Mentioned in 119 AI use cases across 22 industries
A shopper taps a button, and Google’s AI calls nearby stores to ask whether an item is in stock, what it costs, and whether there are promotions, then sends the answers back.
The AI not only predicts a problem, it also starts the fix by ordering the part, booking the technician, notifying the driver, and adjusting the route, while a manager approves it.
This is like giving clinical trial teams a very smart assistant that can instantly read through trial documents, data tables, and reports, then summarize findings, highlight safety issues, and draft analysis text so humans don’t have to do all the slow, manual reading and writing themselves.
An AI system suggests beauty products a person is likely to want, instead of making them search through many options manually.
This is like giving every shopper their own smart personal assistant that knows the entire store, all the promotions, and the shopper’s preferences, and can guide them from “I have a need” to “order placed” through natural conversation across web, app, or even voice.
Think of this as building your own ‘Netflix-style’ recommendation brain: it watches what each user does, learns their tastes, and then uses a mix of traditional recommendation models and modern generative AI to decide what to show or suggest next.
This is like giving a football club’s scouting department a super‑assistant that has read every match report, watched all the stats, and can instantly summarize which players fit the coach’s style and why.
This is like having a smart assistant watch all your videos and automatically create a searchable index of what’s said, who appears, where logos show up, and key moments—so teams can quickly find and reuse the right clips without manually scrubbing through footage.
This is like a super‑smart search and monitoring engine for banks and financial firms that can instantly scan all their data (transactions, logs, customer activity, documents) to spot risks, fraud, and opportunities, then plug into AI tools for answers and automation.
This is like putting a smart ‘check engine’ light on every aircraft part and piece of ground equipment. Instead of waiting for something to break, Azure’s AI watches sensor data and tells you in advance when a component is likely to fail so you can fix it during planned downtime.
Think of media buying as trading ads on a stock exchange. Programmatic buying is the robot trader that automatically bids on ad space in milliseconds. AI makes that robot trader much smarter, faster, and able to decide which impressions are worth paying for, at what price, and for which audience.
Imagine your entire IT environment—servers, networks, apps, cloud services—constantly watched by a smart assistant that never sleeps. It reads all the logs, alerts, tickets, and performance data, spots early warning signs, figures out what’s really important, suggests fixes, and in many cases can trigger automated responses before users even notice a problem.
This is like having a smart digital sales associate that quietly watches how people browse, search, and compare products across apps and websites, then helps brands put the right message or product in front of the right shopper at the right time as they move from “just looking” to “I’m ready to buy.”
Instead of using AI only as a mad scientist to invent new pills, pharma is using it as a super-optimizer that quietly trims waste and speeds up all the boring-but-expensive parts of the business.
This is like having an AI pair‑programmer built into Visual Studio Code. As you type code or comments, it suggests whole lines or functions, helps you write boilerplate faster, and answers coding questions inside your editor.
This is like giving an energy company a smart financial co-pilot that constantly watches AI spending and recommends cheaper, smarter ways to run AI workloads without breaking what already works.
This is like giving every home a super-smart thermostat and butler that constantly watches prices, weather, and your habits to run appliances at the cheapest, greenest times without you having to think about it.
This is like giving your online store a very fast, very smart assistant that watches how customers browse, what they buy, and how the site behaves, then constantly tweaks recommendations, pricing, and operations to sell more with less waste.
Think of this as a digital super-assistant for an online store that can predict what you’ll sell, talk to customers 24/7, write product descriptions and ads, and adjust prices – all automatically and at scale.
This is like having Google’s Gemini AI sitting inside your code editor, suggesting code, explaining errors, and helping you write and fix software faster as you type.
This is like giving your call center or helpdesk a smart ear that listens to what customers say (emails, chats, social posts) and instantly tells you if they’re happy, angry, or worried, using prebuilt AI from cloud providers.
This is a study that asks: "How much value do Netflix-style ‘Because you watched…’ recommendations really create?" It measures what happens to user behavior and business outcomes when you turn personalized recommendations on vs. off.
This is about how Netflix-style “Because you watched…” lists are created. The system watches what you watch, when you stop, what you rewatch, and then predicts what you’re most likely to enjoy next—like a super‑attentive video store clerk who’s seen your entire viewing history.
