Mentioned in 67 AI use cases across 10 industries
This is about making every shopper’s online store experience feel like a helpful salesperson knows their tastes — showing the right products, offers, and content to each person instead of the same generic website for everyone.
Imagine every shopper in your online store having a smart salesperson who remembers their tastes, budget, and goals, and quietly reorders the search results and product suggestions just for them every time they type in the same vague query like “running shoes.”
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.”
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 giving every potential customer their own personal marketer. Monocle uses AI to tailor messages and offers to each person so emails, ads, and website content feel much more relevant instead of generic.
This is like having an always-on creative studio that can instantly draft ad copy, images, videos, and campaign ideas on demand, then refine them based on performance data.
This is like having an AI co‑developer for video games that can help build game worlds, characters, and logic much faster than a traditional team doing everything by hand.
This is like putting GPS trackers on every marketing touchpoint (ads, emails, events) so you can finally see which ones actually helped move a customer from first click all the way to revenue, not just who happened to be last in line.
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.
This is a forward-looking overview of how AI will change digital marketing—like a roadmap showing how smart tools will increasingly help marketers target the right people, create content, run ads, and measure results with far less manual work.
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.
This is like an always‑on smart salesperson that constantly watches demand, competitors, and stock levels, then automatically adjusts your product prices to hit your goals (more profit, more volume, or both) without a human changing prices all day.
This is like a smart accountant for your marketing budget: it watches all your ads and customer touchpoints and figures out which ones actually convinced people to buy, so you know where your money is really working.
Think of Aidaptive as a digital concierge that quietly watches how every guest shops, browses and books online, then automatically rearranges your website and offers so each visitor sees the rooms, packages and prices they’re most likely to buy—without your team needing to constantly tweak campaigns by hand.
This is like a smart early‑warning system for telecom companies that watches customer behavior and complaints, predicts who is likely to cancel soon, and tells your team exactly which customers to contact and what offers or actions will keep them from leaving.
This is like having a smart digital marketer that studies every customer’s behavior and then automatically decides who should see which message, on which channel, and when—at scale and continuously.
Think of this like an autopilot for your online ads. Instead of humans constantly tweaking budgets, audiences, and creatives, AI watches performance in real time and automatically shifts spend to what works best so you get more sales for every advertising dollar.
Think of these AdTech AI agents as a team of tireless digital interns that understand ads, audiences, and campaign data. You tell them your goals (e.g., ‘get more app installs in Germany within this budget’), and they continuously research options, tweak settings, buy media, test creatives, and report back—without needing a human to click every button in every platform.
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 social media team a smart assistant that studies your followers’ behavior all day, figures out what they like, and then helps you decide what to post, when to post it, and who to show it to so your ads and content work better with less guesswork.
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 your marketing team a super-smart assistant that learns what each customer likes, sends them the right messages at the right time, and does a lot of the routine work for you automatically.
Think of this as giving your marketing team a super-smart assistant that can study what every customer is doing in real time, write tailored messages for them, decide which ad to show where, and keep learning what works so your budget isn’t wasted.
This is like having a digital ad agency in a box: you type what you want to promote, and the AI helps you generate ad creatives, copy, and campaigns in minutes across channels.
Think of this as giving your marketing team a super-fast, super-smart analyst who studies every customer click, email, and ad impression, then quietly tells you: ‘show this group offer A, show that group message B, and stop wasting money on these channels.’
This is like giving your marketing team a super-smart sorting machine. It looks at all your customer data and automatically groups people into smart segments—"likely to buy now", "needs nurturing", "high-value upsell"—so you can send the right message to the right people without guessing.
This is like a smart content clerk that quietly watches what each viewer reads or watches and then rearranges your website or app so everyone sees shows, videos, or articles they’re most likely to click next.
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 a smart in-store salesperson for your online shop that learns what each shopper likes and rearranges the shelves, product suggestions, and emails for every person in real time.
This is like giving every shopper their own smart stylist who has read the entire store catalog, remembers what similar customers liked, and can instantly suggest the right products and bundles in natural language across web, app, email, and chat.
Imagine every shopper on your website having an ultra-knowledgeable personal stylist and product expert who instantly understands what they want, searches your entire catalog, and presents the right items in the right words and images—at scale, 24/7.
