How Does AI Help with Personalization?

When we work with teams on websites, shops, or apps today, we often hear the same sentence: "Our users don't find what they need quickly enough." This is rarely just a content issue. It's a relevance issue.

Customers are accustomed to digital interfaces being intuitive, thanks to streaming, platforms, and modern shops. McKinsey describes it clearly: 71% of consumers expect personalized interactions, and 76% are frustrated if they don’t happen. McKinsey (2021)

Simultaneously, companies face increasing pressure: more channels, more content, more touchpoints. Without personalization, it all operates in "scattergun mode" – meaning more scrolling, more searching, more decision fatigue for users.

This is where AI comes into play. Not because it’s "magic," but because it can identify patterns we can’t manually maintain. The trend is clear: In the Twilio Segment Report 2023, 92% of companies report using AI in their personalization efforts. Twilio Segment (2023)

But when almost everyone is "personalized," the quality becomes decisive. This is where it gets exciting. Good personalization feels like an attentive host: it helps without pressuring you. Bad personalization feels like a loud shopping mall: signals everywhere, "more and more." In our projects, a consistent approach has proven effective: Relevance is a service – not a tactic.

Personalization has a dark sibling: the version that exploits your attention rather than supports your goal.

These patterns often emerge in the first analyses: too many pushes, too many "recommended for you" modules, too many variants – and in the end, everything seems arbitrary. Ironically, the mechanism designed to guide causes overload.

This is not just a feeling. In news consumption, we see how quickly overload leads to withdrawal: 39% of users avoid news, and 11% report digital fatigue. Reuters Institute (2023)

In commerce, "more" is not automatically better. Medallia reports that intelligent personalization is viewed positively, while overload significantly increases churn. Medallia (2024)

Here comes our first fresh perspective: Personalization is not "more fitting content," but often "less unnecessary content." If we think of personalization as reduction, a different UX emerges: fewer modules, fewer decisions, less data traffic.

In projects, we use a practical technique called "stoplight personalization." We sort personalization ideas not by coolness but by risk: "Green" are obvious helps (e.g., content based on an explicitly chosen interest). "Yellow" are things to be used sparingly (e.g., ranking in the feed). "Red" are things that undermine autonomy (e.g., aggressive triggers causing impulsive decisions). This simple signal makes discussions concrete – preventing personalization from becoming a distraction machine.

AI can do a lot. But it doesn’t take away your responsibility for which behavior you reward.

When we design personalization intentionally, something changes right from the start: We define success differently.

Many systems are historically trained to maximize clicks, watch time, or cart value. That can work – and simultaneously make a brand “louder” until it doesn't feel like itself anymore. For purpose brands, this is particularly painful: You want to build trust, not buy attention.

Our second fresh perspective is a system goal we articulate concretely in strategy sessions: Time well spent over time spent. It doesn't mean metrics become unimportant. It just means that besides conversion and revenue, you also measure whether personalization relieves: Do users reach their goal faster? Do they search less? Do support inquiries decrease because paths are clearer?

We often use a second, practical method: the "relevance contract." Sounds grand, but it's simple. We write in one sentence what the user gets and what they "pay" for it.

Example: "You get a homepage with topics you really want to read – we use your reading behavior from the last 30 days for this." Once this sentence sounds honest, personalization is usually accepted. If it feels evasive, it’s a signal: Data scope or benefit promise doesn’t fit.

This isn’t idealism. Personalization pays off when it’s experienced as a service: Companies that consistently use personalization achieve significantly higher revenues on average than competitors. McKinsey (2021)

The point is: You don’t have to choose between impact and business. Good personalization is often both because it respects people and thereby makes bonding possible.

When we talk about AI personalization, we quickly arrive at recommendation systems. And the most important question behind it is: “How does the system decide what you see next?”

There are two basic ideas that are easy to remember. The first is content-based: You enjoy reading about circular economy, so the system suggests similar topics. The second is collaborative: People who behave like you also liked X – so X might suit you.

In reality, a third element almost always comes into play: ranking. Imagine a list of 200 potentially appropriate contents. A model sorts them by the likelihood they will be helpful now. It’s powerful because it’s fast – and dangerous if only one signal matters.

In practice, we like to set a small guardrail that has a surprisingly strong effect: Exploration with announcement. Exploration means: The system doesn’t just show the obvious but deliberately throws in the new to prevent you from getting stuck in repetition. Technically, this might be described as “Bandit Logic” or “Serendipity.” For users, it’s simple: “Here’s something you might not know – but it’s near.”

