What Should Your App Do Before AI Takes Over Personalisation?

A popular mobile game launched last year with basic user recommendations—simple stuff like "players who bought this character also liked these weapons." Nothing fancy, just standard filtering. Fast forward twelve months and they're now using machine learning to predict which players are about to quit, automatically adjusting difficulty levels in real-time, and sending personalised push notifications that have tripled their retention rates. The difference? They spent those early months building the right foundation instead of jumping straight into complex AI systems.

Here's the thing about AI in mobile apps—everyone's talking about it, but most apps aren't ready for it. I mean, genuinely ready. You can't just flip a switch and suddenly have Netflix-level personalisation. The apps that will succeed with AI personalisation are the ones preparing their groundwork right now, whilst their competitors are still figuring out basic user analytics.

The mobile app world is moving fast towards AI-driven experiences, but there's a massive gap between wanting personalised features and actually being able to deliver them. Your app needs clean data, proper user profiles, solid technical infrastructure, and—this is the bit most people miss—users who trust you enough to engage with personalised content.

The best time to prepare for AI personalisation was two years ago. The second best time is right now, before your competitors get there first.

This guide covers the practical steps your app needs to take before AI can work its magic. We're talking about data foundations, user experience cleanup, analytics setup, and all the behind-the-scenes work that makes AI personalisation actually possible. Because honestly? The apps that nail this preparation phase will have a massive advantage when AI personalisation becomes table stakes in mobile development.

Build Your Data Foundation

Right, let's talk about something that genuinely keeps me up at night—well, not literally, but you know what I mean. Your data foundation is probably the most boring topic I'll cover in this guide, but its also the most important. Without proper data collection and storage, any AI personalisation you add later will be like building a house on quicksand.

I've seen too many apps try to bolt on personalisation features after launch, only to realise they haven't been collecting the right information. Or worse, they've been collecting loads of data but storing it in such a mess that nobody can make sense of it. It's honestly a bit mad how many businesses I work with have this problem.

Here's what you need to get right from day one. First, decide what user actions actually matter for your app's purpose. Don't just track everything because you can—that leads to data bloat and privacy headaches. Focus on behaviours that directly relate to your user's goals and your business objectives.

Core Data Points to Track

• User preferences and settings changes
• Content interaction patterns (what they tap, swipe, or spend time viewing)
• Feature usage frequency and duration
• Drop-off points in key user journeys
• Search queries and filter selections
• Time-based usage patterns

The trick is making sure this data is clean, consistent, and properly structured from the start. I always tell clients to think of their data like ingredients for cooking—if you start with rubbish ingredients, even the best chef can't make a decent meal. Same principle applies here; messy data means even the smartest AI won't be able to personalise your app effectively.

Clean Up Your User Experience

Here's something I've noticed after building hundreds of apps—most developers get so excited about adding AI features that they forget to fix the basic problems users are already struggling with. It's like putting a fancy sound system in a car that won't start properly! Your app's user experience needs to be rock solid before AI can work its magic.

Think about it this way: AI personalisation is meant to make things easier and more relevant for your users. But if your app is already confusing, slow, or frustrating to use, AI will just personalise a bad experience. That's not going to help anyone, is it?

Start by looking at your user journey with fresh eyes. Where do people get stuck? What features do they struggle to find? I always tell my clients to sit down and actually use their own app like a first-time user would. You'd be surprised how many issues you'll spot that you never noticed before.

Pay particular attention to your onboarding flow—that's where you'll lose most people if it's not working well. Remove unnecessary steps, simplify your sign-up process, and make sure users understand what your app does within the first 30 seconds. AI can't fix confusion; it can only work with clear user intentions.

Record yourself using your app for the first time (or ask someone else to). Watch where you hesitate or feel confused—those are the areas that need fixing before AI can help.

Also, clean up those navigation menus and button placements. If users can't easily get to the features they want, AI won't have enough interaction data to learn from. It's all connected, and getting the basics right will make your future AI implementation much more effective.

Set Up Proper Analytics

Right, let's talk about analytics—because honestly, flying blind in app development is like trying to navigate London without a map. You'll get somewhere, but probably not where you wanted to go! I've seen too many brilliant apps fail simply because their creators had no idea what users were actually doing inside their product.

The thing is, most people think analytics means just tracking downloads and maybe some basic usage stats. That's like measuring a car's performance by only checking if the engine starts. You need to dig deeper—much deeper—if you want to understand how people really interact with your app.

