Mentioned in 162 AI use cases across 28 industries
The AI writes a first draft of business documents so teams start from something useful instead of a blank page.
A pretrained language model is further trained on conversation examples so it responds more naturally in chat-style interactions.
Before buying or rolling out an AI analyst, a firm can run competing models through the same finance test to see which one actually handles finance work best.
This is about using tools like ChatGPT—tailored for lawyers—to draft documents, summarize long cases, search through legal information, and automate repetitive office work so law firms can focus more on clients and strategy.
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.
Think of it as a supercharged, always-on legal research assistant that can read huge volumes of cases and statutes and then help lawyers quickly find relevant law and draft documents in plain English.
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.
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.
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 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.
This is like having an always-available teaching assistant that reads students’ short answers and reports, compares them to a grading guide, and suggests scores and feedback so instructors don’t have to grade everything by hand.
This is like giving your risk and strategy team a super-fast analyst that can read the entire 2025 Federal Reserve Financial Stability Report, highlight what matters for your firm, and answer follow‑up questions in plain English.
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.
This is like giving every customer a tireless digital helper that can answer questions, solve common problems, and route issues to the right human—24/7—through chat on your website, app, or messaging channels.
This is like a standardized test for legal AI tools. Instead of trusting marketing claims, it builds exam-style questions and grading rubrics so you can see which AI systems actually understand law and which ones just sound confident.
This is Google’s push to put AI ‘co‑pilots’ into classrooms and homework tools, so students and teachers can get personalized help, smarter search, and automated support directly inside Google’s existing education products (like Search, Chrome, Workspace, and Classroom).
This is like a game-making and story-writing assistant in one: you write or describe a story, and the AI helps turn it into an interactive, playable experience with scenes, characters, and branching choices.
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 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.
Imagine every student getting a 24/7 teaching assistant who knows their strengths, weaknesses, and pace, and quietly adjusts homework, hints, and explanations just for them. This Dartmouth work shows that AI can realistically play that role for large classes at once.
This is like having a smart digital tutor that learns how each student studies best, then automatically adjusts lessons, examples, and practice questions to fit that student—while helping teachers design and manage this at scale.
Think of this as giving pharma companies a super-smart digital lab assistant and paperwork robot rolled into one. The assistant can sift through mountains of scientific data to suggest promising new drugs faster, and it can also take over a lot of the routine documentation and admin work that bogs down scientists and health‑care workers.
Think of this as a smart digital marketer just for real estate: it helps you instantly create listing descriptions, social posts, ads, and visuals tailored to each property so you can sell faster with less manual work.
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.
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 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.
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.
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.
Like having a research assistant that constantly reads and understands the FDA’s drug shortage pages and instantly answers your teams’ questions about what’s in shortage, why, and what the latest guidance is.
Think of this as a smart research analyst that constantly reads and updates all available reports, news, and data about military and commercial drones, then answers your questions in plain English—like a ‘ChatGPT’ specialized in the global drone market.
This is a report from EliseAI showing how their AI assistant acts like a 24/7 digital leasing and resident services agent for apartment communities—handling inquiries, scheduling tours, and responding to residents so the on-site team can focus on higher‑value work.
This is like giving your call center and support team a super-smart digital receptionist that can talk to customers, answer questions, and route issues 24/7 without getting tired.
Imagine a very smart digital artist and writer that has watched and read almost everything on the internet. When you ask it for a song, a video idea, a game character, or a script, it can instantly draft something new that looks like a human made it. That’s generative AI: a content factory that turns instructions into creative outputs (text, images, music, video, code).
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.
It’s like giving every content marketer a super-fast writing and research assistant that can draft blogs, social posts, and emails in minutes instead of hours, while the human focuses on strategy and polishing.
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.
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.
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.
This is like having a tireless digital marketing copywriter and content assistant that can help you brainstorm, draft, and repurpose marketing content across channels using AI.
This is like having a tireless junior lawyer who can quickly read, draft, and explain legal documents, but works inside your computer instead of at a desk.
This is like giving every marketer a smart digital assistant that can brainstorm campaigns, write and adapt content for lots of channels, and analyze what’s working—so a small team can operate like a much larger one.
This is like giving litigators a super-fast junior attorney who can skim millions of pages, highlight what matters for your case, and organize it for you in hours instead of weeks.
This is like giving your helpdesk inbox a smart assistant that can read every customer message, understand what it’s about, answer common questions instantly, and route tougher issues to the right human agent with all the context pre-filled.
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.
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.
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 having an always‑on digital writers’ room that helps you brainstorm concepts, outline plots, write scenes, and refine scripts for films, TV, ads, or online videos in minutes instead of weeks.
Think of this as a tireless digital marketing assistant that can design ads, test many versions automatically, and keep tweaking them to get more clicks and conversions—without a human having to watch it every minute.
This is like having a tireless junior creative team that studies which ads perform best, then automatically drafts new versions of those ads that are more likely to work—headlines, copy, and visuals—over and over again.
Like having a regulatory expert who has fully memorized the FDA’s adaptive trial design rulebook and can explain what it means for your specific study plan in plain English.
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 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.
This is like giving every customer their own tireless, super-trained support rep who can answer questions, solve common issues, and route complex problems to humans—instantly and at any hour.
Think of an AI shopping assistant as a smart, always-on store associate that lives inside your website or app. It chats with customers, understands what they want (even if they’re vague), recommends the right products, and can walk them all the way through to checkout.
This is like giving every learner their own smart digital tutor that automatically adjusts lessons, exercises, and assessments in real time—based on what the learner already knows, how they respond, and how fast they progress—by coordinating several AI “helper bots” behind the scenes.
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 a smart digital concierge for your buildings. It listens to tenant requests 24/7, routes issues to the right people, predicts what will go wrong before it happens (like a broken elevator), and helps you communicate clearly with tenants so they stay happy and renew their leases.
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.
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.
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 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.
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 your marketing team a super-fast creative assistant that can instantly draft lots of ad ideas and variations tailored to telecom customers, so humans just pick, refine, and approve instead of starting from a blank page.
This is like upgrading from a simple robot that only follows a fixed script to a smart digital teammate that can read documents, understand insurance workflows, and adapt to messy, real‑world cases in claims and policy operations.
This is like giving your fashion design team a very fast, very visual assistant that can turn ideas and references into on-brand designs, concepts, and marketing visuals in minutes instead of days.
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.
Think of this as a tireless junior credit underwriter that can log into systems, pull documents, read them, cross-check rules, and draft decisions on loan applications—then hand them to humans for final approval.