This is like having a super-curious librarian who learns what movies, songs, or shows you like and then quietly rearranges the shelves so that whenever you walk in, the things you’re most likely to enjoy are right in front of you.
This is like giving your entire image and video library a smart brain, so it can automatically understand what’s inside every piece of content and instantly surface the right clips or images for any campaign, channel, or audience.
Think of this as a super-smart ad trader that watches billions of people’s clicks in real time and automatically decides which ad to show, to whom, at what price, and on which platform to get the best return—far faster and more accurately than any human team could.
Imagine a 24/7 security guard for your telecom network who has read every past fraud case, watches all current activity in real time, and can explain in plain language why something looks suspicious and what to do next. That’s what generative AI brings to fraud prevention: it doesn’t just flag ‘weird’ behavior, it also helps investigate, summarize, and respond to it much faster.
This is like giving your online merchandising team a super-smart assistant that constantly watches sales, inventory, and trends, then tells you what to stock, when to reorder, and how to price and present products for maximum profit across the whole product lifecycle.
This is like a smart shop assistant for an online store that learns what each customer likes and then quietly rearranges the shelves for them—showing different products, bundles, and follow‑up suggestions before and after purchase, even around returns.
This is like giving your logistics and supply chain a smart autopilot: it constantly studies past deliveries, traffic, and orders to predict what will happen next and suggest the best routes, inventory levels, and staffing without humans having to crunch all the numbers.
Think of this as building ‘co-pilot’ assistants for programmers that can read and write code, help with designs, find bugs, and keep big software projects on track—like giving every developer a smart, tireless junior engineer who has read all your code and documentation.
Think of this as a smart co-pilot for programmers: it reads what you’re writing and the surrounding code, then suggests code, tests, and fixes—similar to autocorrect and autocomplete, but for entire software features.
This is like giving your factory a quality inspector with perfect eyesight who can start spotting flaws in products on day one, just by looking at a few good examples—no long training process, no weeks of data labeling.
This is the kind of AI that decides “Because you watched X, you’ll probably like Y” on Netflix, YouTube, or Spotify. It watches what each user does, compares that to millions of other users, and then builds a constantly updating list of shows, videos, or songs you’re most likely to click next.
Think of Azure AI Video Indexer as an AI librarian for all your videos. It automatically watches every video, recognizes people, objects, brands, spoken words, and emotions, and then turns that into searchable labels and timelines so your teams can instantly find the exact moments they need instead of scrubbing through hours of footage.
This is Netflix’s “smart brain” that watches what every viewer clicks, skips, and binges, then uses a giant AI model to decide which shows and movies to put in front of each person so they’re more likely to hit play.
Imagine your streaming app as a smart host at a party who learns what each guest likes, suggests the right music and games at the right moment, and nudges people before they leave so they stay longer and have more fun. This system uses AI to do that automatically for every user in your mobile entertainment app.
Think of it as a “check engine” light on steroids for jets, ships, and vehicles: AI constantly watches sensor data and maintenance logs and warns commanders *before* something breaks, so they can fix it during downtime instead of in the middle of a mission.
Imagine an always-on personal stylist who remembers everything you’ve ever liked, tried on, or bought, and then quietly rearranges every store you walk into so that the first things you see are exactly your taste, size, and budget. That’s what AI-powered personalized fashion shopping aims to do.
This is like having a smart digital marketing assistant inside Facebook and Instagram that automatically builds and optimizes your ads so more of the right people see them, for less money, with less manual tweaking.
Think of this as a smart autopilot for your online product inventory. It watches your sales, predicts what will sell and when, and then tells you exactly how much stock you should have so you don’t run out or get stuck with excess inventory.
Think of MOON Embedding as a smarter matchmaking system between what shoppers type (and see) and the ads you show them. Instead of just using keywords, it learns a shared ‘language’ across text, images, and other signals so the ad engine can understand what a shopper really wants and pick the most relevant product ad in real time.
Imagine your whole supply chain—factories, warehouses, trucks, and suppliers—running like a smart GPS for your business. It constantly checks traffic (demand), fuel (inventory), and roadblocks (disruptions) and then suggests the best route and timing so you deliver on time with less waste and lower cost.
This is like letting shoppers show your store a picture of what they want instead of typing words. The AI then finds the closest matching products across your catalog in seconds.