Mentioned in 100 AI use cases across 29 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 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.
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.
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.
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 feature is like a smart crystal ball built specifically for items that sell infrequently and unpredictably (spare parts, slow movers). Instead of pretending they sell every week, it predicts when the next order is likely to happen and how big it will be, so planners can stock just enough without overfilling the warehouse.
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 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 giving your company’s security cameras and fire alarms a brain that learns. Instead of waiting for a fixed list of ‘bad things’ to happen, machine learning watches all activity on your network, learns what “normal” looks like, and then flags and blocks suspicious behavior in real time—often before humans would even notice.
This is like a smart weather-and-power crystal ball: it looks at recent weather and production data and uses machine learning to predict how much solar and wind power will be generated in the next few hours.
Think of a smart city as a city with a digital nervous system. AI is the brain that helps it see traffic jams, power usage, crime hotspots, and public service demand in real time, then quietly adjusts lights, signals, and services to keep everything running smoother and safer.
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 like giving every salesperson a super-smart digital co-pilot that can read all your sales data, emails, and activity, then tell them who to call, what to say, and when to follow up to close more deals.
Think of this as a tireless digital sales assistant that listens to your reps, reads your CRM and emails, and then helps them decide who to call, what to say, and when to follow up so they can close more deals with less grunt work.
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.
Think of this as a smart control tower for mining and other heavy industries that watches your environmental, social, and operational data in one place and uses AI to flag risks and opportunities before they impact production or reputation.
This is like giving every student their own smart tutor that learns how they learn, adjusts lessons and exercises to their pace, and gives teachers a dashboard to see who needs what help—automatically.
This is like giving your development team a super-smart intern that reads your code and automatically writes lots of unit tests for it, including for weird edge cases that humans often forget. Then it checks how much of your code those tests actually exercise (code coverage) and how well they cover unusual behaviors.
This is like giving eDiscovery and litigation support teams a super-smart research assistant that can read huge piles of documents, understand what they say, and answer questions about them in plain English—without replacing the lawyers’ judgment.
Think of modern AI in schools as a super-smart homework helper and writing coach that students can use at any time. It can draft essays, solve math problems, and explain concepts in plain language—sometimes so well that it’s hard to tell what work is the student’s and what work is the AI’s.
This is like giving city traffic planners a supercharged crystal ball: AI watches patterns from cameras, sensors, and crash data to predict where and when roads are most dangerous, then suggests fixes such as changing signal timing, speed limits, or enforcement focus.
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 a smart, conversational tour guide for Washington, DC’s open data. Instead of downloading spreadsheets and decoding columns, any resident or city staffer can just ask questions in plain English—“Where are the most traffic crashes?” or “How many affordable housing units were built last year?”—and the AI finds, summarizes, and explains the relevant data.
Think of this as a super-smart teaching assistant that can instantly create practice questions, explain hard concepts in simpler words, draft lesson plans, and give students personalized feedback 24/7.
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.
Think of this as putting a very smart assistant inside your CRM that watches all your customer interactions, predicts which deals are most likely to close, and nudges sales reps on what to do next and when.
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.
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.
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.
Think of AI in games as a super-fast assistant concept artist or junior designer: it can draft levels, story ideas, or graphics in seconds, but it still needs a human game designer to decide what’s fun, meaningful, and on-brand.
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.
Imagine every student having a patient, expert tutor who is available 24/7, remembers what they know, explains things in many ways, and can instantly create new practice problems and feedback—powered by ChatGPT‑like technology instead of a human.
Like giving every online shopper their own smart in-store salesperson who knows the catalog, can answer questions, suggest outfits, and guide them to the right products in real time.
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.
This is about choosing a sales CRM that has a built‑in ‘smart assistant’—it watches all your customer interactions, predicts which deals to focus on, and automates follow‑ups so your reps sell instead of doing admin.
Think of this as a super-watchful digital guardian angel for banks. It constantly looks at payments, credit decisions and customer behavior to spot anything risky or suspicious in real time – much faster and more accurately than human teams alone.
This is like giving the mobile network its own team of smart digital engineers who constantly watch how it’s performing, spot problems early, and automatically fix or optimize things before customers notice.
This is like hiring a 24/7 digital concierge and receptionist that chats with your guests on your website, apps, or messaging channels, answering questions, taking bookings, and handling common requests automatically.
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 an intelligent flight simulator for radiologists in training: instead of just reading textbooks, learners practice on realistic imaging cases while an AI tutor adapts to their level, points out what they missed on the scans, and helps them learn faster and more safely before treating real patients.
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.
This is like giving every clinician a smart medical co‑pilot that can instantly search the trusted UpToDate knowledge base, summarize what matters for a specific patient, and present it in plain, actionable language at the bedside or in the EHR.
