How to Add AI to Your App

From finance to healthcare and retail to education, AI is transforming apps across industries and quietly reshaping how SaaS products onboard, retain, and delight users. Whether it’s summarizing content, detecting churn risks, or delivering personalized experiences, AI has moved from novelty to necessity in many categories.

Still, with the flood of tools and hype, many founders are asking a more grounded question:

“How do I add AI without wasting time, budget, or user trust?”

This guide is for non-technical SaaS founders who want to integrate AI thoughtfully. You don’t need to reinvent your app or hire a machine learning team from day one. Instead, you’ll learn how to approach AI like any smart product decision with clear goals, user focus, and strategic constraints.

1. Decide If AI Is Right for Your App

Before diving into tools, step back and ask: Will AI truly improve your product or just add complexity? Four things to weigh:

1.1 Strategic Fit

AI should solve real problems, not just check a box. Look for pain points, such as manual processes, repetitive workflows, or low personalization. If AI makes your app faster, smarter, or easier to use, it may be worth exploring.

1.2 Cost and Resources

Even simple AI integrations come with dev time, data needs, and potential API costs. Make sure your team or partner can support it and that there’s a clear ROI.

1.3 Data Readiness

AI runs on good data. Do you have the right type, volume, and quality to support your use case? If not, you may need to solve your data strategy first.

1.4 Privacy and Trust

Will users understand how AI is used? Are you compliant with regulations like GDPR? Ethics and transparency are especially important when features are tied to personal data or decisions.

When you reflect on your current app, you might recognize new areas of opportunity. AI could offer an ideal solution to challenges you've come to accept as unavoidable. Tasks involving analysis, sorting, pattern recognition, and content, for example, are all well-suited to AI.

2. Consider Ways to Use AI in Your App 

If you're having trouble imagining the possibilities, consider these use cases. Here are a few ways AI can enhance your app.

Summarizing Information

AI is great at taking large quantities of data and processing it quickly. 

• Customer reviews and survey responses
• Sales or support call transcripts
• Internal documents or meeting notes

This helps users and teams access insights more quickly without needing to read everything.

Generating Responses

Thanks to generative AI technologies, you can use AI to craft original text. 

• Auto-drafting personalized emails or replies
• Recommending content or actions based on user data
• Powering chat assistants or help desks

These tools not only save time but also scale personalization without requiring a large team.

Categorizing Information

AI can quickly scan and sort inputs, perfect for:

• Tagging support tickets or messages
• Sorting user-uploaded content
• Organizing data into topics, themes, or urgency levels

It brings structure to otherwise unstructured input.

Assess patterns

Another way you might consider deploying AI is for pattern recognition. 

• Unusual usage that could signal fraud
• Drop-off points in user flows
• Opportunities for automation based on frequent actions

This can enhance both security and the overall experience.

Analyze text for content and mood

You can train AI to evaluate how users feel or whether content meets specific standards. Great for:

• Flagging inappropriate or off-brand content
• Delivering mood-based content (e.g. Calm app recommendations)
• Analyzing product reviews to surface key themes

3. Review Examples of Apps that Use AI

When you think of AI and apps, there are probably several that come to mind. ChatGPT, Google Lens, Siri, and Otter AI are examples, just to name a few. There are dozens of other apps that are not explicitly "AI apps" but have integrated AI in other ways. Spark your creativity by reviewing the way these apps integrate AI.

Canva

The user-friendly graphic design app Canva uses AI in a myriad of interesting ways. For one, it leverages DALL·E by OpenAI and Imagen by Google Cloud to let users turn text into an AI-generated image. This feature isn't just a novelty for users. It solves a problem, too, allowing users to find the perfect image to complete their presentation, post, or newsletter.

Use case: AI image generation using DALL·E and Imagen

Purpose: Helps users overcome creative blocks and fill gaps without leaving the platform.

Lesson: AI doesn’t need to be flashy, just useful at a key moment in the flow.

Source: Canva

Amazon

Amazon is on the cutting edge of technology, including using AI in all sorts of new and exciting ways. One visible way is through its AI-generated review summaries. AI processes the reviews and captures key themes and words to make information super skimmable for users. Instead of reading dozens of reviews to decide on a product, shoppers can read a concise sentence or two.

Use case: AI-generated review summaries

Purpose: Streamlines decision-making by summarizing thousands of reviews into key points.

Lesson: Even microcopy can be powered by AI to reduce user friction and boost conversion.

Source: Search Engine Journal

Snapchat

The tech company Snap and its social media app Snapchat are quick to embrace new technology. So naturally, AI is no different. Snapchat's "My AI" feature allows users to converse with AI as if they were talking with a human. The chatbot uses OpenAI's ChatGPT to make social media even more social.

Use case: My AI is a conversational AI chatbot using ChatGPT

Purpose: Adds a playful, personalized layer to user interaction.

Lesson: AI can reinforce a brand's personality and keep users engaged for longer.

