Mentioned in 296 AI use cases across 30 industries
AI runs smart tests automatically every time developers change code, so teams get quick feedback before releasing updates.
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 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 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 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 every sales rep a smart digital assistant that reads your CRM, learns what a good prospect looks like, and helps you find, research, and reach out to new leads faster—directly inside HubSpot.
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 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 this as an early‑warning radar for student success. It looks at students’ past grades, attendance, and other records and then predicts who is likely to do well or struggle, so teachers and administrators can step in before problems become failures.
This is about using tools like ChatGPT inside and between government agencies so that routine paperwork, drafting, coordination, and information sharing between ministries and departments happen faster and more accurately, with the AI acting like a smart civil-service assistant that never sleeps.
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.
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 upgrading from a dumb FAQ bot to a smart service rep that can actually understand what customers mean, look up the right information, and respond in full sentences across channels—without needing a human every time.
This is basically a playbook for teachers on how to use tools like ChatGPT in smart, creative ways—like having a tireless teaching assistant who helps write lessons, examples, and exercises, while students also learn how to use AI critically and responsibly.
This is like giving every Premier League fan their own personal commentator and researcher that instantly finds the best clips, stats, and stories from decades of matches, and tailors them to what that fan cares about.
This is like giving every teacher a super-fast, tireless teaching assistant that can read student work, score it, and draft feedback so the teacher can focus on teaching instead of paperwork.
Think of today’s big AI models as brilliant general doctors who know a little about everything but aren’t yet safe or precise enough to treat complex, high‑risk patients. This paper is about how to retrain and constrain those general doctors so they can safely become top‑tier specialists in specific medical tasks, like reading scans, summarizing patient records, or supporting treatment decisions.
This is like giving your sales pipeline a smart assistant that constantly watches every deal, predicts which ones are most likely to close, and nudges reps on what to do next so nothing slips through the cracks.
Think of this as a super-smart lab assistant for battery scientists: it looks at huge amounts of test data from lithium-ion batteries and then suggests the best recipes and operating conditions to make batteries last longer, charge faster, and be safer—without having to run every experiment physically.
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 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 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.
Imagine a super-scientist that can read research papers, look at chemical structures, examine lab images, and understand patient data all at once, then suggest which molecules to try next or which trial designs are most promising. That’s what multimodal AI is aiming to do for drug R&D.
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 health regulators and watchdogs a super-smart assistant that can read huge amounts of health system data (claims, procurement, staffing, outcomes) and flag patterns that look like fraud, waste, or corruption so humans can investigate faster and more fairly.
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.
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 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 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.
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.
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.”
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 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.
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.
Imagine every student and every teacher having a patient, always-available tutor in their laptop that knows the Khan Academy curriculum and can explain things step by step, ask questions back, and guide practice instead of just giving answers. That’s what Khanmigo is: an AI helper built into Khan Academy for learning and teaching.
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.
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.
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 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 like giving every hotel guest their own smart local concierge who knows the city, the guest’s preferences, and the hotel’s offerings, and then auto-builds a detailed, bookable trip plan for their stay.
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.
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 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.
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.
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 a Security Operations Center as an airport control tower watching thousands of planes (devices, users, apps) at once. Traditional tools show you every single radar blip and alarm; humans get overwhelmed and miss real threats. AI- and ML-powered SIEM act like an assistant that learns normal flight patterns, filters out the noise, and flags only the suspicious flights that may be hijacked — and often does it in real time.
This is like giving your security team an AI co-pilot that watches everything in your environment in real time, spots attacker behavior (including AI-generated attacks) faster than humans can, and automatically helps block and contain those attacks before they spread.
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 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 Sarai as a smart, always-on hotel salesperson and receptionist that can talk with guests on your website or messaging channels, answer questions about your property, and complete reservations on its own – like your best front-desk agent working 24/7, but digital.
This is like giving your travel website a smart, 24/7 travel agent that chats with visitors, helps them find trips, and completes bookings automatically.
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.
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.
This is like giving your hotel a smart, always-on digital concierge that can answer guest questions, handle bookings and services, and route issues, all powered by Microsoft’s AI stack.
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 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.
Imagine a hotel that remembers every guest like a great concierge: what room temperature they like, which pillow they prefer, when they usually arrive, and what they tend to order. AI in hospitality is the digital brain behind that experience—quietly watching patterns in bookings, reviews, and operations so staff can serve guests faster, more personally, and with fewer mistakes.
