How does AI help with personalization?

More content increases the need for relevance

When we work with teams on websites, shops, or apps today, we often hear the same sentence: “Our users can’t find what they need quickly enough.” This is rarely just a content problem. It is a relevance problem.

Customers are used to digital interfaces that think along with them, thanks to streaming, platforms, and modern shops. McKinsey describes this very clearly: 71 % of consumers expect personalized interactions, and 76 % are frustrated when they don’t happen. McKinsey (2021)↗

At the same time, pressure on companies is growing: More channels, more content, more touchpoints. Without personalization, everything runs in “scattergun mode” – and for users that means: more scrolling, more searching, more decision fatigue.

This is where AI comes in. Not because it is “magical”, but because it can recognize patterns that we cannot meaningfully maintain manually. The spread of this approach is also evident in adoption: In the Twilio Segment Report 2023, 92 % of companies say they use AI in their personalization efforts. Twilio Segment (2023)↗

But: When almost everyone is “personalized”, quality is what makes the difference. And that is exactly where it gets interesting. Good personalization feels like an attentive host: It helps you without pressuring you. Bad personalization feels like a noisy shopping mall: signals everywhere, “more of this” everywhere. In our projects, a mindset has therefore proven effective, one that we consistently put first: Relevance is a service – not a tactic.

Optimization can turn attention against people

Personalization has a dark sister: the version that does not support your goal, but exploits your attention.

We often recognize these patterns in the very first analyses: too many pushes, too many “Recommended for you” modules, too many variants – and in the end everything feels arbitrary. Ironically, the very mechanism that is supposed to provide guidance then creates overload.

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

And in commerce, “more” is not automatically better either. Medallia reports that intelligent personalization is rated very positively, while overload significantly increases churn. Medallia (2024)↗

This is where our first fresh perspective comes into play: Personalization is not “more relevant content”, but often “less unnecessary content”. When we think of personalization as reduction, a different UX emerges: fewer modules, fewer decisions, less data traffic.

For this, we use a small, field-tested method in projects that we call “Stoplight Personalization”. We sort personalization ideas not by coolness, but by risk: “Green” are things that clearly help (e.g. content based on an explicitly selected interest). “Yellow” are things that should be used sparingly (e.g. ranking in the feed). “Red” are things that undermine autonomy (e.g. aggressive triggers that push people toward impulsive decisions). This simple traffic-light system suddenly makes discussions very concrete – and prevents personalization from becoming a distraction machine.

Because AI can do a lot. But it does not take responsibility away from you for which behavior you reward.

Success starts with a meaningful goal

When we design personalization consciously, one thing changes right at the beginning: We define success differently.

Many systems have historically been trained to maximize clicks, watch time or cart value. That can work – and at the same time slowly make a brand “louder” until it no longer feels like itself. For Purpose Brands, this is particularly painful: You want to build trust, not buy attention.

Our second fresh perspective is therefore a system goal that we formulate very concretely in strategy sessions: Time well spent instead of Time spent. That does not mean that metrics become unimportant. It only means that alongside conversion and revenue, you also measure whether personalization provides relief: Do users find what they are looking for faster? Do they have to search less? Do support requests go down because the paths are clearer?

We often use a second, tried-and-tested method for this: the “relevance contract”. Sounds grand, but it is simple. We write down in one sentence what the user gets and what they “pay” for it.

Example: “You get a homepage with the topics you really want to read – we use your reading behavior from the last 30 days for this.” As soon as this sentence sounds honest, personalization is usually accepted. As soon as it feels evasive, that is a signal: the scope of the data or the value proposition do not fit.

This is not idealism from a business perspective either. Personalization pays off when it is experienced as a service: Companies that consistently use personalization achieve significantly higher revenues on average than competitors. McKinsey (2021)↗

The point is: You do not have to choose between impact and business. Good personalization is often both – because it respects people and thereby makes loyalty possible.

Personalization is a cycle, not intuition

AI personalization often feels like intuition to users: “Somehow the app knows what I need.” In practice, it is less magic and more a clean cycle of signals, decisions, and feedback.

It starts with data – but not automatically “as much as possible”. In projects, we distinguish between explicit signals (you choose an interest, tick a box, save a list) and implicit signals (you click, scroll, buy, abandon). Explicit signals are often more conducive to trust because they are understandable. Implicit signals are powerful, but more sensitive because they can more quickly look like “observation”.

