Mentioned in 81 AI use cases across 17 industries
Think of AI in clinical trials as a super-organized, tireless assistant that helps pick the right patients, watch over their health data in real time, and flag risks or results much faster than humans going through spreadsheets and reports.
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.
This is like a smart assistant that reads a patient’s electronic medical record and quietly taps the doctor on the shoulder to say, “Based on all this history and lab data, this patient looks like they’re at high risk for X in the next few hours—here’s why and what to watch out for.”
This is like giving drug development teams a super-smart assistant that can read piles of medical data, predict which patients and trial designs will work best, and continuously monitor results so trials finish faster and with fewer costly mistakes.
This is like giving fraud investigators a super-smart digital assistant that can scan huge amounts of payments, claims, and case files in real time and yell “this looks suspicious” long before a human could spot the pattern.
This is like having a smart weather forecast, but for your store’s inventory. It looks at your past sales, seasons, promotions, and other patterns to predict how many units of each product you’ll need in the future so you don’t run out or overstock.
This is like giving the power grid a very smart weather forecast, but instead of predicting rain, it predicts how much electricity people will use so green energy sources can be used more efficiently.
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 having a warning light on your customer base: it looks at past customer behavior and contracts and predicts who is likely to cancel their phone/internet service soon, so you can reach out before they leave.
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.
Kayros is like a super-smart air traffic controller for your factory. It constantly looks at all your machines, orders, and constraints, then automatically figures out the best possible production plan and schedule—and keeps adjusting it when things change.
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 a smart crystal ball for retailers that predicts how much of each product customers will buy, and helps you align inventory and supply so shelves are stocked without over-ordering.
This is like having a super-accurate weather forecast, but for customer demand and store inventory: it predicts what products you’ll sell and tells you how much to stock and where, so shelves are full when customers arrive without overfilling the warehouse.
This is like having an early-warning radar for unhappy phone or internet customers. The AI watches usage and support patterns and raises a flag when someone looks likely to cancel, so your team can reach out before they actually leave.
Think of this as a smart watchdog for banks: it constantly watches transactions and customer behavior, learns what “normal” looks like, and then flags suspicious activity that could be money laundering or fraud—much more accurately than old rules-based systems.
Imagine watching all the money movements in a bank as if they were a big social network: people and companies are dots, and payments are lines between them. This system uses AI to spot unusual and suspicious patterns in that network—like circles of accounts passing money around in strange ways—so compliance teams can catch money laundering much faster and with fewer false alarms.
This is like having a super-smart store manager who can look at all your sales, seasons, and trends at once and then tell you exactly how much of each product to order, where to put it, and when to move it, so you never run out or overstock.
This is like an early‑warning system for phone and internet providers: it studies past customers who left and learns patterns so it can flag which current customers are most likely to cancel soon, giving the company time to intervene with offers or service improvements.
This is like an air-traffic control tower for hospitals that uses AI to watch every bed, patient movement, and bottleneck in real time, then recommends what to do next so patients don’t sit waiting in hallways or ERs.
This is like giving a hotel’s pricing team a super-calculator that constantly studies demand, competitors, and guest behavior to suggest the best room rates and offers every day, automatically.
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 it as a super-fast digital recruiter that scans huge piles of resumes and job descriptions in seconds, shortlists the best matches, and keeps candidates moving quickly through the hiring process instead of getting stuck in inboxes and spreadsheets.
Imagine every recruiter has a super-smart digital assistant that never sleeps, scans thousands of resumes in minutes, talks to candidates, and keeps hiring managers updated automatically. That’s what AI recruiting agents do for the hiring process.
Think of Findem as a supercharged hiring detective that scans millions of public signals about people and turns them into a short, qualified list of candidates who actually match what you need—skills, experience, and diversity goals—before your recruiters ever start manual sourcing.
This is like giving your HR system a smart assistant that can scan resumes, predict staffing needs, flag potential retention risks, and recommend the right people for the right roles, instead of HR teams doing all of that manually in spreadsheets and emails.
Think of this as turning your company’s HR data into a ‘smart advisor’ that spots patterns in hiring, performance, and turnover so leaders can make better people decisions instead of guessing from spreadsheets.
