ANALYSE SECTORIELLE

Automation and AI in Agri-Food: Improving Planning Without Losing Control

Automation and AI are generating growing interest across the agri-food industry — particularly for improving demand forecasting, optimizing inventory levels, reducing waste, and automating repetitive tasks such as order tracking and report generation.

9 min readJune 1, 2026Agri-Food

At a glance

Automation and artificial intelligence are generating growing interest across the food and agri-food industry - particularly for improving demand forecasting, optimizing inventory levels, reducing waste, and automating repetitive tasks such as order tracking and report generation.
These technologies represent real opportunities.
But their value depends directly on what they are built on: reliable data, clearly defined processes, and established accountabilities.
Without these foundations, automation and AI risk amplifying existing limitations rather than resolving them.
Many food and agri-food companies approach automation and artificial intelligence by focusing first on the technologies themselves: which tool could forecast demand, which system could optimize supplier orders, or which solution could automate certain reports.

Read the analysis

Automation and AI are generating growing interest across the agri-food industry - particularly for improving demand forecasting, optimizing inventory levels, reducing waste, and automating repetitive tasks such as order tracking and report generation.

47%

of Quebec SMBs have started at least one concrete AI use case

62%

of leaders believe their data is insufficiently structured for AI

31%

have formal governance around generative AI tools

AI maturity in the enterprise - 2025 → 2026 evolution

Identified and prioritized AI use cases

58%

42%

AI-ready data

Key dimensions

The structural issues covered in this analysis, grouped by theme.

Automation and artificial intelligence are generating growing interest across the food and agri-food industry - particularly for improving demand forecasting, optimizing inventory levels, reducing waste, and automating repetitive tasks such as order tracking and report generation.

These technologies represent real opportunities. But their value depends directly on what they are built on: reliable data, clearly defined processes, and established accountabilities. Without these foundations, automation and AI risk amplifying existing limitations rather than resolving them.