5 Things to Consider Before Adding AI Into Your App

AI is taking the world by storm, it’s the buzzword of the decade, and it’s hard to scroll through LinkedIn or skim a blog without someone shouting about how it’s revolutionising everything. As an app development company we’re seeing clients light up with ideas about weaving AI into their projects. “Can we add it?” “How do we make it amazing?” We’re all about it—AI can transform your app into something extraordinary. But here’s the part not many people are talking about: to make AI truly shine, you’ve got to think through how you use it with intention.

Adding AI isn’t just about jumping on a trend—it’s about unlocking potential the smart way. Here are five things to help get you started on your journey to bring AI into your app.

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1. What’s the Real Value for Your Users?

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First things first: what’s the point? AI isn’t a magic wand you wave to make your app sound cutting-edge. It’s a tool, and like any tool, it’s only as good as the problem it solves. Ask yourself: how will this make your users’ experience better? Are you adding AI because it’s cool, or because it’s solving a pain point they actually care about?

Maybe you’re building a fitness app and AI could personalise workout plans based on user habits. That’s a win—tangible value. But if you’re adding a chatbot just to say “Look, we’ve got AI!” without a clear purpose, you’re wasting time and money. Nail down the why before you even think about the how.

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2. Which Model Fits Your Needs?

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The AI landscape is a jungle. You’ve got big players like OpenAI and Anthropic, open-source options like Llama (by Meta) and DeepSeek, and a dozen others popping up every week. Each has strengths, weaknesses, and quirks. Picking the right one isn’t just about what’s trending—it’s about what aligns with your app’s goals.

Are you processing text? Images? Real-time data? Do you need something lightweight or a heavy hitter? If you’re working with a dev partner (like us!), they should guide you through this maze—comparing options, testing performance, and matching models to your use case. Don’t just grab the shiniest toy off the shelf; do the homework.

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3. Have You Factored in the Costs?

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AI isn’t free—far from it. If you’re using hosted services like OpenAI, you’re paying per API call, and those costs can stack up fast, especially if your app scales. Open-source models might dodge the licensing fees, but they’re not “free” either—you’ll need infrastructure to run them, whether that’s through cloud services like AWS and Azure or your own hardware. And that means ongoing expenses for compute power, storage, and maintenance.

Before you commit, run the numbers. How will this impact your pricing? Your margins? Your runway? We’ve seen companies dive into AI without a cost plan, only to scramble when the bills roll in. Don’t let that be you—budget for it like any other feature.

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4. What About Personalisation and Training?

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Here’s where it gets tricky. If your AI needs to feel tailored—say, adapting to your users’ data or your app’s unique context—you’re not just plugging in a pre-built model and calling it a day. You might need to train it, fine-tune it, or at least feed it the right data to make it useful. That takes time, expertise, and a solid plan.

How will you gather that data? How long will training take? And once it’s live, how will you test and refine it to make sure it’s not spitting out wrong answers or bad data? This is where a good dev partner can save your bacon—helping you scope the effort and execute it right. But even then, you’ll want to understand the process yourself. A successful AI feature isn’t a set-it-and-forget-it deal; it’s an investment.

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5. How Does Security Fit In?

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One more biggie: security. If your app needs to comply with standards like GDPR, HIPAA, or SOC 2 (think healthcare, finance, or anything handling sensitive data), adding AI isn’t just a tech decision—it’s a compliance one. How will it affect what data you can use? Are there restrictions on which providers you can integrate with?

For example, sending user data to a third-party API like OpenAI might clash with privacy regulations, while hosting an open-source model in-house could give you more control—but at the cost of extra security overhead. Map out your requirements early, and double-check with your development team to ensure your AI play doesn’t open up vulnerabilities or legal headaches.

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Wrapping Up: AI Done Right

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AI is incredible. It’s transforming apps, industries, and the way we live—and we’re all in on its potential. But it’s not a one-size-fits-all solution. Adding it without a clear purpose or plan can drain your resources, confuse your users, expose you to risks, and—given the environmental concersn surrounding AI’s massive compute demands—raise some eyebrows about sustainability.

At Morrow, we’ve been quiet on AI not because we’re skeptical, but because we’re thoughtful. When we build with it, we want it to mean something—for your app, your users, and your bottom line. So before you say “Let’s add AI,” take a moment. Think through the value, the models, the costs, the training, and the security. And if you need a partner to talk it over with, you know where to find us.

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