97 AI use cases • Executive briefs • Technical analysis
Think of a 100‑year‑old consumer brand hiring a super‑smart digital assistant that sits inside Microsoft 365 and their business apps. This assistant reads documents, emails, and data, then suggests decisions, drafts content, and automates many routine tasks—without the company having to rebuild all its old systems from scratch.
Think of PepsiCo running its factories and delivery trucks like a gigantic kitchen and delivery fleet. AI is the smart planner that constantly looks at sales, inventory, weather, promotions, and production status to decide what to make, where to ship it, and how to keep machines running smoothly before they break.
This is like giving your demand planners a very smart co-pilot that can read all your plans, history, and assumptions, then challenge, refine, and stress-test your demand forecast before it’s locked in.
This is like giving every shopper their own digital sales associate who remembers what they like, what they looked at before, and what similar customers bought, then uses all that data to tailor offers, messages, and experiences in real time across stores, apps, and websites.
Imagine a smart assistant that reads millions of toy reviews, call-center notes, and survey comments in minutes, then tells Mattel product teams in plain English what kids and parents love, hate, or are confused by — as those opinions are coming in — so they can quickly tweak designs, instructions, or packaging.
This is like giving your global supply chain a smart GPS and co‑pilot: it constantly looks at all the data (demand, inventory, shipping, risks), simulates options, and recommends the best decisions instead of people doing it all in spreadsheets and emails.
This is like having an AI-powered design buddy in a sneaker store: you tell it the vibe, colors, and style you want, and it helps you co-create a unique pair of shoes tailored to your taste.
This is like giving your marketing team a crystal ball that looks at all the clicks, calls, and purchases your customers made in the past and then guesses what they’re likely to do next, so you can talk to the right people with the right offer at the right time.
Think of these tools as emotion thermometers for text and speech: they read what customers write or say (emails, reviews, social posts, support calls) and tell you whether people feel happy, angry, confused, or about to leave for a competitor.
This is like giving your company a super-listening ear that reads all customer comments, reviews, and survey answers and tells you, in plain language, how people feel and why they’re happy or upset.
Think of a smart assistant that can instantly test thousands of packaging ideas on a computer—how strong they are, how much material they use, and how they look—so your engineers only build and test the few best options in the real world.
This is like giving L’Oréal’s marketing team a tireless digital copywriter and designer that runs on Google Cloud. Marketers describe the campaign or product, and the AI helps generate on‑brand text, images, and variations for ads, social posts, and product pages in seconds instead of days.
This is like an AI-powered comment filter for movies: users type or paste a movie review into a simple website, and the system automatically decides whether the review is positive or negative, using a modern language model under the hood.
Think of this as a smart store clerk who quietly watches what each shopper likes, remembers their habits, and then rearranges the shelves and offers just for that person in real time—across websites, apps, emails, and ads.
This is like giving your customer service team a tool that reads every customer message, figures out whether the person is happy, angry, or confused, and then summarizes the main issues so you know what to fix first.
Think of this as a specialist AI toolkit for retailers and consumer packaged goods brands that helps them better understand shoppers, predict demand, and personalize experiences across stores and ecommerce—like having a data-driven co-pilot for merchandising, marketing, and operations.
Think of this as a smart thermometer for customer feelings. It reads reviews, tweets, and comments at scale and tells you whether people are happy, angry, or worried about your products and brand.
This is like giving Mango its own smart ‘shop assistant in the cloud’ that can chat with customers and employees, answer questions, and help with tasks across web, app, and possibly in-store channels.
This is a strategy and analytics approach that helps large consumer packaged goods (CPG) companies use their data and AI as a new kind of ‘economies of scale’—not just buying more shelf space or running bigger TV campaigns, but spotting profit opportunities and efficiency gains across brands, markets, and channels using advanced analytics and generative AI.
