Food manufacturers are feeling the pressure to adopt artificial intelligence, but a new survey shows most are moving too cautiously, and only a fraction have scaled the technology across their plants.
Survey shows fear of hesitation outweighs fear of excess
A February 2026 poll of 1,200 manufacturing leaders found that 60% now worry more about being too hesitant with AI than about being too aggressive. Adoption rates have risen from 53% to 72% in the past two years. Yet just 10% of manufacturers have taken AI beyond pilot projects to full‑scale deployment.
The same data reveal a paradox: while many executives greenlight AI projects to appear decisive, the underlying operations often aren’t ready. Capital ends up tied to pilots that stall, leaving boards to question the value of the spending.
High‑performers take a different path
McKinsey identifies the top 6% of manufacturers as “high performers,” tying at least 5% of profit to AI. These firms share three common practices. First, they redesign workflows rather than simply attaching AI tools to existing processes. Second, senior leaders are held directly accountable for AI outcomes. Third, they allocate more than 20% of their digital budget to AI and scale it in the majority of their operations.
The high performers’ approach is deliberate, not frantic. They focus on a clear problem, fix the data foundation, and then expand. Speed for its own sake does not appear on their checklist.
In the food and beverage sector, only 41% of companies have a formal AI plan, but employees are already using public AI tools. For example, a worker in a regulated plant has been pasting supplier data into a chatbot to speed up a certificate of analysis review, bypassing any audit trail or approval process.
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That kind of ad‑hoc use highlights a broader issue: nearly 40% of F&B teams cite disconnected systems and fragmented data as the biggest barrier to AI adoption. Additionally, 70% of manufacturers still run a mix of legacy and modern equipment, and only 37% have a unified data strategy.
Quality control and supply chain management are the leading AI use cases, with 50% of manufacturers citing quality and 45% naming supply chain as top applications. Defect detection, a fast‑growing quality function, now sees AI use in 47% of plants, up from 33% a year earlier.
These AI applications demand clean, connected data—from sensor readings to batch records. Feeding a contamination‑detection model with fragmented inputs can produce confident but wrong answers, raising the risk of a recall.
When comparing this situation to previous technology rollouts, the pattern is familiar: firms that rush new tools without fixing data foundations often see limited returns. The same lesson applies here—building a solid base before scaling tends to produce the most sustainable gains.
Adoption remains uneven.
That approach may feel slower than reacting to the fear of falling behind, but it mirrors the only pattern that has placed roughly one in ten competitors ahead of the rest.