Source: CNN

Calm

The popular relaxation and meditation app Calm has also jumped on the AI trend. Using Amazon Personalize, an AI technology, Calm queues up content tailored to its users. Just as the name suggests, the tool helps Calm personalize the app experience. Users who love soundscapes but not bedtime stories, for example, will see more of the former and less of the latter.

Use case: AI-powered personalization using Amazon Personalize

Purpose: Matches content (e.g. soundscapes vs. bedtime stories) to user preferences.

Lesson: Smart curation can improve stickiness, especially in content-rich apps.

Source: AWS

ELSA Speak

The language-learning app ELSA Speak uses AI to help non-native speakers perfect their English speaking. The app uses a speech recognition AI tool to determine how close a user's speech is to the correct pronunciation. Red, yellow, and green indicators and AI-generated tips help users make progress.

Even small companies can learn a thing or two from the way these well-known companies are using AI. Luckily, you don't have to be a tech giant to use AI. Thanks to APIs and open-source technology, nearly anyone can use AI to enhance their app. What’s more, AI-as-a-Service (AIaaS) is also a growing area of development. Cloud-based platforms like Microsoft Azure, Amazon Web Services, and Google Cloud allow smaller teams to implement AI without a heavy lift.

Use case: Speech recognition and real-time pronunciation feedback

Purpose: Breaks down language learning into specific, actionable improvement steps.

Lesson: AI can deliver hyper-targeted coaching, especially in education and training tools.

Source: EdTech Digest

Glasp

A browser-based productivity tool that helps users highlight, save, and summarize content from articles, PDFs, and videos. With AI integrated into the platform, Glasp automatically generates concise summaries and key takeaways from the material users highlight. It also uses natural language processing (NLP) to organize content thematically, making it easier to revisit or share insights.

Use case: AI summary and highlight generation for articles and videos

Purpose: Helps users quickly extract insights from long-form content

Lesson: Startups can use AI to create leverage, even on top of other platforms like YouTube or Medium.

Source: MOGE

4. Set Clear Goals 

Now that you have an idea of how other apps use AI, it's time to zero in on exactly how to put it to work in your own app.  Start by identifying a specific business outcome you want to improve, rather than just adding a feature. Do you want to:

• Boost user retention by improving personalization?
• Reduce support costs by automating ticket handling?
• Accelerate onboarding with intelligent summaries or suggestions?

The more specific you can get, the better. A clear picture of what you want to achieve will inform not only the method of AI integration but also the platform, the team, cost, and timeline.

Plus, when you define what success looks like, you'll be able to set KPIs. Having a clear target will also ensure you don't waste time and valuable dollars on the wrong tools.

As part of this goal definition process, don’t neglect any data considerations. For example, you may need high-quality data to train your AI models. If you’ll be using user data, you’ll need to work through any privacy and ethical issues you might encounter. Thinking through data needs like these early will help you secure the right inputs to make your AI integration worthwhile. 

5. Choose a Method to Add AI to Your App 

There are two primary ways to add AI to your app. Let's break down each one so that you can find the best option for your specific use case.

Custom AI Solutions

One way to incorporate AI into your app is to hire a developer to build a custom AI solution. As opposed to an off-the-shelf option, this route will offer more control and options. You can tailor your AI integration to your specific needs.

Custom AI solutions do, however, have some disadvantages. Often, this path is much more expensive and takes longer to implement. Unlike off-the-shelf options, you'll have to consider maintenance and support, too.

• Best for: Unique use cases, proprietary data, or when AI is core to your value prop.
• Pros: Full customization, long-term flexibility, competitive differentiation.
• Cons: Higher cost, longer development, and ongoing maintenance needs.

Popular platforms: Microsoft Azure ML, Google Cloud AI, AWS SageMaker.

Off-the-Shelf AI Solutions

Alternatively, you can integrate AI into your app by leveraging an existing AI platform. Rather than reinventing the wheel, you can benefit from third-party solutions. APIs make it possible to pull in all sorts of features, like speech recognition, for example without custom coding. The advantages of off-the-shelf solutions are obvious—faster implementation, lower cost, proven track record as well as updates and support.

There are a few downsides to consider, too. Out-of-the-box solutions have some limitations. When you use a third-party option, you'll give up some control in exchange for speed and ease of use. Still, this option is the fastest, easiest, and often highest-quality way to integrate AI. As Emerson Reyna, Senior Product Owner at Designli, stated, “If AI is core to your product’s value, build it. If it’s there to help you move faster, buy it. Don’t overbuild AI for the sake of innovation; go custom only when it’s truly your competitive edge.”

• Best for: Common use cases like chatbots, recommendation engines, or content analysis.
• Pros: Quick to integrate, proven reliability, often includes support and updates.
• Cons: Less control, limited customization, potential vendor lock-in.

Popular tools: OpenAI’s ChatGPT, Google Cloud's Vertex AI, Microsoft Azure Cognitive Services, IBM Watson.