Think of this as a control tower for a food & beverage factory: it gathers data from sales, inventory, production, and suppliers, then uses analytics to suggest the best plan so you make the right product, at the right time, with the least waste.
This is like having an always-on digital analyst that reads every customer review, support ticket, social media post, and survey response, then tells you in plain language whether people are happy or unhappy and why.
Think of this as putting a very smart autopilot into your warehouse and shipping network. It watches orders, inventory, and shipping in real time and then continuously suggests or executes the best way to stock, pick, pack, and deliver products to customers with fewer mistakes and lower costs.
Imagine all your sales, inventory, and retail partner reports arriving as messy, different-shaped puzzle pieces every day. This application is like a smart assistant that collects those pieces from every retailer, cleans them up, snaps them into one big picture, and then circles in red where you’re about to run out of stock, where you’re losing shelf space, or where promotions are working best.
Think of your supply chain planning as flying a modern plane: the AI is the autopilot doing millions of calculations per second, and your planners are the pilots deciding the destination, watching for storms, and overriding when needed. This setup makes planning faster, safer, and more precise than humans or software alone.
This is like having a knowledgeable Ralph Lauren sales associate in your phone or browser that you can chat with in plain English. You ask about outfits, styles, sizes or occasions, and it guides you to the right products and combinations, powered by AI instead of a human associate.
This is like giving a CPG company a super-analyst that never sleeps: it scans all your sales, pricing, promotions, store, and external data to automatically surface why performance changes, where growth is hiding, and what to do next.
This is like giving every retail store manager a super‑smart digital co‑pilot that constantly walks the aisles in software: it spots what’s wrong with shelves, pricing, and promotions, then automatically kicks off the work in your business systems to fix it.
This is like a virtual skin consultant on your phone: you show it your skin and answer a few questions, and it recommends the right products and routines tailored specifically to you.
This is like an autopilot for your supply chain: it constantly watches demand, inventory, and operations and then automatically decides what to buy, where to send it, and when—rather than just giving planners reports and leaving them to decide.
This is like giving Symrise’s flavor scientists a super-smart assistant that has tasted millions of recipes. It predicts which ingredient combinations will give the right flavor and work well in a product before anyone mixes them in the lab, so you get to market faster with fewer failed trials.
This is like a smart shopkeeper who remembers each regular customer and quietly adjusts offers and prices based on their habits, loyalty, and sensitivity to price—so they buy more and stay longer, while the shopkeeper still protects their overall profit across the whole supply chain.
This is like having a smart digital salesperson for every single shopper that instantly figures out what offer or promotion will convince them to buy right now—based on what they’re doing, what they’ve bought before, and what similar people responded to in the past.
Think of this as a mood detector for your customers’ messages. It automatically reads emails, chats, and tickets and tags them as happy, neutral, or upset, so your team knows where to focus and how to respond.
This is like having a smart assistant read through thousands of customer comments, group them by topic, summarize what people love or hate, and flag big issues for you—while human experts still check the most important insights before decisions are made.
This is like having an always-on “mood radar” that scans what customers say in calls, chats, emails, and reviews, then tells you who’s happy, who’s frustrated, and why—so you can fix issues faster and design better experiences.
This is like a super-smart recipe helper for cosmetics chemists: it analyzes huge amounts of ingredient and formula data to suggest greener, more sustainable product recipes that still meet performance and safety standards.
Imagine every shopper having a smart helper that knows sales, products, and your preferences, and can do the comparing, searching, and asking-customer-service-questions for you before you ever talk to a human or visit a store.
This is about using tools like ChatGPT as a very fast junior market researcher: you ask it questions about consumers, brands, or markets, and it drafts insights, survey ideas, and segment descriptions instead of a human doing everything from scratch.
Think of this as a ‘digital co-pilot’ for factories, retailers, and consumer brands that helps people decide what to make, where to put it, and how to sell it, using data from across the business. Humans still drive; AI just keeps suggesting the fastest, safest route and flags issues before they become problems.