What You Actually Need to Track

• User journey mapping - where do people get stuck or drop off?
• Feature usage patterns - what gets used, what gets ignored?
• Session duration and frequency - are people coming back?
• Conversion funnels - from download to key actions
• Error rates and crash reports - what's breaking the experience?
• User segmentation data - different groups behave differently

Here's what I always tell clients: set up your analytics before you launch, not after. I can't count how many times I've had to deliver the bad news that we can't retrospectively track something that happened last month. The data just isn't there.

But here's the kicker—don't just collect data for the sake of it. Every metric you track should answer a specific question about your users or your business. Otherwise you'll end up drowning in numbers that don't actually help you make better decisions. Focus on the metrics that directly impact your app's success, and you'll be amazed how much clearer your path forward becomes.

Create Consistent User Profiles

Right, let's talk about user profiles — and I mean proper ones, not just a name and email address thrown into a database. After working with hundreds of apps over the years, I can tell you that most developers get this completely wrong. They either collect too little data or they go mad collecting everything under the sun without any real plan.

The key is consistency. Every user interaction should contribute to building a clearer picture of who that person is and what they actually want from your app. I'm not talking about being creepy here — I'm talking about being helpful. When someone opens your fitness app at 6am every Tuesday, that's valuable information. When they skip workouts during school holidays, that tells you something too.

Start With Behaviour, Not Demographics

Here's where most people mess up — they focus on age, gender, location and think they've got a user profile sorted. But honestly? A 25-year-old night shift worker has more in common with a 45-year-old insomniac than they do with other 25-year-olds who work 9-to-5 jobs. Its about patterns of behaviour, not tick boxes on a form.

The most successful apps I've built track micro-behaviours rather than macro-demographics — how long someone hesitates before making a choice tells you more than their postcode ever will

Track things like session duration, feature usage frequency, time of day preferences, and response patterns to notifications. Build profiles around what people do, not who they say they are. This gives AI something real to work with when it comes to personalisation — actual behavioural data that predicts future actions rather than assumptions based on age brackets.

I've seen too many apps collect data and then... well, nothing. They just sit on it like they're hoarding treasure that'll never see daylight. But here's the thing—data without feedback loops is basically useless. You need to create systems that actually respond to what users are telling you through their behaviour.

Think of feedback loops as conversations with your users. They do something in your app, your app learns from it, then responds with something better next time. Simple in theory, bloody complicated in practice if you don't set it up properly from the start.

Establish Feedback Loops

The most effective feedback loops I've built over the years fall into three categories. First, you've got immediate responses—like when a user skips a certain type of content, your app should remember that and show less of it straight away. Second, there are short-term adaptations where the app adjusts its behaviour based on patterns over a few days or weeks. And third, long-term learning that shapes the entire user experience based on months of data.

Building Response Systems

Your app needs to capture both explicit and implicit feedback. Explicit is when users deliberately tell you something—ratings, reviews, survey responses. Implicit is everything else; how long they spend on different screens, what they tap, what they ignore, when they close the app.

• Track user preferences through their actual choices, not just what they say they want
• Create simple rating systems for content or features
• Monitor engagement patterns to understand what's working
• Set up A/B testing frameworks to validate changes
• Build notification preference systems based on response rates

The key is making sure these feedback loops actually influence something. There's no point collecting user preferences if your app doesn't change based on them. When AI personalisation becomes standard, you'll already have the infrastructure to feed it meaningful data about what your users genuinely want.

Design for Data Collection

Right, let's talk about something that makes or breaks AI preparation—how you actually collect data from your users. I've seen too many apps where data collection feels like an afterthought, bolted on when someone realises they need user information for personalisation. That's doing it backwards, honestly.

Your app needs to be designed from the ground up with data collection in mind. But here's the thing—users hate feeling like they're being interrogated every time they open your app. The trick is making data collection feel natural, like part of the experience rather than a chore.

Think about how Netflix does it. They don't pop up endless surveys asking what you like; instead, they watch what you actually do. How long you watch something, when you pause, what you skip—that's all data collection, but it doesn't interrupt your binge-watching session.

Making Data Collection Invisible

The best data collection happens when users dont even notice its happening. Every tap, swipe, and scroll tells you something about their preferences. Every feature they use (or ignore) gives you insight into their needs.

But you also need explicit data sometimes. When you do ask users for information directly, make it worth their while. Explain why you're asking and what benefit they'll get in return.

Design your onboarding to collect the most important data first—the stuff you absolutely need for basic personalisation. You can always ask for more later when users are more invested in your app.

