In the early days of digital products, engagement was the one metric to rule them all. Getting enough clicks and time-on-site was more or less a sign that clients are there and watching or using your product. As digital markets mature, user attention becomes hard to predict and direct.
From gaming to fitness to streaming, long-term retention replaces engagement as the most meaningful metric and artificial intelligence catalyzes this shift. Adaptive digital systems respond to both general rhythms and person-specific quirks of human behavior.
As a result, inconsistencies are smoothed out, and here comes habitual use. Let’s unpack some examples of how AI helps brands to become daily routine.
Attention Versus Habit
Attention is an ephemeral unit. Neuroscientists have mapped how it spikes easily – and drops off with equal speed. Unfortunately for marketers and product designers, this is the law for various digital categories. Apparently, grabbing attention has never been enough: sustainable engagement requires deeper anchors.
Take digital health apps as an example: 3.3% retention after 30 days is rather common in the industry. Fitness apps struggle to engage users beyond the first few weeks, too. Gaming apps are often considered as the ‘sticky’ benchmark – however, for many game titles, fewer than 10% of users return after 30 days.
At the same time, Duolingo reports a monthly retention rate of about 55%. How come their retention rate is so much higher than average for learning apps (2-3% after 30 days)? The answer is habit formation.
While enjoyment and novelty are amazing for grabbing initial attention, it’s regularity combined with satisfying outcomes that really forms habit loops. Users drift away or move to the next big thing when the interface sparks attention and fails to deliver on habit-building. Besides, who doesn’t want their digital product to become a part of someone’s automatic neural pathways?
Especially mature digital markets are facing this challenge in 2026. The proliferation of platforms of all kinds has shortened average subscription tenures, even though users are generally content with their experience of each particular product. Having to juggle multiple subscriptions means users often churn (just to enjoy some time with your direct competitor and come back in a year). This makes short-term engagement metrics like session time way less insightful: they’re quite useless when one needs to project long-term user loyalty.
From Recommendations to Routines: Fostering Daily Habits with AI
To get back to the Duolingo example: the app is rather explicitly designed for retention. The interface is focused on mechanisms like adaptive difficulty and streaks. During each session, users get small rewards for successes and, of course, consistency. Hence, senses of progress and commitment are reinforced.
The famous streak counter works precisely because it capitalizes on loss aversion: people want to protect what they’ve already built, and this protectiveness nudges them to come back again and again.
The role of AI in the system is to predict when a user is at risk of dropping off. Then, personalized nudges are set in motion: for someone, a review session is a good comeback, while adjusted challenges keep somebody else going.
Yet another enemy of habit is cognitive overload. When users need to make too many choices, they move on to another (simpler) interface. Both Netflix and Spotify use AI to curate highly personalized recommendation lists and thus reduce the cognitive burden of choice. Personalized playlists and the feature of elaborating playlists with music ‘similar to what you like’ are how Spotify keeps user experience both fresh and free of choice overload. Result? Both Netflix and Spotify are well-anchored in users’ daily routines.
For fitness and wellness tech, the main danger lies in occasional peaks of activity followed by long periods of disengagement. Apple’s Fitness+ and Peloton both use AI to emphasize consistency (daily or regular engagement) over sporadic high-effort sessions. Apple has integrated activity rings, haptic reminders and cross-device tracking – all to make little wins a part of a user’s daily experience.
Peloton, on the other hand, actively implores social and emotional incentives for regular use. In both cases, AI powers schedule recommendations, personalized suggestions, and motivational nudges based on past behavior.
Collaborations: From Visibility to Behavioral Infrastructure
For non-digital brands, partnerships with AI-first solutions are valuable to build retention, too. Take an example of Paris Saint-Germain and Zing Coach collaboration. AI-powered fitness routines inspired by PSG players were integrated directly into Zing’s adaptive training platform. Users get to follow workout plans inspired by their favorite players. Along the way, AI tracks their progress, tailors challenges to user-specific needs, and provides exclusive experiences and rewards.
As a result, PSG builds stronger connections with its fanbase through daily engagement at the level of workout routine. Brand visibility grows beyond matchdays, and a constant positive association is imprinted in fans’ brains. Besides, users feel more engaged and less like distant spectators: after all, they are training with their favourite football stars. This sense of belonging increases retention for a non-digital brand, too.
Next to AI, the secret retention ingredient is emotional affinity. Football fans’ attachment to their idols helps them to return daily to the fitness app, which, in turn, delivers a highly-relevant experience. This kind of collaboration hints at a new era of brand partnerships built on shared behavioural systems rather than shared billboards.
Obviously, use of AI in retention-oriented strategies differs by industry. Still, the underlying principle remains the same: adapting the whole system to each user reduces cognitive friction and helps build sustainable habits.
How AI-Driven Adaptation Contributes to Retention
Alex Kurov, Chief Product Officer and Co-Founder at Zing Coach, knows firsthand how designing with AI from the very beginning helps to absorb human inconsistency.
“Keeping retention healthy goes beyond locking users in the product for longer. Actual behavior and motivation are always messy; thus, you can’t build a one-size-fits-all solution. Artificial intelligence allows products to adapt to fluctuating motivations, ever-changing context and new goals.
In fact, AI makes digital experiences move in tandem with the rhythms of real life. A personalized cue, a task tailored individually, or a meaningful reward structure resonates better than motivational speeches targeted at everyone and no one in particular.”
The better a habit integrates into one’s life pattern, the surer users’ retention. AI makes this magic scalable. Machine learning and behavioral models personalize user experiences, both in here and now and over time. Recent academic work demonstrates that AI recommendation systems improve retention by stratifying users with accurately selecting personalized responses grounded in long-term behaviors.
What’s key here are attention to nuanced long-term preferences and facilitating a dynamic interplay between what a user likes and what they expect from your system. Traditional metrics like session length simply don’t give you any insights or control in this area.
All things considered, a deeper shift emerges: AI is moving digital products from being reactive to proactive anticipation and sustaining usage patterns.
From Flickers of Attention to Everyday Rituals
Digital attention is not what it was back when Facebook introduced its ads: anyone with a few dollars can get views, clicks, and first-time users. Retention, on the other hand, becomes more rare and valuable. AI helps brands to personalize experiences, adapt to individual users, and scale all that.
The most successful products of tomorrow will meet them where they are, and become a part of their daily habits. In a world with endless digital choices, habitual use separates brands that survive from those that fade with the endless scroll.