This is like giving your supply chain a smart GPS and weather system that constantly looks ahead, finds the fastest and safest routes for parts and materials, and automatically reroutes when there’s a disruption (factory shutdown, port delay, raw‑material shortage).
This is describing a ‘smart HR department in a box’ that uses AI and data analytics to sift CVs, predict employee issues, and automate routine HR work so people leaders can focus on people instead of paperwork.
This is like giving your factory a smart air-traffic controller that constantly looks at all your machines, workers, and orders, then automatically decides the best sequence of jobs so everything ships on time with minimal idle time and overtime.
This is like giving your store a very smart assistant that looks at past sales, seasons, and trends to guess how much of each product you’ll need—and then keeps adjusting that guess every day so you don’t run out or overstock.
This is like a smart autopilot for hotel pricing. It watches bookings, market demand, and competitors’ prices, then automatically suggests or sets the best room rates to fill more rooms at higher profit.
This is like a weather forecast, but for store sales: it uses past sales data and patterns (seasonality, holidays, promotions) to predict how much you’ll sell in the future at each store or channel.
Think of this as a super-smart billboard system that doesn’t track who you are, but instead reads the page you’re on in real time and shows an ad that fits the exact topic, tone, and situation of that content.
This is like giving every salesperson a smart digital assistant that lives inside Microsoft Dynamics 365. It watches deals, emails, and tasks, then proactively suggests next steps, drafts outreach, and updates CRM records for them.
This is like giving your planning team a super-calculator that looks at years of sales, promotions, seasons, and external events to predict how much customers will buy next week, next month, and next season—far more accurately than traditional spreadsheets.
This is like having a smart early-warning system that spots which mobile or internet customers are about to leave and suggests the best way to keep them—before they call to cancel.
This is like giving a hotel restaurant a smart co‑pilot that watches sales, inventory, and guest behavior, then quietly advises what to serve, how much to buy, and when to promote things to make more money and waste less food.
This is like giving a hotel a super-smart digital revenue manager and marketing analyst that never sleeps. It watches demand, prices, competitors, and your own website traffic in real time, then tells you what to charge, where to sell, and how to get more guests to book directly instead of through expensive online travel agencies (OTAs).
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.
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 turning a manual, paper-heavy hiring process into a smart filter and assistant that helps recruiters scan resumes, rank candidates, and communicate faster, while also flagging potential bias or legal issues.
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.
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 this as a smart co‑pilot for nurses: it watches patient data, compares it to what’s happened with thousands of similar patients before, and then suggests what to watch out for and what actions might be needed—while the nurse stays in full control.
This is like a smart crystal ball for retailers: it looks at your past sales, promotions, seasons, and external factors, then predicts how much of each product you’ll need in the future so you don’t run out or overstock.
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.
This is like an early-warning radar for employee resignations. It looks at patterns in engagement, feedback, and HR data to flag people or teams that are likely to quit soon so you can intervene before you lose them.
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.
Think of AI in clinical trials as an ultra-fast, tireless research assistant that helps pharma teams find the right patients, design better studies, monitor participants in real time, and clean up data much faster than humans alone—so new drugs get to patients sooner.
Think of this as turning drug development into a ‘smart factory’ where AI helps pick the right patients, design better trials, and spot problems earlier—so medicines get to the right people faster and cheaper.
Think of an AI ATS like a very fast, tireless recruiting assistant that reads every resume, ranks candidates, writes outreach messages, and keeps applicants moving through the hiring pipeline automatically, instead of recruiters doing it all by hand.
This is about turning your CRM (Microsoft Dynamics 365) into a smart sales assistant that watches all your customer data, predicts who to talk to next, and automates routine follow‑ups so your sales team can focus on closing deals instead of clicking buttons.
Think of Trial IntelX as a GPS and traffic system for clinical trials: it constantly watches where all the trials are, how they’re moving, and where there are bottlenecks, then surfaces that intel so sponsors and CROs can choose better sites, plan faster, and avoid delays.
Imagine a very smart store manager who can see every product in every store and warehouse at once, predict where customers will actually buy it, and quietly shuffle inventory around before shelves go empty or stock piles up in the wrong place.