This is about using AI as an always‑on radar and autopilot for the supply chain: it constantly scans for risks (like delays, shortages, demand spikes), predicts problems before they hit, and suggests or triggers responses so the business can keep products flowing to customers.
This is like giving your planning team a super-calculator that looks at years of sales, promotions, seasons, and outside events to tell you how much of each product customers will want next week, next month, and next quarter—far more accurately than human spreadsheets.
Imagine your retail planning team with a super-analyst who has read every sales report, every inventory file, and every marketing plan you’ve ever had, and can instantly tell you what to buy, how much, where to send it, and when to mark it down. That’s what AI-powered retail planning tools like Toolio aim to do across the full planning calendar.
This is like giving your supply chain team a super-smart GPS that constantly looks at sales, inventory, and outside signals (like promotions or disruptions) and then tells you what to produce, where to ship it, and when—so shelves stay full without wasting money on excess stock.
This is about using smart chatbots as digital shopping assistants that can answer questions, suggest products, and guide people through purchases—like a knowledgeable store clerk living inside a website or app.
This is like having an AI-powered focus group constantly reading all your customer reviews, chats, and social comments, then summarizing how people feel about your products and why—at the level of each brand, store, and category.
This is like a GPS for your consumer-goods supply chain: it constantly looks at demand, production, inventory, and transport data and then tells you the cheapest, fastest way to move products from factories to shelves—while updating the plan whenever reality changes.
This is about choosing between an off‑the‑shelf "forecasting calculator" and a made‑to‑measure "forecasting tailor" for predicting customer demand. Generic tools give you average predictions built for many companies; a custom AI model is trained specifically on your own sales, marketing, inventory, and seasonal data to better guess how much you’ll sell and when.
This is like giving your online store a smart assistant that can read all your product reviews, understand if customers are happy or unhappy, and summarize the mood for you automatically.
This is like teaching a computer to read customer reviews or social media posts and automatically decide whether people sound happy, unhappy, or neutral about a product or service.
Imagine your whole supply chain – factories, warehouses, trucks, ports, and retail stores – all sharing a single, constantly-updated AI ‘brain’ that can see disruptions early, reroute goods automatically, and negotiate trade‑offs between cost, speed, and service across every partner in the network.
This is like giving your marketing team special glasses that color each customer by how they feel about your brand—happy, neutral, or unhappy—and then grouping similar colors together so you can treat each group differently.
This is like giving your company a smart ear that listens to what customers say in reviews, social media posts, and surveys, then automatically labels each comment as happy, unhappy, or neutral and summarizes the main themes so you know what to fix or double down on.
This is like having a very smart assistant read through millions of customer reviews on an app store or marketplace and then automatically build the same satisfaction metrics your research team would create—things like “service quality”, “ease of use”, or “value for money”—without hand-coding survey questions or rules.
This is like giving a small or mid-sized consumer brand the kind of crystal ball big retailers use: it looks at past sales, seasonality, and market signals to predict where and when customers will buy more so you know which products to push and which markets to expand into.
Imagine every shopper having a smart, always-on personal stylist and shopping assistant that already knows their tastes, budget, and needs, and can instantly adjust offers, recommendations, and messages for them across website, app, email, and in-store screens. That is what generative AI enables for retail personalization.
This is like giving your online store a tool that reads every customer review and instantly tells you whether people are happy, unhappy, or mixed—without a human having to read them all.
This system is like a smart mailroom clerk that reads every customer complaint as it comes in and instantly puts it into the right bucket (billing issue, product defect, service delay, etc.) using a powerful language AI instead of manual tagging.
This is like giving every customer call or message a live “mood thermometer” that tells your team whether the customer is happy, confused, or upset while the interaction is actually happening.
This is like giving a computer a big pile of hotel reviews and asking it to automatically tell you which guests were happy, which were angry, and what they talked about most—without a human needing to read every review.
