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Food and consumer packaged goods (CPG) companies have reported using AI for demand planning, customer service, internal IT, marketing, tailored foodservice recommendations, product formulation and product innovation. The examples below cover ten applications across eight case descriptions—not ten separately named companies. The reported results come mainly from company, technology-provider and consultancy accounts, so they are useful evidence of how the systems were applied, not independent proof that AI alone caused the outcomes.
What are real examples of AI in food and CPG?
The cases differ in their tasks, inputs, deployment scope and measures of success. The table keeps those distinctions visible; “not stated” means the cited case description does not specify the detail.
| Company or case | Application and reported workflow | Inputs or integration described | Reported result or evidence limit |
|---|---|---|---|
Unilever and Walmart Mexico |
AI-powered collaborative planning, forecasting and replenishment. Unilever says the pilot began in 2022, first in nutrition and then across its in-store product range. | Point-of-sale information and customer planning; further technical details are not stated by Unilever. | Unilever reported 98% point-of-sale availability during the initial pilot and said it planned a broader rollout to 30 key customers. The availability figure and rollout plan are company-reported. |
HelloFresh: customer service |
A generative AI chatbot that supports customer self-service. | Customer-service workflow; the case page does not state the model, data sources or deployment dates. | AWS reports that self-service increased by over 60% globally. The case page does not provide independent validation of the measurement. |
HelloFresh: recipe cards |
Generative AI used in an automated recipe-card creation process. | Recipe-card workflow; specific inputs and human-review steps are not stated. | AWS describes the project but does not quantify its result. |
HelloFresh: IT provisioning |
Generative AI used to automate provisioning of IT resources. | Internal IT resource provisioning; technical details are not stated. | AWS describes the capability but does not quantify its result. This is an internal IT application, not a food-production use. |
Kraft Heinz: TasteMaker |
A generative AI platform for marketing content and product concepting. | Google Cloud says TasteMaker uses Kraft Heinz brand and product information. | Google Cloud reports concept production time falling from eight weeks to eight hours and 70% adoption among product-development and marketing users on the platform. These are Google Cloud’s reported results, not independent measurements. |
Unilever Food Solutions |
AI-based personalized recommendations for foodservice operators. | Unilever says recommendations draw on proprietary company resources and operator-specific details, including menus and reviews. | The case description does not quantify an outcome. |
Unilever: formulation and product development |
AI used to analyze consumer and product data, optimize ingredients, develop recipes and simulate product characteristics before physical production trials. | Consumer and product data; simulations are used ahead of physical trials. Unilever cites the Hellmann’s Easy-Out squeeze packaging design as an example of AI-assisted simulation. | Unilever says simulation saved physical testing time in that example but does not quantify the time saved. |
Unnamed multinational food manufacturer: inventory |
Generative AI used to predict inventory risks, including product damage, and suggest responses. | Inventory-risk workflow; the company and more detailed data inputs are not identified in Accenture’s case description. | Accenture reports millions in annual savings. The customer is unnamed, and the account is not an independent audit. |
Unnamed multinational food manufacturer: worker communication |
An AI-based platform helps supervisors and workers communicate across language barriers, with the stated aim of reducing errors. | Workplace communication across languages; further system details are not stated. | Accenture describes the intended error-reduction benefit but does not quantify it. This is another application in the same unnamed manufacturer case, not a separately identified company. |
Unnamed regional CPG company: plant-based milk |
An AI-assisted platform guided formulation by combining ingredient, analytical and sensory data. | AKA Foods says the formulation process included 14 trained tasters. | The customer is unnamed. The case is vendor-reported and does not provide a quantified commercial or product-performance outcome. |
HelloFresh accounts for three of the applications, and Accenture’s unnamed multinational customer accounts for two. The AKA Foods customer is also unnamed. The named organizations across these cases include Unilever, Walmart Mexico, HelloFresh and Kraft Heinz; the case descriptions therefore do not establish ten separately named company deployments.
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Yes, the reported applications include both early-stage concept development and formulation work, but those are different parts of product innovation. Kraft Heinz’s TasteMaker supports marketing content and product concepting. Unilever describes using AI to analyze product and consumer data, optimize ingredients and develop recipes, then simulate product characteristics before physical trials. The plant-based milk case describes using ingredient, analytical and sensory data to guide formulation, with trained tasters involved.
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A further named-company example comes from Board of Innovation: it reports that a one-week AI-supported sprint with Tata Consumer Products produced 42 concept cards and low-fidelity prototypes in four days, with eight concepts selected for further consumer testing. Those figures describe concepts and prototypes, not market-ready products, and the case does not establish that AI alone produced them.
The Kraft Heinz case also illustrates why a tailored system may depend on company-specific context. Justin Thomas, Head of Digital Experience and Growth at The Kraft Heinz Company, said: “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.”
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How should these deployments be compared?
A useful comparison starts with the job the system performs, rather than treating every example as the same kind of “AI.” The cases span forecasting, generative content, recommendations, risk prediction, simulation and formulation support. The source descriptions do not consistently identify model types or technical designs, so labels should not be inferred where the case does not provide them.
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- Task and workflow: Identify where AI enters the process—such as planning, customer support, concepting or formulation—and what work still happens outside the system.
- Data: Check which inputs are described. The cases mention point-of-sale information, brand and product information, operator menus and reviews, or ingredient, analytical and sensory data; several provide no detailed input account.
- Human involvement: Look for operator-specific context, trained tasters, physical production trials or consumer testing. These indicate where people or physical validation remain part of the workflow, rather than being replaced by a generated output.
- Scope: Distinguish a pilot, a described capability and a stated rollout plan. A plan to expand is not evidence that expansion has already occurred.
- Outcome measure: Separate availability, self-service, concept-production time, platform adoption, savings and qualitative aims. The measures are not standardized across cases, so they do not support a head-to-head ranking.
How common was AI use among CPG leaders?
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. These are survey findings from 2024, not a 2026 adoption estimate. At publication, McKinsey also said no CPG player had truly scaled traditional and generative AI capabilities. The survey offers historical industry context; it does not measure the deployments in the case table.
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