Key takeaways:
AI adoption in food manufacturing is growing, but concentrated. Quality inspection and documentation automation are the most mature applications, but traceability integration and agentic AI are still early in deployment for most operations.
According to a joint whitepaper from the World Economic Forum (WEF) and Boston Consulting Group (BCG), early AI adopters in manufacturing have achieved up to 14% cost savings, with agentic AI systems expected to extend that impact further as they move from pilot to scale.
Manufacturers seeing the most consistent returns from AI investments prioritize data readiness before adoption, rather than treat it as an afterthought.
AI adoption may be growing, but use in CPG manufacturing is still lacking overall. According to McKinsey’s “The State of AI” report, 68% of consumer goods and retail businesses globally now use gen AI in at least one business function. However, much of that use is in marketing and sales (46%) and product development (21%). Only 8% use it in manufacturing functions.
That leaves a lot of opportunity for food manufacturers to use AI as a competitive advantage. But not so fast. There are a few questions to answer first:
What AI applications have the greatest ROI potential?
What pain points can the technology address?
And, most importantly, are we truly ready for it?
Let’s take a look at where AI stands in the industry, what opportunities are worth exploring, which are still experimental, and how to get started.
The best ROI is in quality, documentation, and monitoring
Adoption isn’t uniform across the industry, and it isn’t useful across every application equally. In a few areas, though, the technology is consistently delivering:
Quality inspection is the most mature category. Computer vision systems that scan production lines for contamination, defects, and foreign objects are operating in large-scale facilities with measurable improvements in detection consistency over manual checks. The obvious advantage is accuracy, but these systems also run continuously and generate an automatic audit record. Mars has been running AI-powered vision systems in its confectionery plants specifically for real-time defect detection, alongside predictive quality models designed to catch deviations before they reach the end of the line.
Documentation automation is the second area with substantial traction. Quality assurance (QA) teams that once spent days or weeks building and maintaining Hazard Analysis and Critical Control Points (HACCP) plans are doing that work in hours on the right platform. These systems ingest existing standard operating procedures (SOPs), process flowcharts, and product specifications to generate structured HACCP frameworks. The time savings are significant, and the consistency improvements matter when audits arrive.
Real-time monitoring and supply chain execution round out the top tier. Internet of Things (IoT) sensors connected to AI platforms now track critical control points (like temperature, pH, and humidity) around the clock, alerting teams when readings drift outside acceptable ranges. At the supply chain level, manufacturers are pushing further. In February 2026, General Mills CEO Jeffrey Harmening described how the company built a connected data foundation, deployed autonomous planning, and implemented AI-enabled execution in logistics and manufacturing, work the company’s CFO said has generated meaningful savings and is contributing to further cost reductions in the current fiscal year.
Traceability and predictive analytics are promising but not ready for most operations
There are two primary areas where the gap between what AI can do and what most facilities have implemented remains wide:
Traceability integration gets significant attention, and for good reason. Rapid recall response and end-to-end supply chain visibility address real operational and reputational risks. Most facilities are testing or piloting, but not yet operating at scale yet. But with FSMA 204 compliance coming up, this application may become more of a priority.
Predictive analytics is the other area where the promise outpaces current reality. Using machine learning to identify risk patterns before a deviation escalates is invaluable. But it also requires clean, well-structured historical data, which many facilities don’t yet have.
When evaluating vendors, platforms that lead with traceability or predictive analytics capabilities may be technically capable. Whether your operation is ready to use them well is a different question.
Putting the work into ensuring your organization is ready for AI and identifying what workflows you want to transform is critical to getting results. McKinsey’s report found that 80% of companies across industries say their organizations aren’t yet seeing tangible enterprise-level earnings impact from gen AI use, and only 21% have fundamentally redesigned any workflows around it.
The next wave of AI is already on the factory floor, but just barely
For the past two years, generative AI has been the primary focus of AI adoption in manufacturing. That work is ongoing. But a different category is entering the conversation.
Agentic AI refers to software systems that can perceive their environment, reason about what to do, and take action with minimal human intervention. They’re distinct from generative tools, which respond to prompts. Agents observe conditions, make decisions, and execute tasks.
A joint whitepaper from the World Economic Forum (WEF) and Boston Consulting Group (BCG) notes, “AI agents amplify the manufacturing vision of real-time decision-making, near-autonomous systems and seamless human-machine collaboration. While manufacturing productivity has stagnated over the past decade… this transformative vision presents a significant opportunity to reignite productivity growth.”
Adoption is still early though. According to a Capgemini Research Institute report, only 14% of organizations across industries have implemented AI agents at partial or full scale, with another 23% running pilots. Most are still evaluating.
Here are a few early factory-floor use cases:
A troubleshooting agent that interprets a worker’s question, searches relevant knowledge bases, drafts an issue report, and waits for human review before submitting
A monitoring agent that notices recurring patterns in safety checklist data and flags them to a line manager before an issue escalates
A shift readiness agent that identifies when staffing gaps and certification shortfalls create risk for production targets before a shift begins
In these examples, the AI acts, but a human approves before anything consequential happens. The “human-in-the-loop” model isn’t a limitation of current technology; it’s by design.
Pilot, data, people: the pattern behind successful AI implementations
So what steps can companies take to ensure consistent returns from AI investment?
Start with a defined problem: Quality inspection and documentation automation work because they address well-defined, high-volume problems with clear success criteria. Clearly define the issue before even starting the solution search so you know exactly what results you want AI to deliver.
Pilot before scaling: Hovis, a UK bakery brand, started with a targeted AI engagement at three sites in 2024, gap analyses, criticality assessments, and maintenance strategy work that improved production reliability. After evaluating those results, the company expanded the program ninefold in October 2025, rolling AI-driven sensor systems and predictive maintenance across multiple facilities. “We’re moving from reactive firefighting to foresight,” said Chris Lawton, Hovis’s head of engineering. “A single stoppage on one line can ripple across our entire network.” The rollout is projected to deliver full ROI within the first year.
Clean up your data: AI performance relies heavily on data quality. Facilities that cleaned and structured existing records before onboarding a platform report faster, smoother implementations. General Mills’ Chief Digital & Technology Officer Jaime Montemayor has described clean data as the precondition for everything else the company has been able to accomplish with AI.
Treat change management as part of the project: Worker resistance to AI-driven monitoring is an implementation risk, particularly when technology is introduced without adequate context. A successful rollout is only possible when frontline employees understand what the system does and why.
As with any technology project, organizational readiness is what makes or breaks adoption. Take the time to determine which AI applications will address your unique needs and pain points, take steps to avoid potential implementation pitfalls, and make data quality a high priority. Then you’ll be well equipped to see measurable results.
