Theory is useful. Seeing it in action is better. These artificial intelligence in marketing examples come from real businesses using real tools. Some are large companies with dedicated teams. Others are small operations running lean. What they have in common is that AI is doing specific, measurable work inside their marketing.
Work through these with an eye for what’s transferable. The specific platform may differ from what you use, but the underlying approach is usually applicable across tools.
1. Netflix: personalised content recommendations
Netflix’s recommendation engine analyses viewing history, search behaviour, ratings, time of day, and even how long someone hovers over a thumbnail before deciding whether to watch. The system then surfaces content that matches that person’s demonstrated preferences.
The business outcome is significant. The platform has estimated that its recommendation system drives the majority of content hours watched. Without it, subscribers would browse longer, find less, and cancel sooner.
The lesson: personalisation isn’t just a nicety. When done well, it directly affects retention. Any business with a catalogue of products, services, or content can apply the same logic at a smaller scale.
2. Spotify: Discover Weekly and AI-driven playlists
Spotify’s Discover Weekly playlist, which refreshes every Monday with 30 songs curated specifically for each user, is one of the most-cited examples of AI personalisation done right. The algorithm analyses listening patterns, skips, saves, and comparative data from other users with similar taste profiles.
What made Discover Weekly work wasn’t just the technology. It was the specific constraint: a weekly playlist feels like a personal recommendation, not a generic shuffle. The AI delivers the music. The product design makes it feel like a personal touch.
The lesson: the way you frame AI personalisation matters as much as the underlying quality of the recommendations.
3. Amazon: dynamic pricing and product recommendations
Amazon updates its pricing on millions of products multiple times per day. AI monitors competitor pricing, demand signals, inventory levels, and buyer behaviour to adjust prices in real time. The recommendation engine, which accounts for a significant percentage of the company’s revenue, surfaces products based on purchase history, browsing behaviour, and what similar buyers purchased.
Both systems run continuously without manual input at the product level.
The lesson: these capabilities are no longer exclusive to Amazon. Tools like Prisync for dynamic pricing and Woocommerce plugins for product recommendations bring the same approach to smaller operations.
4. Sephora: AI-powered virtual try-on
Sephora’s Virtual Artist tool uses augmented reality powered by computer vision to let customers try on makeup products through their phone camera before purchasing. The system maps facial features, applies product colours accurately, and adjusts for lighting conditions.
Return rates on products tried through the virtual tool are lower than the category average. Conversion rates on those sessions are higher.
The lesson: AI in marketing doesn’t always look like an ad campaign. Sometimes it’s a tool that removes a barrier to purchase, and that barrier removal drives revenue.
5. Coca-Cola: AI-generated creative and content
Coca-Cola has used AI tools to generate advertising creative and test visual concepts at a speed that traditional production couldn’t match. In some campaigns, AI-generated imagery was tested alongside traditionally produced creative to identify which concepts resonated before committing to full production.
The approach shifts significant creative testing budget from post-production review to pre-production concept testing.
The lesson: AI creative tools are not replacements for a creative director. They’re rapid prototyping tools that reduce the cost of testing an idea before committing resources to producing it properly.
6. HubSpot: AI-powered lead scoring for B2B companies
HubSpot’s predictive lead scoring tool analyses contact records in a CRM and assigns a likelihood-to-close score based on dozens of factors: company size, industry, job title, email engagement, website visits, content downloads, and more. Sales reps see a prioritised view of their pipeline.
Businesses using predictive scoring consistently report that their reps spend more time on conversations that convert and less time on contacts that go cold.
This is one of the clearest examples of an AI marketing benefit, specifically the value of predictive analytics, showing up in a practical, accessible tool.
7. Starbucks: personalised offers through Deep Brew
Starbucks built an AI system called Deep Brew that personalises offers to each loyalty member based on their order history, location, time of day, local weather, and store inventory. The system determines which products to promote to each individual, when, and through which channel.
The result is offers that feel like the app knows you, not like the app is running a promotion. Purchase frequency among loyalty members has increased alongside app adoption.
The lesson: personalisation at this level doesn’t require building a proprietary AI system. Customer data platforms and marketing automation tools available to mid-size businesses can implement the same logic at a smaller scale.
8. The Washington Post: Heliograf for automated content
The Washington Post developed an in-house AI tool called Heliograf that generates short news reports automatically from structured data. It’s used for election results, sports scores, earnings reports, and other data-driven stories where the structure is predictable and the writing task is repetitive.
