Mentioned in 319 AI use cases across 30 industries
This is like putting a smart security camera on all your insurance transactions. It watches events in real time, spots suspicious patterns that look like fraud, and alerts your team before money goes out the door.
Instead of checking who changed what in each ML account one by one, a bank can collect all activity logs in one place to spot risky actions faster.
An AI assistant watches the safety reports and jobsite photos your team already uploads, spots dangerous issues like missing hard hats or water near electrical panels, and warns supervisors right away.
An AI system creates product ads by first planning the big picture, then filling in the detailed wording and content for the ad.
A chatbot answers common billing, outage, and account questions any time of day, so customers get quick help and human agents can focus on harder cases.
An AI assistant answers questions like 'Is this drug already in shortage?' and 'What should I do next?' by pulling from FDA shortage pages, database entries, and guidance documents.
A movie studio could offer a safe app where fans make their own clips inside approved story worlds instead of using unlicensed AI tools.
Think of NVIDIA BioNeMo as a set of very smart chemistry and biology "co-pilots" that can read and write molecules and proteins the way ChatGPT reads and writes text. Instead of scientists manually trying out millions of possibilities in the lab, BioNeMo helps them design and screen promising drug candidates on a computer first, massively narrowing the search space.
AI listens to a live game broadcast and quickly translates it into other languages without flattening the excitement in the announcer’s voice.
The platform can understand and organize ad creative in different languages so teams in different countries can still find and reuse the best-performing content.
Give each AI agent a badge, a camera, and a fence: control what it can access, watch what it does, and stop forbidden actions in real time.
This is like an ultra-detailed 3D CAD tool for molecules, powered by AI. Instead of engineers designing car parts, RosettaFold3 designs and predicts how proteins, DNA, and small‑molecule drugs fit and move together inside the body.
AI assistants look up the right internal documents and data before answering, so responses are more accurate.
Aviva built a shared factory for machine learning so data scientists can build, test, approve, deploy, and monitor models in a repeatable way instead of doing manual setup each time.
All factory data is stored in one organized place so analysts and data scientists can train models, test them, and build reports from the same data.
The AI writes a first draft of business documents so teams start from something useful instead of a blank page.
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.
Filmmakers can use AI to help write, plan shots, scout locations, edit, add subtitles, and improve visuals so small teams can make films faster and cheaper.
An agency uses Copilot like a fast analyst assistant to pull reports and find campaign problems in minutes instead of half an hour.
Instead of building AI from scratch, a studio teams up with an AI company to make approved tools for creators and professionals.
Researchers built an AI system that helps design hard-to-study proteins that do not hold one fixed shape, using both machine learning and physics simulations.
A pretrained language model is further trained on conversation examples so it responds more naturally in chat-style interactions.
Small businesses are using AI like a digital helper to speed up everyday work and get more done with the same team.
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 an insurer a living, zoomable map of how cars and drivers behave in the real world, updated in near real time, and then using AI to spot risks, opportunities, and patterns that humans would never see by looking at tables and static reports.
This is like upgrading an insurer’s old spreadsheet-based risk calculator to a smart assistant that not only predicts which policies are risky more accurately, but also clearly explains which customer or policy features drove each prediction.
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.
Imagine a huge classroom where different versions of Google’s Gemini sit side‑by‑side answering the same homework and exam questions. A panel of judges then scores which Gemini answers are most helpful for students. This paper is about building that classroom arena and seeing how good Gemini really is as a learning assistant.
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.
Like having a junior project finance lawyer and investment analyst who has read this entire renewable energy project finance primer and can answer questions or summarize sections on demand.
This is like giving a retail business a smart digital operations manager that can analyze sales and customer data, answer questions, and suggest actions to run stores and ecommerce more efficiently.
This is like putting an extremely fast, tireless safety inspector on every camera around your construction site. It watches video in real time and automatically spots things like workers without helmets, people entering danger zones, or unsafe equipment situations so supervisors can react immediately.
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.
Imagine a blood pressure clinic that treats each patient the way a tailor makes a custom suit: it uses your genes, lifestyle, gut bacteria, and medical history—analyzed by AI—to pick the drug and dose that fit you best instead of guessing and adjusting over months.
This is like a detailed scoreboard for your online ads that shows which clicks and channels actually helped make a sale instead of just guessing from the last click.
This is like a super-accurate 3D blueprint generator for molecules inside the body. Instead of running long, expensive lab experiments to see how proteins and potential drugs fit together, AlphaFold 3 uses AI to predict those shapes on a computer in hours, so scientists can shortlist the best drug ideas much faster.
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 if every customer saw a version of your brand that felt like it was made just for them—a website, email, or ad that talks in their language, remembers their preferences, and adapts in real time as they interact. AI personalization is like giving every customer their own dedicated concierge who knows them well and continuously learns how to serve them better.
This is like a referee who re-watches the whole game instead of trusting each player’s version of what happened. Rather than believing every ad platform’s claim about how many sales it drove, it helps you measure true impact across all channels together.
Like having a super-analyst who reads all the technical reports on future thermal energy storage, compares options, and tells you which technologies are most worth betting on and why.
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.”