What Data Actually Matters

Not all data is created equal for AI preparation. Focus on collecting information that directly relates to user preferences and behaviour patterns. Here's what you should prioritise:

• User interaction patterns (what they tap, how long they spend on different screens)
• Content preferences (what they save, share, or bookmark)
• Usage timing (when they're most active, how often they return)
• Feature adoption (which tools they use, which they ignore completely)
• Search and filter behaviour (what they're looking for, how they narrow down options)

Remember, good data collection isn't about gathering everything you possibly can—it's about gathering the right things in a way that doesn't annoy your users. Quality beats quantity every time when you're preparing for AI-powered personalisation.

Prepare Your Technical Infrastructure

Right, let's talk about the technical side of things. I know it's not the most exciting part, but getting your infrastructure sorted now will save you from headaches later—trust me on this one. When AI personalisation becomes part of your app, your backend needs to handle way more data processing than it probably does right now.

Your servers need to cope with real-time data analysis, machine learning model execution, and personalised content delivery for thousands of users simultaneously. If your current setup struggles when you get a traffic spike, it's definitely not ready for AI workloads. I've seen apps crash spectacularly because they tried to bolt on personalisation features without upgrading their infrastructure first.

Core Infrastructure Requirements

Here's what you need to focus on:

• Cloud-based scaling solutions that can handle sudden load increases
• Content delivery networks for fast personalised content distribution
• Robust database architecture that supports complex queries
• API rate limiting and caching systems
• Security protocols for handling sensitive user data
• Backup and disaster recovery systems

The good news? You don't need to rebuild everything from scratch. Most modern cloud platforms offer managed services that can handle the heavy lifting. But you do need to plan your architecture properly—randomly adding services as you go is a recipe for disaster.

Start by auditing your current setup. How fast does your app respond during peak usage? Can your database handle complex analytical queries without slowing down user-facing features? These are the questions that'll determine whether you're ready for the next step or if you need some serious backend work first.

Future-Proof Your Content Strategy

Here's the thing about AI personalisation—it needs good content to work with. You can't just throw random blog posts and product descriptions at an AI system and expect magic to happen. I've seen too many apps rush into AI features without having their content house in order, and honestly? It's like trying to cook a five-star meal with ingredients you found at the back of your fridge.

Your content strategy needs to think beyond what users see today. Every piece of content you create should be structured in a way that makes it easy for AI to understand, categorise, and personalise. That means proper tagging, consistent formatting, and metadata that actually makes sense. I mean, if you can't explain what a piece of content is about in simple terms, how do you expect an algorithm to figure it out?

Create Content That Scales

The apps that will win with AI personalisation are the ones building modular content systems right now. Think about it—instead of creating one massive article, break it into smaller, focused pieces that can be mixed and matched based on user preferences. This isn't just about being prepared for AI; it makes your content more flexible and reusable today.

The most successful apps treat their content like building blocks rather than finished monuments

Tag Everything (But Make It Smart)

Start tagging your content with user intent in mind, not just topic categories. What problem does this piece solve? What user type benefits most? What stage of the user journey does it support? These tags become the foundation that AI systems use to make personalisation decisions. And here's a tip from someone who's made this mistake—keep your tagging system simple and consistent. You don't want to end up with 47 different ways to describe the same thing.

Conclusion

Right, so we've covered a lot of ground here—from building your data foundation to preparing your technical infrastructure. And honestly? If you implement even half of these recommendations, you'll be miles ahead of most apps when AI personalisation becomes the standard (which, let's face it, is happening faster than most people think).

The thing is, I've seen too many app owners wait until it's too late. They spend months trying to retrofit their apps with proper analytics, scrambling to collect the user data they should have been gathering from day one. It's a bit mad really, because the groundwork we've talked about isn't just about preparing for AI—it makes your app better right now.

Clean user experiences, proper analytics, consistent user profiles... these things improve your retention rates today. They help you understand why users are leaving your app, what features they actually use, and how to make their experience better. The AI part is just the cherry on top.

I mean, you don't have to implement everything at once. Start with the basics: get your analytics sorted, clean up your user onboarding, and begin collecting meaningful user data. Then work your way through the technical infrastructure and content strategy pieces.

But here's the thing—don't overthink it. The apps that succeed in the AI era won't be the ones with the most sophisticated algorithms; they'll be the ones that understand their users best. And that understanding starts with the foundation you build today, not the AI you add tomorrow.

Your users are already telling you what they want through their behaviour. The question is: are you listening?