Mentioned in 100 AI use cases across 19 industries
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.
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.
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 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 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 putting a smart security guard in your cloud data center who never sleeps, learns what “normal” looks like, and automatically flags or blocks suspicious behavior before it turns into a breach.
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.”
Think of your online store as a smart salesperson who knows every customer’s tastes, can instantly tidy and rewrite your product catalog, and can answer questions 24/7 in natural language. This article describes how to bolt that salesperson’s “AI brain” onto a typical ecommerce site using search, recommendations, and automation.
Think of this as a field guide to all the ways computers can learn from medical and pharma data—like a tireless junior doctor and data analyst rolled into one—to help spot diseases earlier, pick better treatments, and run hospitals and clinical trials more efficiently.
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 Alibaba.com getting a super-smart digital buying assistant that doesn’t just answer questions, but can actually help run parts of the buying and selling process for you — from finding the right suppliers to lining up orders and follow‑ups, almost like giving every buyer and seller a junior operations team inside the platform.
Think of AI in retail as giving every shopper a smart, always‑on personal assistant plus a savvy store manager behind the scenes. It helps customers quickly find the right products, prices, and deals while quietly optimizing inventory, supply chain, and staffing so shelves are stocked and operations run cheaper and faster.
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.
Imagine an online clothing store that can guess your right size as accurately as a good salesperson who’s seen thousands of customers before. This research tests different machine learning "brains" to see which one predicts the best size for each shopper using past data like body measurements and purchase history.
This is like giving your company’s videos and images a smart librarian who can instantly find any clip or picture based on what’s inside it (people, objects, actions, scenes), even if no one ever tagged or labeled the files correctly.
Imagine every time you open your TV, there’s a smart concierge who has watched everything you’ve ever seen, remembers what you liked, what you quit after 5 minutes, what you binged in a weekend, and what people like you enjoy. That concierge quietly rearranges the shelves so the things you’re most likely to love are always right in front of you. That’s what a Netflix-style recommender system does—at software scale for millions of viewers.
Think of AI in marketing as a team of tireless digital interns that watch every interaction your customers have with your brand and then help your marketers decide: who to talk to, what to say, when to say it, and on which channel—automatically and at massive scale.
Think of this as a very smart fashion brain that studies what people actually buy and wear, then helps brands decide what to design, how much to produce, and which customer to show it to—so you make more hits and fewer flops.
This is like giving every shopper a smart digital tailor that knows their body and how different brands really fit, so they can pick the right size first time when buying clothes online.
This is about using AI as a super-smart control center for factories and supply chains. It watches machines, inventory, orders, and logistics in real time, then predicts problems before they happen and suggests the best way to run production so you waste less time, material, and money.
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 like a super-smart TV ad matcher that watches the show in real time, figures out what it’s about and who is likely watching, and then picks the most relevant ad to show that viewer – without needing their name or cookies.
This is like a smart, always-available hospital receptionist that understands what patients need, checks doctor calendars, insurance rules, and clinic constraints, and then finds and books the best possible appointment slot automatically.
This is like an always‑on smart salesperson that constantly watches demand, competitors, and stock levels, then automatically adjusts your product prices to hit your goals (more profit, more volume, or both) without a human changing prices all day.
This is like giving your online store a smarter salesperson who understands spoken questions (voice search) and photos (visual search), then guides shoppers to exactly what they want so they’re more likely to buy.
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.
Think of a streaming service that knows not just what shows you like, but also when you watch, what device you use, and whether you usually binge or sample. Contextual recommendation algorithms use this extra situational information to put the right movie, song, or game in front of you at the right moment.
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.
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.
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.
Think of this as turning your marketing team into pilots of a self-driving ad machine: humans set goals and guardrails, while AI continuously tests, tweaks, and reallocates budget across channels to get you more customers for less money.
Think of AI in programmatic advertising as a super-fast trading bot for ad space: it constantly scans who is online, what they’re doing, and in a split second decides which ad to show, at what price, and on which website to maximize your marketing results automatically.
Think of these AdTech AI agents as a team of tireless digital interns that understand ads, audiences, and campaign data. You tell them your goals (e.g., ‘get more app installs in Germany within this budget’), and they continuously research options, tweak settings, buy media, test creatives, and report back—without needing a human to click every button in every platform.
Think of AI code assistants as a smart co‑pilot sitting next to every developer: they read what you’re typing, suggest the next few lines or whole functions, explain confusing code, and help spot bugs — much like autocomplete on steroids for programming.
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.
This is about using AI as a smart digital marketing assistant that creates, tests, and optimizes your online ads automatically so you sell more without manually tweaking every campaign.
This is like giving your marketing team a super-smart assistant that constantly studies which people click and buy, then automatically adjusts who sees your ads so you’re not wasting money showing ads to the wrong audience.