Heliograf produces hundreds of articles per year that would otherwise require a journalist’s time to write, freeing those journalists for investigative and long-form work.
The lesson: AI content generation is most valuable for repetitive, structured content where the data is already available. Product descriptions, inventory updates, localised landing pages, and FAQ responses are all categories where this applies beyond newsrooms.
9. Domino’s: AI-powered ordering and predictive preparation
Domino’s uses AI in its ordering system to predict what a returning customer is likely to order, pre-populate their cart, and reduce the friction of reordering. On the operations side, the system uses ordering data to predict demand by store, adjusting preparation schedules before the peak hits.
Marketing and operations share the same data. That means a promotion being pushed through the app is coordinated with preparation capacity, not running ahead of it.
The lesson: AI marketing and AI operations are not separate disciplines. The most effective implementations connect customer-facing activity to the operational systems that fulfil it.
10. Airbnb: dynamic pricing for hosts
Airbnb’s Smart Pricing tool analyses local demand, comparable listing prices, historical booking patterns, seasonal trends, and upcoming events to suggest pricing to hosts. Hosts who use Smart Pricing see higher occupancy rates on average compared to those setting prices manually.
The tool removes the burden of constant price monitoring from hosts and aligns their pricing with real market conditions in real time.
The lesson: AI pricing tools are now available to businesses of almost any size. If you sell anything where demand fluctuates, a dynamic pricing approach is worth investigating.
11. Grammarly: AI writing assistance as a marketing product
Grammarly’s core product is AI that analyses writing in real time and suggests improvements to grammar, clarity, tone, and style. For marketers, the practical application is consistent, on-brand copy across teams without needing a copy editor to review everything manually.
Marketing teams using AI writing assistance report faster review cycles, fewer rounds of revision, and more consistent brand voice across distributed content creators.
The lesson: AI writing tools are not just for generating content from scratch. Their value in editing and maintaining consistency is significant for any team producing content at volume.
12. A small ecommerce brand using Klaviyo’s predictive analytics
Pulling one example from a smaller scale because the examples above skew large. A mid-size ecommerce brand selling home goods used Klaviyo’s predictive analytics to identify customers with a high predicted lifetime value and a 90-day window before their predicted next purchase.
They ran a targeted campaign to that segment with personalised product recommendations based on previous purchases. The campaign generated a 34% higher average order value compared to their standard broadcast campaigns and a significant reduction in the discount depth needed to convert.
The lesson: the same AI capabilities running at Starbucks and Amazon are available through Klaviyo, Mailchimp, and similar platforms at a fraction of the cost. The scale is different. The technology is the same.
What these examples of AI in digital marketing have in common
Looking across all twelve, a few consistent patterns emerge.
- AI is handling the volume and pattern recognition that humans can’t do manually at scale.
- The human role is strategy, creative direction, oversight, and the decisions that require judgment.
- The best implementations connect AI to a specific business problem, not to a general goal of “using AI.”
- Results compound. The longer these systems run, the better they get at predicting and personalising.
If you’re earlier in this journey and want the foundational context before applying any of these approaches, the full breakdown of what AI marketing is and how it works is the right starting point. From there, you’ll be able to map each of these examples to the specific tools and benefits most relevant to your situation.
Craft Tech Media tracks how these tools develop and how businesses of different sizes are implementing them. The examples above will look modest compared to what’s available in two years. Starting now means starting with more runway.
Frequently asked questions
The Klaviyo example at the end is the most directly transferable. Predictive segmentation and personalised email campaigns are available through tools most small ecommerce businesses already use. The approach scales down to lists of a few thousand contacts.
For the majority of tools referenced above, no. Google Smart Bidding, Meta Advantage+, Klaviyo’s predictive analytics, HubSpot’s lead scoring, and Grammarly are all configured through standard interfaces without custom code. Enterprise systems like Starbucks’ Deep Brew are custom-built, but they’re not the only way to achieve personalisation.
Yes. Most of the tools referenced operate globally. Data privacy regulations vary by market, and the Australian Privacy Act imposes specific requirements around how customer data is collected and used. Businesses should check compliance requirements for their region before implementing data-intensive AI marketing tools.
Start with the channel or process causing you the most friction. If email performance is poor, start with AI segmentation and personalisation. If ad spend feels wasteful, start with automated bidding. If lead follow-up is slow, start with AI chatbots or lead scoring. Fix the most painful problem first, then expand.