This is like giving your food R&D team a super‑smart assistant that can instantly search through years of recipes, lab data, regulations, and consumer feedback, then suggest promising new product ideas and formulations in days instead of months.
Imagine you’re planning to launch a new flavor or variant of an existing product (a line extension). This system looks at how similar launches behaved in the past and predicts how your consumers’ characteristics will change—who will switch, who will trade up or down, and how segments might shift—before you actually launch.
Think of this as a smart digital shop assistant that can talk with customers, understand what they want, and instantly suggest the right products, offers, and content across apps, websites, and in-store screens.
This appears to be a set of AI and machine‑learning tools that help retailers treat in‑store shoppers and online visitors as the same person, so marketing, recommendations, and offers feel continuous across website, app, and physical stores.
This is like having a robot read thousands of customer reviews and convert all the feelings (happy, angry, neutral) into a clear product score so you instantly see if people love or hate a product.
Imagine your CPG supply chain has a smart control tower that constantly watches sales, inventory, promotions, and logistics, then quietly fine‑tunes ordering, production, and distribution so shelves stay full while warehouses stay lean. That’s what AI is doing for the CPG supply chain: it’s like adding a 24/7 super‑planner that spots patterns humans miss and prevents waste before it happens.
Imagine giving your product development team a super-fast, tireless assistant that can read every consumer review, trend report, and test result in seconds, then suggest new product ideas, formulas, and packaging options before your competitors have even finished their first meeting.
This work is like a guidebook that explains how computers learn to understand whether people’s opinions in reviews, posts, or comments are positive, negative, or neutral, and how to apply those techniques in real-world consumer settings.
Think of this as putting a super-smart autopilot on a consumer goods company’s planning and logistics. It continuously reads sales, weather, promotions, and supply data, then suggests how much to make, where to ship it, and when to adjust plans so shelves stay stocked with minimal waste.
This is like giving a store a pair of smart eyes: cameras and image-recognition software watch shelves and customer behavior, then an AI predicts what will sell next so buyers know what, when, and how much to reorder.
Think of AKA Foods as a super-smart digital food scientist that helps brands invent and improve food products faster. It sifts through huge amounts of ingredient, nutrition, and consumer trend data to suggest what to create next, how to formulate it, and how to position it in the market.
Think of this as a smart reader that goes through thousands of customer reviews and tells you not just if people are happy or angry, but why — for example, that people love the taste but hate the delivery time.
This is like having an AI assistant that reads every single customer review in your online store, understands if people are happy, angry, or confused, and then hands you a simple summary of what’s going well and what needs fixing.
This is like having a super-smart digital food scientist that can invent new recipes for plant‑based foods—mayonnaise, milk, burgers—by learning from millions of real food examples and ingredients, then proposing new formulas that taste and feel like the originals.
This is like giving every consumer packaged goods brand (snacks, cosmetics, beverages, etc.) a super-observant assistant that watches what happens from the factory to the store shelf and then tells your teams exactly where things are going wrong and what to fix first.
This is like giving your online store super-hearing: it reads all customer reviews, ratings, and comments and automatically tells you who’s happy, who’s angry, and why, so you can fix problems and double down on what people love.
This is like an automated focus group for video games that reads thousands of YouTube comments and tells you whether players are happy, angry, or disappointed about a game or trailer.
This is like giving your company a super-hearing assistant that listens to every customer review, email, chat, and survey, then tells you in plain language whether people are happy, angry, or confused – and why.
This is like giving your product reviews to a team of specialists instead of one rushed intern: first one system cleans and organizes the text, another figures out if the feeling is positive, negative, or neutral, and later stages go deeper (e.g., which features people love or hate). All stages work together to give a precise emotional score for each review.