This is like having an AI assistant watch a live TV channel or livestream for you and take notes in real time—who is speaking, what’s being said, topics, scenes, and key moments—so people and systems can react instantly instead of waiting for manual review later.
This is like having a smart, always-on Google marketing consultant that looks at your ads and analytics data, explains what’s happening, and suggests concrete optimizations to improve campaign performance.
This is like giving your online ads a motion upgrade and a built‑in coach. The system can turn static images into eye‑catching animations and automatically tell you which versions of your ads work best, so you waste less money guessing what creatives to run.
Think of the future transport system as a giant, city-wide brain. Instead of each car, bus, or train acting on its own, AI watches traffic, weather, demand, and incidents in real time and then orchestrates everything—routes, signals, pricing, and even maintenance—so people and goods move faster, safer, and cheaper.
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 every product in your store has a smart price tag that quietly watches how many people want it, what competitors are charging, and how much stock you have left—and then changes the price in real time to get the best mix of profit and sales, not just on Black Friday but all season long.
This is like having a very fast junior developer who writes code for you, but this guide teaches you how to double‑check that junior’s work so it’s safe, correct, and secure before it goes into your product.
This is about the next generation of digital ad buying, where software agents act like tireless junior media buyers. Instead of humans manually tweaking bids, budgets, and targeting rules in programmatic platforms, AI agents continuously watch performance and automatically adjust campaigns to hit goals like ROAS or CPA.
Think of AIOps platforms as a 24/7 AI control tower for your IT systems. They watch logs, metrics, and alerts from all your tools, spot patterns humans would miss, and automatically fix or route problems before they become outages.
Think of this as a smarter, more polite billboard system for the internet. Instead of shouting the same message at everyone, AI helps show the right ad to the right person at the right time—while staying within new privacy rules.
This is like an AI pair-programmer built directly into Visual Studio Code. As you type, it suggests whole lines or blocks of code, helps write tests, explains code, and can transform comments or natural language into working code snippets.
This is like giving your developers a smart co-pilot inside JetBrains IDEs that can read and write code, explain it, and help with everyday tasks without leaving their usual tools.
Think of AI code assistants as smart copilots for programmers. As you type, they guess what you’re trying to build and suggest code, explain errors, write tests, and help you understand unfamiliar code — like an always‑available senior engineer sitting next to every developer.
GitHub Copilot is like an AI pair-programmer that sits in your code editor and suggests whole lines or blocks of code as you type, based on your comments and existing code.
This is like giving West Ham’s scouting team a super-smart digital assistant that watches players, tracks their stats, and flags promising talent they might otherwise miss.
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.
This is like giving West Ham’s scouts a super-smart assistant that watches huge amounts of player data and video, spots promising young talent early, and ranks who is most worth a closer look.
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 having a super-smart app developer sitting next to you while you describe what you want in plain English. You say the ‘vibe’ of the app – who it’s for, what it should roughly do – and the AI fills in the technical details, wiring screens, data and logic so a working app appears much faster than with traditional coding.
This is like giving every software developer a smart co-pilot that suggests code as they type, understands your codebase, and can help write, refactor, or explain code—while staying under your company’s control instead of sending everything to a public cloud AI.
Imagine every shopper in your store sees a shelf that magically rearranges itself to show the products they are most likely to buy at the best price for them and for you. AI personalization for retail media does that on your website and app ad slots in real time.
This is like giving every software developer a smart pair-programmer that lives inside VS Code: it reads the code you’re writing, suggests the next lines, helps refactor, and explains unfamiliar code or errors in plain language.
This is like giving a store a crystal ball that uses past sales and promotions to guess how many items customers will buy in the future, so they stock just the right amount.
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 teaching retail systems to ‘learn’ from sales, customer, and inventory data the way a great store manager does—spotting patterns in what people buy, when they buy, and what makes them come back, then using that to decide prices, promotions, and stock levels automatically.
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.
This is about tools like GitHub Copilot or ChatGPT that sit inside a developer’s editor and suggest code as they type—like an auto-complete on steroids for programmers. The article’s core claim is that, in real-world use, these assistants don’t actually save as much time as the hype suggests.
This is a guide showing how to plug ‘AI helpers’ into every step of your software development process so your developers have smart assistants that can plan, write, review, and maintain code alongside them.
Think of GitHub Copilot as an AI pair‑programmer that sits in your code editor and guesses what you want to type next, suggesting whole lines or functions based on what you’ve already written and your comments.
This is like giving every shopper their own smart stylist who has read the entire store catalog, remembers what similar customers liked, and can instantly suggest the right products and bundles in natural language across web, app, email, and chat.
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 your company a super‑listener that reads what customers write (emails, chats, reviews, social posts) and instantly tells you if they’re happy, angry, or confused—at large scale and in real time.
