How to Implement AI Into an App?
As AI has been a hot topic for a few months now, more and more companies - including no code ones - have started to integrate it into their apps: Notion, Airtable, Bubble, to name a few.
Many others are still exploring the best way to do so, and integrating AI into apps will definitely be a priority for tech companies from 2024 and onwards.
In this article, we will go through some of the AI technologies that can be integrated into apps, explore the benefits for companies and go through some of the industries where AI integration has already proven successful.
What will we cover
Artificial intelligence is a subfield of computer science. Its purpose is to teach machines to perform complex tasks and act like humans would do.
Artificial intelligence integration involves embedding AI-driven systems, features and functionalities to improve user experiences, automate tasks, provide personalized recommendations, analyze data, and solve complex problems.
AI techniques include, among others: natural language processing, machine learning algorithms, speech recognition.
Let's deep dive into some of the different types of artificial intelligence technologies that can be integrated into apps.
According to Arthur Samuel (1959), machine learning is "the field of study that gives computers the ability to learn without explicitly being programmed.” In other words, It's a technique aimed at teaching machines to get insights and learnings from data and improve through experience, without being explicitly programmed to perform the desired action.
Famous examples of applications based on machine learning include:
Streaming platforms such as Netflix: the platform is able to make recommendations based on the movies/series the user has been watching
Social media like Facebook: social networks make pages suggestions and friends recommendations based on users' activity, likes, comments
Product recommendations on e-commerce websites: one of the most popular AI-based features.
Machine learning will learn from your purchase and browsing history, searching patterns, to recommend specific products that a user is likely to like and purchase.
Natural language processing, or NLP, is a branch of AI and actually requires machine learning to be able to function. Purpose of NLP is to enable computers and digital devices to recognize, understand and generate text and speech, in a way that is both meaningful and useful.
Some examples of NLP use include:
Chatbots: nowadays, chatbots are widely used in customer service. Chatbots combine conversational AI and NLP to provide answers to customers queries
Translation tools: Language tools use NLP to translate texts from one language to another
Auto complete: while writing text messages or doing some web research, we're all familiar with the autocomplete functionality, which makes predictive suggestions.
Computer vision is an AI technology that also uses machine learning, as well as neural networks, to teach computers and systems to extract meaningful information from digital images, videos and other visual inputs.
Computer vision requires a huge set of data to work; until it can eventually recognize images.
Facial recognition, which is used to unlock phones, for instance, is based on computer vision.
Image recognition with deep learning has become a major trend in AI. Many use cases show how important this technique has become. Image recognition is the ability from softwares to recognize elements (objets, people, places, etc) in digital images.
Image recognition works by leveraging machine learning, where AI learns by reading and learning from large amounts of image data.
A widely discussed artificial intelligence technology; generative AI - as the name suggests - refers to the technology that is able to generate new contents, texts, images, and has powerful features. Generative AI is revolutionizing content creation.
Most famous example of generative AI tools is ChatGPT, which is able to generate texts and images based on simple requests.
Implementing artificial intelligence features on apps presents various benefits:
Data collection and data analysis are critical to companies success; it enables them to understand behaviors and preferences from customers & prospects, and then,be able to meet their needs.
Artificial intelligence can automate data collection from many various sources, and analyze large volumes of data, quickly and efficiently. It then leads to pattern recognition and trends identification, which makes the decision-making process easier for businesses.
Developing and implementing intelligent features for an application can lead to a more personalized - and ultimately better - user experience. Apps can indeed adjust the content, products, or services to users based on their past behavior, preferences, and similar user profiles.
Using AI to personalize and enhance user experience is essential for companies, as 71% of customers want businesses to provide personalized experiences; but it already seems to be a no brainer for a vast majority of them: According to a report by Twilio Segment, 92% of companies are already using AI personalization to drive business growth.
AI-powered facial recognition technology is now widely spread on mobile apps. AI algorithms can correctly identify persons and allow access to authorized users by analyzing face features, contours, and unique identifiers.
Facial recognition is now widely used, for example, in banking apps, social media apps, or more generally, apps that require a strong authentication process.
Artificial intelligence, through automation, has the power to help companies save precious time and increase productivity.
One good example for this AI benefit are chatbots, which can now take over repetitive tasks with little to no added value; and let employees focus on more important tasks and issues.
This is particularly true in customer service: where chatbots can now provide instant support to customers, answer some common & repetitive questions (refund processes for an e-commerce company, arrival times for a train company, etc).
Let's go through some examples of successful companies which integrated AI into their apps.
Streaming platforms like Netflix's recommendation models are based on AI. The algorithm will suggest series or movies based on the user's viewing habits, and behaviors of users with a similar profile.
Marketing and more specifically emailings are a good example of the use of AI in B2B apps. Email marketing based on AI is the process of using machine learning to improve strategies and email marketing campaigns.
Some ways AI can be leveraged in emailings include:
- Generating content: AI can help marketers with their content creation; suggesting efficient email objects, rewriting messages with another tone of voice and optimizing it
- Evaluating the best sending time: The platform can send the campaigns at different times based on users' habits of opening times, maximizing the campaign delivery
Through itinerary personalization, travel is also one of the industries relying on AI for some functionalities. Trip planning for instance, relies on artificial intelligence, making tailored recommendations (activities, restaurants, etc) based on the traveler’s preferences and past travel history.
Now that we have a clear understanding of the benefits and technologies, let's address the concrete question: how do you actually implement AI into an app?
While custom, code-based integration offers total flexibility, the rise of no-code tools has made artificial intelligence accessible to a much wider audience.
The most common way to integrate AI is by using APIs (Application Programming Interfaces). To put it simply, an API is a "gateway" that allows one application to communicate with another.
Many AI services, such as those from OpenAI, Google AI, or DALL-E, provide APIs. By connecting your application to these APIs, you can send data (for example, text to be summarized) and receive a response from the AI (the summary).
For developers and no-code app builders, integrating via APIs is primarily done through connector tools like Zapier or Make. These platforms act as a "bridge" between your app (like Webflow) and the AI service's API, without you having to write a single line of code.
Some no-code platforms, such as Blitz, Bubble or Airtable, have already started to integrate native AI features. This allows you to add AI capabilities directly from the platform's interface, without going through an external service, which simplifies the process even further.
Key takeaways
Sources
.png){kind=logo}