Comparison table between Custom vs. Off-the-Shelf AI:

6. Find a Development Team You Trust 

Next, you'll want to find a team of developers to execute your AI plan. Whether you’re integrating an off-the-shelf API or developing a custom solution, having the right dev team is the difference between a smooth launch and a costly rebuild. Here are a few qualities to consider

• Proven results: Ask for case studies or live examples of apps they’ve built, especially if AI was involved.
• Fluency in relevant tech: They should be comfortable with the tools and languages your AI platform requires (Python, Node.js, TensorFlow, etc.).
• AI-specific experience: This may include prior work with APIs such as OpenAI or Google Cloud, or experience in training and deploying models.
• Operational discipline: Timelines, communication, and documentation matter just as much as the code.
• Product intuition: Great teams don’t just ship features; they ask smart questions to make sure what they’re building actually works for users.

Your team can make or break your project, so don't shortcut this process. A well-established development team is key to integrating AI into your app successfully.

7. Test, Improve, and Deploy Your App 

Once you find a development team, it's time to get to work. Your team will help you through the launch of your new AI features, including testing and launch. Soon enough, your users will have the chance to try your new and improved app for themselves.

Just because you've added AI doesn't mean you've future-proofed your app. Be sure to return to your goals and check the extent to which AI has helped you achieve them. Keep tabs on your KPIs and be ready to tweak and pivot as needed.

For the best chances of success, pay attention to user feedback. How do your new AI features enhance your user experience? How might you use AI to make your app even stronger? You might be able to push the envelope even further than you imagined.

Pitfalls to Avoid When Adding AI

Without a clear roadmap for integration, AI will waste your time and budget. Here are some common mistakes SaaS teams make when integrating AI:

• Overbuilding too early: Adding AI before validating product-market fit often leads to wasted effort. Focus on building a strong foundation first, then layer in AI to scale or refine.
• No clear success criteria: If you’re not measuring outcomes (activation, speed, engagement), it’s impossible to know if the AI is helping. Every feature should tie back to a KPI.
• Using AI as a gimmick: Users see through buzzwords. AI should serve a purpose; if it doesn’t make the experience faster, easier, or smarter, it’s likely adding friction.
• Ignoring compliance or ethics: Privacy, bias, and transparency are especially important in sectors such as healthcare, finance, or education. Make sure your AI features are defensible and respectful of user data.

The Designli Approach

At Designli, we don’t jump into AI for the sake of it. We focus on solving real problems, not just adding technical flair. That’s why our process starts long before a single line of AI code is written.

Here’s how we guide non-technical founders toward meaningful, validated AI integration:

SolutionLab: Strategic Alignment Before Build

Every AI feature starts with a conversation, not a model. In our SolutionLab, we map your business goals, identify user pain points, and explore whether AI is the right fit, all through the development of an interactive prototype.  Together, we outline what success looks like and where automation, personalization, or intelligence can actually make a meaningful impact.

Designli Engine: Lean, Fast, and Built for Scale

Once we’ve defined the right feature, our full-time team moves fast using the Designli Engine to build lightweight, scalable AI integrations. Whether it’s a custom solution or an API-driven feature, everything we build ties directly back to the user journey we mapped together.

We work like an accordion, expanding or contracting your product team based on your stage, launch plans, and strategy. Need to ramp up for a big push? We scale. Need to stay lean for a season? We compress without losing focus or context. What makes this model work is continuity: we prioritize keeping the same people on your project, because nothing moves faster than a team that already understands your product inside and out.

Hypothesis-Driven Development (HDD): Validate, Then Scale

Post-launch, we don’t guess. Every AI feature is treated like an experiment. We define the metric, monitor performance in real-time, and iterate based on actual user behavior. No black box. No bloated backlog. Just insights that fuel smarter product decisions.

FAQs

How much does it cost to add AI to an app?

It depends on the complexity. Integrating prebuilt APIs like OpenAI or Google Cloud typically ranges from a few hundred to a few thousand dollars. Custom AI models or infrastructure can require significantly more investment. Starting lean is often the best approach.

Can I use AI in an MVP?

Yes. AI can be integrated into MVPs to solve specific problems, such as streamlining onboarding or automating a support task. The key is using AI to validate a concept, not overbuilding from day one.

What’s the easiest way to start using AI?

Using off-the-shelf AI tools via APIs is the most efficient entry point. Features like content summarization, sentiment analysis, or chatbot integrations can be added quickly without building models from scratch.

Do I need a data scientist to implement AI?

Not necessarily. Many practical AI features can be implemented by developers using existing tools and platforms. A strong product team with experience in AI integration can often bridge that gap without requiring in-house data science expertise.

Final Thoughts on Adding AI to an App

For SaaS founders, AI integration began as a goal, but it has now become a tool. One that, when used deliberately, can reduce manual effort, personalize the experience, or help users find value faster.

But AI only works when it’s tied to real user needs and clear metrics. Without that, you risk spending time and budget chasing novelty over impact.

Start small. Start specific. Test and learn. Whether it’s smarter onboarding, faster support, or content curation, the best AI features disappear into a better product.

Ready to integrate AI into your app? We'd love to help. Reach out to connect with us. 

You might also like:

• What is Speech Recognition In Apps: An Introductory Guide
• What is a Dedicated Development Team, and When Should You Hire One?
• What is ChatGPT and How Can It Be Used In Apps?