Imagine every shopper on your website having an ultra-knowledgeable personal stylist and product expert who instantly understands what they want, searches your entire catalog, and presents the right items in the right words and images—at scale, 24/7.
This is a playbook for getting your software teams ready to use AI as a smart co‑pilot—helping them write, review, and test code faster—rather than replacing them.
This is like giving every software engineer a smart co-pilot that reads their whole codebase, remembers how things work, and helps write, review, and understand code directly in their workflow.
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.
Think of AIOps on AWS as putting an autopilot on your IT operations. It watches logs, metrics, and alerts across your cloud systems 24/7, learns what “normal” looks like, and then automatically flags problems, finds root causes faster, and can even fix some issues without a human jumping in.
This is like having a super-smart analyst sitting next to you during an NFL game, constantly crunching numbers on every player and play, then drawing helpful lines and graphics on the screen to explain what’s happening and what might happen next.
This is like giving football scouts a supercomputer assistant that has watched every match in the world and read every stats sheet, then pointing it at "find us the next star that fits exactly how West Ham plays."
This is Meta’s “autopilot” for ads: instead of you manually picking every audience detail, Meta’s AI watches how people behave on Facebook and Instagram, learns who reacts to which ads, and then automatically shows your ads to the people most likely to care, in real time.
Think of Ringier’s ad inventory like airplane seats: if the price is too low, you leave money on the table; if it’s too high, seats go empty. This AI system constantly studies how buyers behave in the ad auction and automatically adjusts the minimum price (floor price) so that more impressions sell at the best possible price without scaring away demand.
This is a playbook from AWS for running your IT operations with a ‘smart autopilot.’ It explains how to use AI to watch logs, metrics, and alerts so it can spot problems early, suggest fixes, and sometimes even act automatically—before users notice something is broken.
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.
This is like giving your media buying team a super-calculator that constantly studies billions of ad impressions and audience signals, then automatically adjusts who you target, where you show ads, and what you pay so every dollar has a better chance of turning into real business results.
Think of GEM as a super-smart matchmaker that reads every ad, every user’s behavior, and a ton of context, then “imagines” which specific ad version and placement a person is most likely to respond to—millions of times per second across Meta’s apps.
Think of this as a super-analyst that watches every ad impression, every click, and every purchase in real time, then constantly tweaks who sees which ad, on which channel, and at what price to get more results for the same (or less) budget.
Think of this as a very smart scorekeeper for your marketing spend. Instead of guessing which ads, channels, and campaigns are working, AI sifts through all the messy data and tells you which dollars are actually driving sales – and which ones you can safely cut.
This is about using machine learning as a smart ‘check engine’ light for factories and vehicles. Instead of waiting for a part to fail or doing maintenance on a fixed calendar, models watch sensor data (vibration, temperature, voltage, etc.) and warn you ahead of time when something is likely to break so you can fix it before it causes downtime.
This is Netflix’s R&D lab for making sure every member quickly finds something they’ll love to watch. Think of it as a constantly learning concierge that rearranges the entire Netflix store for each viewer, in real time.
This is like a smart co-pilot for your ad campaigns that constantly watches performance and quietly suggests what to tweak—budget, segments, messaging—while the campaign is still running so you don’t waste money.
Imagine your entire order-to-delivery process as a relay race where different specialists carry the baton: one checks inventory, another chooses the best supplier, another plans shipping, and another keeps the customer updated. This solution uses a team of AI “agents” on AWS to coordinate that whole relay automatically, so orders move from quote to delivery with minimal human intervention.
This is like giving your online store a smart brain that watches how every shopper browses and buys, then quietly adjusts prices, search results, and recommendations so each person sees what they’re most likely to want and buy.
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.
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 like giving Netflix a smart brain that quietly watches what you watch, when you stop, what you search for, and then rearranges the entire app, recommendations, images, and streaming quality just for you—millions of people at once, all differently.
Think of this as Netflix building its own very smart "taste brain" that understands movies, shows, images, and text, then wiring that brain into all the ways it personalizes what you see — rows, artwork, search, and more — instead of relying on a bunch of separate smaller brains.
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 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 an online shop or airline that quietly adjusts prices for each customer the way a skilled market trader does—watching how you browse, what you’ve bought before, and how urgent you seem—then offering a price it thinks you’ll accept right now.
This is Google adding an AI shopping helper that can guide customers from product discovery all the way through checkout, automatically filling in steps, suggesting options, and smoothing out the buying process inside Google’s shopping surfaces.
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.
Imagine a shopper can take a photo of a dress they see on the street, upload it to your online store, and instantly see similar dresses you sell—no need to guess keywords like “floral midi dress with puff sleeves.” That’s visual search for ecommerce.