Then context is added: device, time, perhaps even the channel. Someone who is on the go on mobile needs different answers than someone on desktop. This is exactly where AI helps: It can combine many weak signals and derive a probability of what would be helpful to you right now.

The feedback loop is important. Every recommendation is a hypothesis. Do you respond to it – or ignore it – the system learns. This learning process makes personalization more precise over time, but it also carries a risk: If the system learns only from “clicks”, it quickly optimizes toward stimulation and repetition.

That is why our third fresh perspective is: Don’t let AI learn only from reactions, but from satisfaction. That sounds abstract, but it becomes concrete when you add “counter-signals” alongside clicks: “Show less”, “Too frequent”, “Not relevant”. And when you understand personalization not as a continuous run, but as a dialogue.

This is especially crucial for inclusive experiences. A user with a screen reader setup has different needs than someone without assistive technology. Personalization can help remove barriers here – but only if you do not misinterpret signals and give users genuine control.

If you set this up properly, you get an effect we see again and again: users do not feel “tracked”, but supported – because they understand which signals they are giving and what results from them.

Every system balances similarity and discovery

When we talk about AI personalization, we quickly end up with recommendation systems. And the most important question behind them 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 like reading about the circular economy, so the system suggests similar topics. The second is collaborative: People who behave similarly to you also liked X – so X might be a good fit for you.

In reality, a third element almost always comes into play: ranking. Imagine a list of 200 potentially suitable pieces of content. A model sorts them by the probability that they will be helpful right now. This is powerful because it is fast – and dangerous if only one signal counts.

This is where, in practice, we like to put in a small guardrail that has a surprisingly strong effect: Exploration with a heads-up. Exploration means: The system does not only show the obvious, but deliberately mixes in new things so that you do not get stuck in repetition. Technically, this can be described as “bandit logic” or “serendipity”. For users, it is simple: “Here is also something you do not know yet – but that is close to your interests.”

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

At the same time, we see in social feeds how quickly ranking can turn into a one-way street when diversity is not actively built in. That is why we recommend that teams optimize not only for “the best result”, but also for the mix: familiarity plus surprise, relevance plus freedom of choice.

And one more detail that is rarely said out loud: Good personalization is not just an algorithm, but also design. If you explain “Why am I seeing this?”, a black box becomes an understandable offering – and a recommendation becomes a respectful suggestion.

The best help barely feels like personalization

Personalization is strongest when it helps quietly. Not when it is visible everywhere.

In onboarding, for example, AI can quickly figure out which starting point does not overwhelm you. Imagine a learning platform: Those who start confidently get more pace. Those who struggle get smaller steps. The same can work in an app – for example through an initial, voluntary interest setup (explicit signal) plus a cautious adjustment based on behavior.

With content, we often see the greatest benefit in a simple question: “What is relevant to you today?” A blog that does not give you 20 articles at once, but instead a clear selection, saves time. And it saves data. This is exactly where personalization connects with sustainable UX: When fewer unnecessary elements are loaded, unnecessary data traffic also decreases – an aspect that many competitors completely overlook.

In commerce, recommendations are of course classics. That they have an economic impact is well documented. An often-cited extreme is Amazon: Estimates suggest that around 35 % of revenue is influenced by recommendations. Firney (2025)↗

Nevertheless, our favorite use case is often support. A personalized help area that remembers which product version you use, which steps you have already taken and which language you prefer reduces frustration. In many products, this is the direct path to fewer tickets and more trust.

And then there is another underestimated area: personalization against distraction. There are now AI tools that learn work contexts and help bundle notifications sensibly or protect focus periods. ad hoc news (2024)↗

When we put all of this together, a guiding principle emerges: Personalization is useful when it makes the next step easier – not when it is only looking for the next click.

Past data can reinforce old inequalities

AI learns from data. And data does not tell the truth – it tells the past.

That is the core of bias. If certain groups click, buy or are even recorded less often, the system learns: “Show them less of it.” This can feel like relevance, but sometimes it is simply a reflection of inequality. And it can lead to filter bubbles: Those who liked X once keep getting more and more X – until new things hardly get a chance.

Dark patterns are part of this too. Not because AI automatically manipulates, but because teams sometimes set the wrong goals. If the system is optimized only for short-term signals, these typical patterns emerge: too many reminders, artificial urgency, an endless feed.

That’s why we work with three guardrails that work in almost every product:

1) Frequency Capping: Personalization has a dose. When notifications are personalized, we limit their frequency and don’t repeat the same thing endlessly.