Imagine reading thousands of Amazon food reviews and not just seeing an overall star rating, but knowing exactly what people liked or disliked about the taste, packaging, delivery, or price. This system fine‑tunes existing AI language models so they can automatically read each review and tag the sentiment for each specific aspect (e.g., “taste: positive”, “packaging: negative”).
This is like giving your online store a smart, multilingual ear that listens to everything customers say in reviews, chats, and social media, and then instantly tells you who is happy, who is angry, and why – even if they’re speaking different languages.
This research looks at what happens in shoppers’ minds when a product is designed by AI instead of a human designer—how it changes what they notice, how much they like it, whether they trust it, and if they’ll actually buy it.
This is like a smart magic mirror on your phone that lets you ‘try on’ Chanel makeup and beauty products virtually, seeing how they look on your own face before you buy anything.
Think of this as a smarter app store shelf that learns what each person actually likes and then puts the most relevant apps right in front of them instead of making them scroll through thousands of options.
This is Estée Lauder plugging into Microsoft’s AI ‘power plant’ to add smart, chatty, creative capabilities across its beauty business—things like product content, marketing copy, and internal decision support—without having to build the AI from scratch.
This is like a shared, AI-assisted control tower where consumer goods companies and retailers can see the same supply and demand picture, coordinate orders and inventory, and resolve issues together instead of trading spreadsheets and emails.
Think of AI in new product development as a digital co-pilot for your R&D and marketing teams. It scans huge amounts of customer feedback, market data, and technical information, then proposes ideas, predicts which concepts will succeed, and helps you design and test products virtually before you spend serious money in factories or on campaigns.
Think of Unilever’s Personal Care division having a very smart digital co-pilot that helps decide which products to launch, how to design them, what price to set, and what ads to run – all much faster and more precisely than humans alone could do.
This is like having a GPS and weather forecast for your consumer-packaged-goods (CPG) supply chain: it doesn’t move the trucks itself, but it tells you where traffic jams, storms, and shortcuts are so you can plan routes and inventory smarter.
This is a strategy and advisory offering that helps consumer goods and retail companies plug AI into their existing business—from demand forecasting to pricing, marketing, and store operations—so they can use data and automation at scale instead of manual guesswork.
Think of this as a playbook for how tools like ChatGPT will change everyday work in consumer goods companies—from marketing and sales to supply chain and store execution—and what new roles, skills, and guardrails are needed.
This looks like a thought-leadership or resource page about how consumer packaged goods (CPG) companies can use AI across digital commerce – more like a playbook than a specific software product.
This appears to be an AI-related chapter or paper in an academic/industry book from IGI Global (irma-international.org), likely describing how AI is used in a consumer-facing context (e.g., shopping, digital assistants, or marketing to end consumers). However, the specific function—what the AI actually does—is not visible in the provided content.
This looks like a consulting offering that helps consumer packaged goods (CPG) brands use AI across their business—things like better demand planning, pricing, promotions, and marketing—rather than a single narrow app. Think of it as a team that brings ‘AI copilots’ to different parts of a CPG company.
Think of this as a research-based playbook that explains how people react when what they see, buy, or interact with was designed by AI instead of a human. It doesn’t build an app; it tells you what to expect from your customers’ brains and emotions when you roll out AI-designed products, packaging, ads, or interfaces.
Think of AI in CPG as giving every function in the business—marketing, supply chain, sales, and R&D—a super-fast assistant that spots patterns in sales and shopper data, predicts demand, and suggests what to make, where to ship it, and how to sell it more effectively.
This is not a single app but a playbook for big consumer brands on how to use AI at scale without losing the trust of shoppers, employees, or regulators. Think of it as a roadmap that shows where AI can help (like forecasting demand, planning promotions, or personalizing offers) and how to put guardrails around it so people believe and accept the outcomes.
Think of AI in food and beverage as a super-smart assistant that helps decide what products to make, how much to produce, which ingredients to buy, what to say in marketing, and how to get items onto shelves with less waste and guesswork.