This is like giving your marketing team a crystal ball that looks at all your past customer and campaign data and says, “If you spend money here, with this message, to this audience, you’re most likely to get results.”
Think of it as a super-fast reader that scans millions of web pages and figures out what each page is really about – not just the words on it, but the meaning and mood – so your ads show up in places that actually fit your brand and audience.
This is like a supercharged planning sandbox for delivery routes and vehicle schedules: you can try different ways of assigning trucks and drivers to trips on a computer, see how each plan performs, and then pick the best one before you spend real money on the road.
Think of Salesforce as a digital command center where all your customer information, sales activities, and marketing efforts live in one place — and now it has an AI copilot that recommends who to call next, what to say, and automates a lot of the busywork.
This is like having a very smart auditor that continuously watches tax records, bank-like transaction trails, and filing patterns to spot who might be under-reporting income or committing tax fraud, and then alerts tax officers to investigate those specific cases first.
This is like giving a CPG company a super-analyst that never sleeps: it scans all your sales, pricing, promotions, store, and external data to automatically surface why performance changes, where growth is hiding, and what to do next.
Think of your supply chain planning as flying a modern plane: the AI is the autopilot doing millions of calculations per second, and your planners are the pilots deciding the destination, watching for storms, and overriding when needed. This setup makes planning faster, safer, and more precise than humans or software alone.
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.
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 tool is like an automated marketing analyst that studies all your customer data and groups people into smart, predictive segments so you can send the right message to the right audience at the right time.
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 having a smart digital salesperson for every single shopper that instantly figures out what offer or promotion will convince them to buy right now—based on what they’re doing, what they’ve bought before, and what similar people responded to in the past.
This is like a smart early‑warning system for phone and internet companies: it watches customer behavior, predicts who is likely to cancel soon, and automatically suggests (or triggers) the right offer or outreach to keep them from leaving.
Think of this as a smart engine inside an online store that automatically shows each shopper the most relevant products, content, and offers, based on everything SAP already knows about them and similar customers.
This is about using AI as an always‑on radar and autopilot for the supply chain: it constantly scans for risks (like delays, shortages, demand spikes), predicts problems before they hit, and suggests or triggers responses so the business can keep products flowing to customers.
Think of AI recruitment as a super-fast digital hiring assistant that reads CVs, screens candidates, schedules interviews, and flags the best matches for a role – the way spam filters scan thousands of emails to find the ones you actually want.
This is like giving your marketing team a crystal ball that looks at all the clicks, calls, and purchases your customers made in the past and then guesses what they’re likely to do next, so you can talk to the right people with the right offer at the right time.
This is like giving every shopper their own digital sales associate who remembers what they like, what they looked at before, and what similar customers bought, then uses all that data to tailor offers, messages, and experiences in real time across stores, apps, and websites.
Think of this as a smart digital concierge and revenue manager that quietly watches what guests like, how they book, and how the market moves—then adjusts offers, prices, and messaging in real time so every guest feels known and every room night earns more.
This is like giving your global supply chain a smart GPS and co‑pilot: it constantly looks at all the data (demand, inventory, shipping, risks), simulates options, and recommends the best decisions instead of people doing it all in spreadsheets and emails.
Imagine your retail planning team with a super-analyst who has read every sales report, every inventory file, and every marketing plan you’ve ever had, and can instantly tell you what to buy, how much, where to send it, and when to mark it down. That’s what AI-powered retail planning tools like Toolio aim to do across the full planning calendar.
Imagine giving your fraud investigators a tireless digital assistant that reads billions of transactions, emails, and claims every day, flags anything that “looks off,” and explains why it’s suspicious so humans can step in before the money is gone.
This is like a super-powered hiring and talent map for your company: it scans millions of profiles and your internal data to suggest the best people to hire, promote, or reskill, instead of relying on manual resume review and gut feel.
Imagine your hiring team gets a smart co-pilot that reads every CV, compares it with the job needs, learns what ‘good hires’ looked like in the past, and then brings you a short, high-quality candidate list—while also warning you about possible bias and compliance issues.
Think of Apli as a smart hiring assistant that reads resumes, screens candidates, and moves them through the hiring funnel automatically so your recruiters only focus on the best-fit people.