Netflix is a good example of how relevant recommendations can be: About 80% of the content watched is discovered through recommendations. Netflix Insights via MobileSyrup (2017)

At the same time, we see in social feeds how quickly ranking can become a one-way street if diversity is not actively incorporated. Therefore, we recommend optimizing teams for not just “the best result” but also the mix: Familiarity plus surprise, relevance plus choice.

And one detail that’s rarely expressed: Good personalization is not just about algorithms but also design. When you explain “Why do I see this?” it turns a black box into an understandable offering – and a recommendation into a respectful hint.

Personalization is at its strongest when it quietly helps. Not when it’s visible everywhere.

In onboarding, AI can quickly identify which entry won’t overwhelm you. Imagine a learning platform: Those who start confidently get more pace. Those who stumble get smaller steps. An app can work the same way – through initial voluntary interest setup (explicit signal) plus gentle behavioral adjustments.

In content, we often see the greatest benefit in a simple question: “What’s relevant for you today?” A blog that doesn’t give you 20 articles at once but a clear selection saves time. And it saves data. Here, personalization connects with sustainable UX: If fewer unnecessary elements are loaded, unnecessary data traffic decreases – an aspect many competitors completely overlook.

In commerce, recommendations are naturally classics. That they have economic effects is well-documented. A frequently quoted extreme is Amazon: estimates suggest about 35% of revenue is influenced by recommendations. Firney (2025)

Yet, our favorite use case is often support. A personalized help section that remembers which product version you use, what steps you’ve already taken, and which language you prefer reduces frustration. In many products, this is the direct way to fewer tickets and more trust.

Then there’s an underrated area: Personalization against distraction. There are now AI tools that learn work contexts and help meaningfully bundle notifications or protect focus phases. ad hoc news (2024)

When we sum it all up, a guiding principle emerges: Personalization makes sense when it makes the next step easier – not when it just seeks the next click.

Most teams don’t fail at AI, but at starting. Too big in thought, too many data sources, too much tooling – and suddenly nothing happens.

We almost always proceed in small, verifiable steps. If you want to start, this roadmap works well in many contexts:

1) Clarify Goal: What should become easier for users? And what business impact do you expect?

2) Data Check: What signals do you actually have, and which of them are clean, current, and permissible?

3) Build MVP: One location, one use case. For example: personalized homepage or personalized help articles.

4) Measure and Adjust: Not just clicks, but also quality.

For KPIs, we recommend always measuring at least one “feel-good indicator” alongside conversion and revenue: return rate, abandonment, complaint rate, or a short satisfaction question.

Economically, personalization is strong – but only if it doesn’t annoy. Twilio Segment reports that 56% of consumers are more likely to buy again after a personalized shopping experience. Twilio Segment (2023)

And in marketing, we see how impactful small adjustments can be: Segmented and personalized email campaigns were associated with significantly higher revenue. Campaign Monitor (2022)

If you need to sell this internally, an honest calculation example helps rather than big promises: “If we increase conversion by 3%, the tool pays off in X months.” That’s tangible.

And another point we consciously keep in mind in 2026: Performance and sustainability. If personalization leads to fewer irrelevant elements being played out, it can also improve load times and data burdens. It’s not just a “green” idea – it’s often simply better UX.

This turns personalization into a product component that grows, rather than an experiment left somewhere.

Looking ahead, personalization is shifting in two directions: becoming more creative – and simultaneously more cautious.

More creative because generative AI can not only select but also adjust or reformulate content. This can be fantastic if it truly helps users. Imagine a store offering the same product information in different “reading modes”: short, detailed, technical, in simple language. Or a learning platform providing explanations in different examples based on interests.

But here lies a boundary: If generative content serves only to trigger people more, it’s not better personalization – just better distraction. In 2026, the ability is there. The question is the attitude.

The second direction is more cautious: Privacy Tech. We are seeing more approaches that aim to enable personalization without centralizing raw data. Terms like Federated Learning or Differential Privacy are appearing not only in research but in product roadmaps of major platforms. For you as a team, this means it becomes easier to combine personalization and data protection – if you are willing to rethink your architecture accordingly.

Tooling is also progressing. Many personalization and experimentation platforms today connect recommendations, testing, and segmentation. If you want to dive deeper, it's worth looking at tools like Optimizely, Dynamic Yield, or for more technical teams, AWS Personalize.

Our view remains calm: You don’t have to follow every trend. But you should know what direction is possible.

When personalization becomes standard in the coming years, the difference won’t be who “uses AI.” It will be who deploys AI so that people feel understood – yet remain free.