Think of this as a playbook for law firms and in‑house legal teams on how to safely and productively use tools like ChatGPT: where they help (drafting, summarising, research), where they’re risky (confidentiality, hallucinations), and what changes in culture and process are needed so lawyers actually adopt them.
This is like giving every college student a 24/7 smart study coach that can explain concepts in simple terms, quiz them, and help them plan their learning, rather than just giving them another digital textbook.
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 a smart template wizard for documents: you tell it what kind of document you need and some details, and an AI writes a first draft for you that you can then review and edit.
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 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 a 24/7 digital career advisor that talks to workers, helps them understand how AI will affect their jobs, and suggests skills and training paths so they can stay employable.
This is like giving every radiologist a smart digital scribe and reporting assistant that understands medical images and dictation, then drafts structured radiology reports for them to review and sign—inside the systems they already use.
Think of this as a team of always-on smart assistants for an insurance company: one that drafts and reviews policies, one that answers customer questions, one that reads long claim files and medical reports, and one that helps underwriters and actuaries make sense of mountains of data.
Think of this as a super-fast, tireless junior claims adjuster. It reads claim documents, pulls out all the important details, checks rules, and drafts decisions or next steps so your human team only needs to review the tricky edge cases.
Imagine your marketing department had an endlessly energetic assistant that could draft ads, personalize messages for every customer, test which versions work best, and adjust campaigns on its own while your team focuses on strategy. That’s what generative AI is doing for marketing and advertising.
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.
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 turning a farm into a ‘smart factory’ for crops and livestock: sensors measure soil, water, weather, and plant health; AI and machine learning learn from this data; then the system tells farmers exactly when and how much to irrigate, fertilize, or treat plants and animals, reducing waste and improving yields.
Think of this like a supercharged weather crystal ball built specifically for power markets: it predicts very detailed weather patterns that drive electricity supply and demand so traders can buy and sell power and gas at the right time and price.
This is a guide about using tools like ChatGPT-style content generators and AI media tools to create marketing content faster so more people discover and visit your brand online.
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.
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.
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.
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.
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.
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.
Think of this as turning today’s security analysts into ‘AI-augmented guardians’: people who use smart tools that can spot cyberattacks much faster than humans, while also learning how to control and question those tools so they don’t make dangerous mistakes.
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.
This is like giving every hotel guest their own 24/7 digital concierge on their phone. Guests can message a smart assistant to ask questions, request services, or get recommendations—without calling the front desk.
This is like a smart, medical-focused chatbot that explains how AI is being used in healthcare and helps people explore use cases, ideas, and benefits of AI in medicine.
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.
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 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.
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 having a super-smart digital assistant for the sports world that can instantly answer questions, create reports, draft commentary, and analyze information for coaches, teams, media, and fans.
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.
Think of this as giving the emergency department a very fast, very experienced digital assistant that helps doctors and nurses notice critical problems sooner, choose better tests and treatments, and move patients through the system more efficiently — especially when things are chaotic and time-sensitive.
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.
This is like giving every sports fan a smart digital concierge that learns what they love—seats, merch, highlights, stats—and quietly adjusts the entire game-day and at-home experience around them.
Like a digital sales coach living inside the CRM that drafts follow-up emails, scripts calls, suggests next-best actions, and answers product questions for agents and brokers.
This is like giving an AI a chest X-ray or MRI scan and having it write the first draft of the radiologist’s report, instead of the doctor starting from a blank page. The doctor still reviews and edits, but the AI does the heavy lifting of describing what it sees.
This is like giving your warehouse a weather forecast for customer demand so it can stock the right products at the right time instead of guessing and getting caught in a storm of shortages or excess inventory.
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 a smart farming advisor that looks at past harvests, weather, and soil data to suggest which crop to plant on a field and how much yield to expect, instead of farmers relying only on experience and guesswork.
This is like giving doctors a very smart, talkative assistant that can explain why it is suggesting a diagnosis or treatment, instead of just giving a black‑box answer. It combines ChatGPT-style conversation with explainable AI tools so clinicians can see the reasoning and evidence behind each suggestion.
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.
Think of this as a smarter CRM that not only stores customer details but also watches what your customers do, predicts what they’re likely to want next, and nudges your sales and service teams with “do this now” suggestions.
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.
Think of this as a smart coach for your field salesforce that watches everyone’s activity and results, then quietly tells each rep: “Do this next, in this territory, with this product, because it’s most likely to hit your quota.”
Think of Amazon Q Developer as a smart engineering sidekick that lives inside your AWS and dev tools. You describe what you want in plain English, and it helps you write, debug, and modernize code, understand cloud architectures, and work with AWS services 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 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.
Think of it as a smart thermometer for your remote workforce’s mood and engagement. It quietly reads signals from surveys, chats, check-ins, and activity data to tell managers who’s thriving, who’s checked out, and where to intervene before problems blow up.
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.