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 giving every journalist a smart digital assistant that can help research, draft, fact‑check, and personalize stories at scale—while editors stay in control of what gets published.
This is about using smart software and robots as a ‘digital brain’ for mines—helping decide where to dig, how to run equipment, and how to keep workers safe, based on huge amounts of data from sensors, machines, and geological surveys.
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 about using smart software that learns from patterns in network traffic and user behavior to spot hackers and suspicious activity much faster than human teams or rule-based tools can, and then automatically block or contain threats before they spread.
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.
Think of Copilot Arena as a public test track where many different AI coding copilots race on real developer tasks. Instead of trusting vendors’ own benchmarks, this platform lets you see how each coding AI actually performs with real users and messy, real-world code problems.
This is like giving your claims department a team of tireless digital assistants that can read documents, understand photos, and follow rules to move claims from ‘reported’ to ‘paid’ with minimal human involvement.
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 having a 24/7 digital concierge who looks and talks like a real person, remembers guest preferences, and can handle routine questions and requests for a hotel or luxury travel brand without needing more staff at the front desk.
Imagine a tireless digital news intern that reads thousands of articles every minute, picks the most relevant ones for your audience, and drafts short summaries or full pieces so your editors just review and polish instead of writing everything from scratch.
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 like a smart accountant for your marketing budget that looks at all your past campaigns and figures out which channels (Google, Meta, TV, email, etc.) actually drove sales, and by how much, so it can tell you where to move money to get more revenue for the same spend.
Like a 24/7 digital front-desk clerk that can answer questions and help guests book hotel rooms automatically over chat or web.
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 like installing security cameras on all the doors of your store so you can finally see which doors customers actually use before they buy. Instead of guessing which ads or channels work, you can trace the real path people take from first touch to purchase.
This is like giving your existing code to a very smart assistant and asking it to write the unit tests for you. The large language model reads the code, guesses what it should do, and then writes test cases to check that behavior automatically.
This is like having a smart sales assistant that reads a prospect’s details in HubSpot and then drafts a personalized outreach email in Gmail for you, so reps just review, tweak if needed, and send.
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.
Think of this as a guide to how modern AI can act like a very fast, tireless financial analyst: reading huge volumes of data, spotting patterns in markets or risk, and then suggesting what to do next.
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 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.
Think of your company’s network as a city. AI gives both the police and the criminals super-powered binoculars and autopilot cars. Defenders use AI to spot unusual behavior and block attacks faster than humans can. Hackers use AI to scan for weak doors, write convincing scam messages, and automate break‑ins at scale.
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 describes how modern social platforms use AI as an always‑on assistant that decides what each person sees, when they see it, and how brands can talk to them—so every user’s feed and every ad feel custom‑made.
This is like an automatic film composer for your social or marketing videos: you upload or create a video, and the AI instantly picks, edits, and times professional music and sound effects so it fits the mood and pacing without you needing musical skills.
This is like Netflix-style recommendations, but for news and media, where editors set the rules of the game and algorithms handle the heavy lifting of matching each reader with the most relevant stories and content.
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.
Think of this as a global field guide to “AI-as-a-junior-lawyer”: it surveys how tools like ChatGPT-style assistants, contract analyzers, and legal research bots are being used in law firms and in‑house teams around the world, and what that means for cost, risk, and competitiveness.
This is like giving your insurance claims department a tireless digital assistant that can read claim documents, check details, and help decide payouts much faster and more consistently than humans alone.
Think of this as a tireless creative and analytics assistant that can draft campaigns, personalize messages for each customer, and learn from results to do better next time—all in minutes instead of weeks.
This is like giving every scientist in a pharma or biotech lab a tireless, super-fast research partner that can read millions of papers, spot hidden patterns in data, and suggest the next best experiment — while the human still makes the final judgment calls.
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.
This is like giving every doctor an always‑on digital colleague that has read every medical textbook, guideline, and journal article, and can quickly suggest possible diagnoses and treatments while the doctor is seeing a patient.
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 like a very powerful ‘Google Maps brain’ that can look at extremely detailed satellite and aerial images, understand what’s on the ground (roads, buildings, ships, fields, etc.), and connect that with other types of data, so many different applications can reuse the same core model instead of building their own from scratch.
This is like having an on‑demand creative team that instantly drafts lots of ad visuals and copy options for you, so your marketers just pick and refine instead of starting from a blank page.
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.
Think of this as giving your hotel a super-smart digital concierge that quietly watches what guests like, remembers their preferences, answers questions 24/7, and helps staff respond faster and more personally to every stay.
This is like an AI movie studio where you type or upload an idea and it automatically creates a video clip for you, including the visuals, voices, and sound effects, without needing cameras, actors, or editors.
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.
Think of Orbitae AI as a smart control tower for an automotive company’s data. It connects to all your scattered data sources (production, sales, after‑sales, supply chain), lets managers ask questions in natural language, and then turns complex analytics into simple dashboards, forecasts, and recommendations to run the business better and faster.
This is like giving every customer their own smart, always-on concierge that remembers who they are, what they like, and can talk to them naturally over chat, email, or other channels—without needing a human to type every response.