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 tools like ChatGPT into a smart study and research partner for a university: it helps students learn faster, teachers design better lessons, and researchers explore ideas more quickly, all while the university figures out how to use it safely and effectively.
Imagine giving your software tester a super-smart assistant that can read requirements, write test cases, suggest missing checks, and even help explain bugs—just by talking to it in natural language. This paper surveys how those assistants, powered by large language models like ChatGPT, are being used in software testing and what still goes wrong.
Imagine your sales team has a long line of people waiting outside the store, but only a few will actually buy. AI lead scoring is like a smart bouncer that looks at each person’s behavior and history, then quietly tells your reps, “Talk to these five first; they’re most likely to buy today.”
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.
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 every salesperson a smart, trustworthy assistant that lives inside their CRM. It listens to all the data in your sales boards, summarizes what’s important, predicts which deals need attention, and drafts the next emails or follow‑ups for you, while keeping managers in control of what AI can and can’t do.
This is like putting a smart air-traffic-control system around your AI tools in finance. Instead of just letting AI ‘fly the plane’ on fraud checks, payments, or credit decisions, Sardine adds guardrails, logs, and supervisors so every AI action is monitored, explainable, and can be stopped if it looks unsafe or non‑compliant.
Think of this as putting an AI ‘air traffic controller’ on top of your customer support systems in the cloud. It quietly watches everything—traffic spikes, slow services, error logs—and automatically tunes the environment so support agents and customers get fast, reliable help 24/7.
Now Assist is like an AI super-assistant built directly into ServiceNow that helps employees and agents answer questions, resolve tickets, and complete workflows much faster by understanding natural language and surfacing the right information or actions automatically.
Think of this as a citywide ‘control tower’ that watches what’s happening—traffic, utilities, emergency calls, citizen requests—and then uses AI to suggest faster, cheaper, safer ways to run city services.
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 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 would be like a smart insurance analyst that reads articles and policy documents about social engineering fraud (phishing, fake invoices, business email compromise) and explains—in plain English—what is and is not covered, where the gaps are, and what questions a broker or client should ask.
This is like giving every resident a smart, friendly guide to city hall that knows their situation, speaks their language, and can help them quickly find and use the right public services—without having to stand in line or fill out confusing forms.
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.
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 your litigation and investigations team a super‑powered, tireless junior lawyer that can read millions of emails and documents in hours, highlight what’s important, group similar issues, and surface risks and evidence so your senior lawyers only spend time on what really matters.
This is like having a smart, offline paralegal that can read through all your case files, contracts, and statutes stored on your own servers and then answer questions by mixing two skills: fast keyword search and “meaning-based” AI search. It never has to send your documents to the cloud.
Think of this as a super-assistant for your sales team that listens to customer data, drafts emails and proposals, suggests next-best actions, and keeps the CRM clean so reps can spend more time talking to customers instead of typing notes.
This is like giving Europol a very smart digital analyst that can sift through massive amounts of police and intelligence data, spot patterns, and suggest leads far faster than human teams could do alone—but in a closed, highly secret environment.
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.
Think of this as turning your marketing from guessing to GPS navigation. Instead of marketers guessing what customers might want, AI and predictive analytics study past behavior (clicks, purchases, time on site) to forecast what each person is likely to want next and automatically adjust campaigns, channels, and offers in real time.
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 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.
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 a buyer’s guide to a toolbox of AI helpers for marketers — one tool writes copy, another makes images, another helps with SEO — so your team can get marketing done faster with fewer manual tasks.
Think of this as a playbook for turning tools like ChatGPT into a tireless junior marketer that helps you research, plan, draft, and optimize content so your team focuses on strategy and creativity instead of repetitive work.
This is a roundup of software tools that act like supercharged writing and design assistants for marketers. You tell them what you need—like a blog post, landing page, ad copy, or social post—and they draft it for you so teams can produce more content, faster, with consistent quality.
This is about using AI as a smart marketing strategist, not just a copywriter—like having a junior CMO that can help you research audiences, shape campaign ideas, and test messages before you spend real budget.
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.
Think of this as an AI co-pilot sitting on top of Microsoft Dynamics 365 that helps marketers make smarter decisions: it reads your customer and campaign data, surfaces insights, and suggests next best actions instead of you digging through endless reports.
This is like a super-powered search engine for video: instead of scrubbing through hours of footage, you can type what you’re looking for and the AI jumps you straight to the right moments.
Imagine every editor in your newsroom has a super-smart assistant that can instantly scan documents, social feeds, data, and past coverage, then suggest story angles, headlines, images, and even first drafts—while the human editor still decides what is published.