Imagine a TV show where many of the sets, background characters, and even some visual effects are created and tweaked in real‑time by a super–smart digital art department instead of huge physical sets and big VFX teams. That’s what the ‘Beta Earth’ AI production phase is about: using AI as a permanent, responsive virtual studio for a TV series.
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.”
This is like a super-smart marketing Rolodex that automatically figures out which customers are most likely to respond to which messages, and then helps you talk to each group differently across ads, email, and your website.
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 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.
This is like an AI-powered creative team that quickly drafts many versions of your ads and helps you pick the ones that are most likely to perform well.
Think of this as a super-analyst that watches every ad impression, every click, and every purchase in real time, then constantly tweaks who sees which ad, on which channel, and at what price to get more results for the same (or less) budget.
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.
Think of this as a super-accountant for your marketing: it watches people’s interactions with your brand across ads, social, search and other touchpoints, then tells you which efforts actually caused sales or sign‑ups so you know what’s working and what to cut.
This is like giving every potential customer a school report card so your sales team knows who’s most likely to buy and should be called first, instead of treating every name on a list the same.
This is like a smart co-pilot for your ad campaigns that constantly watches performance and quietly suggests what to tweak—budget, segments, messaging—while the campaign is still running so you don’t waste money.
Imagine every customer sale is a relay race where many marketing touches (ads, emails, social posts, referrals) pass the baton before someone finally buys. Classic “last-click” gives the medal only to the last runner. An AI attribution model watches the whole race and fairly credits each runner so you know which parts of your marketing truly drive revenue.
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 online store a smart brain that watches how every shopper browses and buys, then quietly adjusts prices, search results, and recommendations so each person sees what they’re most likely to want and buy.
This is like figuring out which players on your sales team actually helped score a goal, not just who made the last kick. Data-driven attribution looks at all your marketing touchpoints (ads, emails, website visits, etc.) and uses statistics to decide how much each one contributed to a sale or conversion.
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.
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.
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 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 having a super-fast digital media trader that watches your Facebook ads 24/7 and automatically shifts budget, bids, and creatives to whatever is working best—without a human needing to click buttons all day.
Think of this like a smart air-traffic controller for your ads. It watches how all your campaigns perform across Google, Meta, TikTok and other channels at once, learns what’s working, and automatically shifts budget, creatives, and targeting to get you more results for the same spend.
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.
Imagine every person watching TV or scrolling online sees an ad that’s been instantly rewritten and re-edited just for them—different script, images, and product angle—created automatically by AI instead of a big creative team doing one version for everyone.
Imagine every visitor walking into your online store and instantly seeing the products, offers, and content most relevant to them—like a smart shop assistant who remembers every past visit, what they liked, ignored, and bought, and rearranges the whole store in real time for that one person.
This is like giving every online shopper their own smart store assistant that instantly knows what they like, what’s in stock, and how to guide them to the right product and offer in real time.
This is about giving every shopper their own ‘personal store window’ online. AI watches what each person browses, buys, clicks and ignores, then rearranges products, offers and content in real time so the site feels like it was built just for that one customer.
This is like a smart shop assistant for an online store that learns what each customer likes and then quietly rearranges the shelves for them—showing different products, bundles, and follow‑up suggestions before and after purchase, even around returns.
Think of this as a smart shop assistant built into your online store that quietly watches what each shopper does and then rearranges the shelves, product lists, and offers in real time so each person sees the items they’re most likely to buy.
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 like giving your online store a smart digital marketer that automatically writes product descriptions, emails, and ads for you so you sell more without hiring a big marketing team.
This is a sandbox where creative teams can quickly test how ElevenLabs’ AI voices and audio tools work with images and videos before putting them into real campaigns or products.
Think of this as a smart store clerk who quietly watches what each shopper likes, remembers their habits, and then rearranges the shelves and offers just for that person in real time—across websites, apps, emails, and ads.
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 map of all the ways online stores are using AI today—like a guidebook that explains how Amazon‑style recommendations, smart pricing, chatbots, and fraud checks actually work and where they’re going next.
Think of your marketing like a relay race where several runners (ads, emails, social posts, etc.) help score a sale. Data‑driven attribution models use statistics and AI to figure out which runners actually mattered most, instead of just giving all the credit to whoever crossed the finish line last.