducing processing waste and unsafe additives. Companies are already using AI algorithms to create an egg-free alternative to mayonnaise and plant-based foods to replace meat, fish, dairy and egg-based products, which rely more heavily on natural resources.
Growing the field
The McKinsey Global Institute predicted AI could add an extra $13 trillion to global economic activity by 2030, yet there are issues that may constrain its application for social good. The transition to a circular economy requires value chains and an entire network of trusted partners—it cannot be done by one company alone. Collaboration between stakeholders across the ecosystem, including companies, governments and NGOs, is paramount when it comes to data generation, collection, and sharing.
Similar to the human brain, AI processes data and learns from it to make better decisions over time. AI production requires a clear understanding of the actual problem it’s meant to solve. Only then can it successfully complete these four steps: data collection, data engineering, algorithm development, and algorithm refinement. Global corporate investment in AI reached $39 billion in 2016, according to some estimates, and further, consciously value-driven investments are needed to advance and disseminate this technology in the service of a circular food economy.