2) Diversity by Design: We deliberately build in diversity. Not as a coincidence, but as a rule: alongside what fits, also include what is close-but-new.

3) Make user control visible: A “Show me less of this” is not a nice-to-have, but a safety valve.

This not only feels more ethically sound, but also strengthens the brand. Because users notice whether a system takes them seriously.

And it fits with what we fundamentally pursue at Pola in the digital space: access for everyone, inclusion as a driver, and a calm UX that doesn’t rely on tricks. Personalization is not a special topic here – it is simply another place where it becomes clear whether a brand truly lives its values.

If you take this to heart, a nice side effect emerges: personalization is no longer perceived as an “algorithm”, but as a form of care.

Value must remain understandable and controllable at all times

Trust is the currency of every personalization effort. And trust doesn’t arise from “we’ve got this”, but from clarity.

Many companies experience a paradox: On the one hand, people are willing to share data in exchange for value. Accenture found that 83 % of consumers would share personal data to get a personalized experience. Accenture (2018)↗

On the other hand, basic trust is low. A 2025 overview describes that only 37 % of customers trust companies to handle their data. Waves and Algorithms (2025)↗

For us, this does not lead to a “Then better not at all”, but to a very concrete design principle: Data minimization with visible explanation. You don’t need every signal. You need the smallest amount of data that truly enables your value.

In practice, this means setting up consent properly, offering genuine choices, and making personalization explainable. The sentence “Because you did X, we’re showing you Y” is a small UX building block with a big impact.

If you additionally offer control (“edit interests”, “turn off personalization”, “don’t show again”), personalization becomes an opt-in service rather than a feeling of surveillance. That also supports the logic of trust: transparency about data use can significantly increase acceptance. Waves and Algorithms (2025)↗

And yes, in 2026 this is also no minor matter from a regulatory perspective. GDPR remains the framework, and with the developments surrounding the EU AI Act, the trend is clear: systems need to become more explainable, better documented, and more accountable.

Our practical conclusion: Data protection is not the brake on personalization. It is the condition that makes personalization work in the long term – and fit the brand.

A small use case is the better starting point

Most teams do not fail because of AI, but because of how they get started. Thinking too big, too many data sources, too much tooling – and suddenly nothing happens at all.

That is why we almost always proceed in small, verifiable steps. If you want to get started, this roadmap works well in many contexts:

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

2) Data check: Which signals do you really have, and which of them are clean, up to date, and permissible?

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

4) Measure and adjust: Not just clicks, but quality too.

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

Because personalization is economically powerful – but only if it does not annoy people. 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 strongly small adjustments can work: segmented and personalized email campaigns have been associated with significantly higher revenue. Campaign Monitor (2022)↗

If you have to sell this internally, an honest calculation is more helpful than big promises: “If we increase conversion by 3 %, the tool will pay for itself in X months.” That is tangible.

And one more point that we are consciously keeping in mind in 2026: performance and sustainability. If personalization means that you serve fewer irrelevant elements, it can also improve load times and reduce data load. This is not just a “green” idea – it is often simply better UX.

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

Content becomes more flexible, data processing more cautious

When we look ahead, personalization is currently changing in two directions: It is becoming more creative – and at the same time more cautious.

More creative, because generative AI can not only select content, but also adapt or reformulate it. That can be great when it genuinely helps the user. Imagine a shop that can offer the same product information in different “reading modes”: short, detailed, technical, in plain language. Or a learning platform that provides explanations using different examples, depending on what you are interested in.

But this is also exactly where a limit lies: If generative content is only used to trigger people more and more, that is not better personalization – just better distraction. In 2026, the capability is there. The question is the attitude.

The second direction is more cautious: Privacy Tech. We are seeing more and more approaches designed to enable personalization without centrally collecting raw data. Terms like Federated Learning or Differential Privacy are no longer found only in research, but also in the product roadmaps of major platforms. For you as a team, this means: It is becoming easier to bring personalization and data protection together – if you are willing to design your architecture accordingly.

Tooling is also evolving. Many personalization and experimentation platforms now combine recommendations, testing and segmentation. If you want to dive deeper, it is worth taking a look at tools like Optimizely, Dynamic Yield or for more technically oriented teams at AWS Personalize.

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

If personalization becomes the standard in the coming years, the difference will not be who “uses AI”. Rather, it will be who uses AI in such a way that people feel understood – and still remain free.