Food and consumer packaged goods (CPG) companies are using AI for distinct jobs: forecasting store demand, answering customer questions, creating marketing concepts, tailoring foodservice advice and supporting product formulation. The examples below are reported deployments and projects, not independent proof that AI alone caused the stated outcomes.
How are food companies using AI in planning and operations?
1. Unilever and Walmart Mexico: forecasting and replenishment
Unilever says it began piloting an AI-powered customer connectivity model with Walmart Mexico in 2022. The work started with nutrition products and then expanded across Unilever’s in-store product range. Unilever reports 98% point-of-sale availability during the initial pilot and said in 2024 that it planned to extend the approach to 30 key customers. Those figures and the rollout plan are Unilever’s account, not independently audited findings.
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The application is collaborative planning, forecasting and replenishment: using retail and supplier information to help keep products available. The published account does not specify a common comparison period or an independently verified measure of AI’s contribution.
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2. An unnamed multinational: inventory-risk support
Accenture describes a generative AI system for an unnamed multinational food manufacturer that predicts inventory risks, including product damage, and suggests responses. Accenture reports millions in annual savings, but does not identify the customer or provide an independent audit of that figure. The case therefore illustrates a risk-management workflow, not a verifiable company-wide savings benchmark.
#1 Best Overall
3. An unnamed multinational: communication across languages
The same Accenture case describes an AI-based platform intended to help supervisors and workers communicate across language barriers and reduce errors. The account does not identify the manufacturer or quantify the error reduction. This is a workforce-communication application, separate from the inventory-risk system, even though both appear in one case description.
How are CPG companies using generative AI with customers and employees?
4. HelloFresh: customer-service chatbot
AWS says HelloFresh’s generative AI chatbot increased self-service by over 60% globally. That is the result reported on AWS’s case page; the page does not independently validate the measurement or date the deployment.
Self-service is the relevant outcome here: the chatbot is intended to handle customer needs without requiring a human agent for every interaction. The reported percentage should not be read as an equivalent reduction in total support workload or costs.
Rank #2
5. HelloFresh: recipe-card creation
AWS also identifies automated recipe-card creation as a HelloFresh generative AI project. The case page does not quantify the impact of this capability. It is a content-production workflow, distinct from the customer-service chatbot.
6. HelloFresh: IT resource provisioning
A third HelloFresh project described by AWS uses generative AI to automate provisioning of IT resources. This is an internal technology-operations use, not a food-production or recipe-development application. AWS does not provide a result measure for it in the case description.
7. Unilever Food Solutions: personalized foodservice recommendations
Unilever says its professional foodservice business uses AI to give operators tailored recommendations drawing on proprietary company resources and operator-specific details such as menus and reviews. The model of use is contextual advice for foodservice operators, rather than a general-purpose consumer chatbot. Unilever’s description does not state a quantified business outcome.
Can AI help develop new food products and marketing ideas?
8. Kraft Heinz: TasteMaker for marketing and product concepts
Google Cloud says Kraft Heinz built TasteMaker, a generative AI platform that uses company brand and product information to support marketing content and product concepting. Google Cloud reports that concept production time fell from eight weeks to eight hours and that 70% of product development and marketing users adopted the platform. These are Google Cloud’s reported results, not independent measurements.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The design choice matters: rather than treating AI as an unconstrained idea generator, TasteMaker is described as working with brand and product information and addressing specific workflows. Justin Thomas, Kraft Heinz’s Head of Digital Experience and Growth, explained: “There were two critical considerations when we built TasteMaker AI. The first was to bring our brand intelligence into the platform. The second was to build capabilities and workflows that would address very specific problems.”
9. Unilever: formulation and product development
Unilever describes using AI to analyze consumer and product data, optimize ingredients, develop recipes and simulate product characteristics before physical production trials. Its Hellmann’s Easy-Out squeeze packaging design is an example of AI-assisted simulation that the company says saved physical testing time. This is a product-development workflow: simulation can help teams explore or screen designs, while the account does not establish that virtual analysis replaces all physical testing.
10. An unnamed regional CPG company: plant-based milk formulation
AKA Foods says an unnamed customer used its AI-assisted development platform to combine ingredient, analytical and sensory data to guide plant-based milk formulation. Its vendor-reported account includes 14 trained tasters. The customer is not identified, so the case cannot be independently tied to a named manufacturer or compared with a named brand’s product launch.
What adjacent AI innovation example is worth knowing?
Tata Consumer Products: a rapid concept sprint
Board of Innovation reports that an AI-supported, one-week sprint with Tata Consumer Products produced 42 concept cards and low-fidelity prototypes in four days, with eight concepts selected for further consumer testing. This is a named-company innovation example, but it is a concepting sprint rather than evidence of market-ready products or a scaled operational deployment. The selection for further testing is an important distinction: concepts still need consumer validation.
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McKinsey & Company’s 2024 survey of 63 CPG leaders found that 71 percent reported AI adoption in at least one business function and 56 percent reported regular generative AI use. Those figures describe respondents to a 2024 survey, not all CPG companies or adoption in 2026. At the time, McKinsey also said no CPG player had truly scaled traditional and generative AI capabilities.
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Together, the cases show why “AI in food” is not one use case. Forecasting and inventory-risk prediction address operations; chatbots and tailored recommendations serve customers or operators; recipe-card creation and marketing content generate material; formulation and simulation support product development. The systems may use different data and require different safeguards, so their outcomes cannot be compared as if they were one technology or one standardized performance measure.
How should businesses assess an AI deployment?
These published examples do not use a common measurement framework, and most accounts come from the company, cloud provider, vendor or consultancy involved. A useful evaluation should therefore start with the workflow and evidence, not the headline percentage.
- Define the task: distinguish demand planning, customer service, internal IT, content creation, formulation and worker communication.
- Identify the inputs: establish which retail, product, brand, ingredient, sensory or operator information the system needs, and whether it is available and suitable.
- Map workflow integration: determine where AI suggestions enter existing systems and who acts on them.
- Set human review: specify when employees, operators, product developers or trained tasters validate outputs—especially for food formulation, safety-sensitive operations and consumer-facing claims.
- Define scope: record the geography, products, users and time period covered; a pilot result is not automatically a result at full scale.
- Choose an outcome measure: separate availability, self-service, production time, adoption, error rates, savings and consumer test results rather than treating them as interchangeable.
- Check attribution: compare results against a baseline or suitable control and account for other process changes before crediting AI with an outcome.
The cases establish that organizations have reported applying AI across these functions. They do not amount to a complete census of CPG deployments, and vendor or company performance claims should remain attributed unless independently evaluated.
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