Published: August 22, 2024
20 min read
In this article, you'll learn:
1
🔍 Why Should You Integrate AI into Your App?
2
⚙️ Features of Incorporating AI Into an App
3
📝 Integrate AI into an App: Step-By-Step Guide
4
🛠️ Challenges of Using AI in Apps and How to Overcome Them
5
🤝 How Stormotion can help you integrate AI into your mobile app
6
👂Takeaways
Imagine if your app could understand client preferences, show them content they'd likely enjoy, and even predict their future needs. This isn't a distant future scenario but an achievable reality today, thanks to the evolution of AI technology.
Whether you're launching a newborn startup aimed at disrupting the market or enhancing an existing business by integrating artificial intelligence, it's crucial to recognize this as one of the most powerful strategies to boost user retention.
Despite the fact that artificial intelligence has only recently become widely available, by June 2024, 230 million people were already using AI applications. The total revenue of the AI application sector amounted to 1.8 billion USD in 2023. By 2028, this figure is expected to grow to 18.8 billion USD (Business of Apps). Furthermore, in a McKinsey report, 59% of respondents stated that the implementation of AI in at least one function increased their revenue.
So, where to start? The path to integrating artificial intelligence into your program may seem daunting, filled with technical jargon and complex algorithms. But fear not!
This article will guide you through the relatively new technology landscape, offering real-world examples to inspire and inform you on how to implement AI in an app, whether you aim to build a personal training app or create a women's health app. Our experienced developer will also provide insights into integrating artificial intelligence into an application.
Let's start exploring how to integrate AI into an app effectively!
It is not simply cool to add AI to your app. It’s smart, savvy, and can dramatically change your app and improve the user interface. Here are some compelling reasons to incorporate AI into app, illustrated with practical benefits:
Picture this: you are in a store, and the owner or the clerk already seems to know your likes and dislikes, recommends some of your favorite products, and has even remembered the last item that you bought from there. AI does all of this for your app. It also enables artificial intelligence to recommend products that are most likely to interest the users, creating loyalty among users.
You can integrate AI into an app in various fields, whether it's a fitness app, commercial application, or financial accounting. Learn more about what is machine learning and how it can be leveraged.
You can integrate AI into an app in various fields, whether it's a fitness app, commercial application, or financial accounting.
(image by Imran Molla)
Such targeting not only enhances consumers’ interactivity but also motivates the purchase of products. An Accenture report reveals that ninety-one percent of customers are more willing to make purchases from brands that offer them suggestions and discounts.
As any app developer will learn, user interaction is the pulse of any kind of application. Leveraging AI is central to the formula for maintaining user engagement. AI can significantly enhance your application's functionality by adapting settings and content to user behaviors. When you add AI to your app, it becomes more personalized and responsive to individual user needs. For those looking to incorporate advanced conversational AI, consider exploring ChatGPT integration.
Example of integrating an AI assistant into an app.
(image by Anna Kapustina)
For instance, AI-generated push notifications can alert the user about available stores, abandoned carts, or products that he/she may like based on search history.
According to HubSpot, it costs five times as much to attract a new customer as it does to maintain an existing one. AI assists in increasing the user retention rate with the help of timely shared material; therefore, it acts as a way to improve the organization’s revenue.
Check out practices for integrating ChatGPT into an app for more insight!
Regular, day-to-day activities are often repetitive and contain a large margin of errors, whereas AI eliminates these flaws. From handling customer service questions through chatbots to personalizing other aspects such as content suggestions, AI can enable efficiency in several aspects of your application. This not only makes the work more effective but ensures that your team spends most of their time working on the important issues.
Implementing AI features in Android apps can significantly enhance process optimization and automation.
(image by Jack R.)
For instance, there is the Uptech project called “Hamlet” that operates utilizing AI and is aimed at greatly facilitating the process of summarizing texts that are rather long within a short amount of time, thereby freeing up the consumer’s brain.
Adding AI to your mobile app strengthens its capacity for data analysis to find patterns that might not be easily observed otherwise. These patterns can be used to develop business strategies, make effective decisions that help in the growth of the business, and discover new opportunities for growth.
For instance, DeepMind’s AlphaFold has altered the paradigm of biological research through its protein folding prediction, demonstrating the capabilities of AI in discovering more intricate patterns than a human can discover.
Knowing how to integrate AI into an app can help a business reduce costs since it is a cost-effective solution. AI is advantageous in the management of resources, identifying when resources require repair or replacement to avoid expensive breakdowns, and providing energy efficiency improvements. In addition, it can also replace many functions in customer support, meaning the company does not need to hire a lot of workers.
What’s more, due to its ability to foresee possible problems and perform repetitive operations, AI contributes to reducing operational costs and enhancing the effectiveness of your application in the long term.
Usually, the application’s ability to use AI in engagement primarily depends on augmented reality and machine learning, and Snapchat is an excellent example. Snapchat aimed to produce lenses and filters that would eventually engage users of the application in real-time once they integrated AI.
Extracting facial features and recognizing faces, AI allows users to add fun and cute filters to pictures and videos. Based on the integrated machine learning algorithms, the app’s AR function makes the interface engaging and interesting so that users continue using the app.
You can also learn more about ML/AI in meditation industry and how it can be leveraged.
Thus, the main idea of Ikea Place is to change the way people experience furniture shopping with the help of artificial intelligence. This app is an augmented reality and AI tool that helps buyers see the furniture in their estimations before they order from Ikea.
(video by Yanosh Govoshi)
Ikea Place applies AI algorithms that accurately estimate the size of a room and place virtual furniture in it; that is, the application increases customers’ satisfaction as it eliminates uncertainty.
Waze – the social application for navigation grants real-time reports regarding traffic conditions and proposes the most optimal routes with the help of constructed artificial intelligence. Machine learning models work with data from millions of users for a correct traffic forecast and the best route.
This way, with the help of information received from users of the application, Waze trains itself and guarantees the best driving experience to its consumers. It enables Waze to suggest great features like notifications of hazards and current fuel prices, becoming critical appliances for drivers.
It can thus be concluded that the incorporation of AI in Spotify is an innovation that has shaken the music streaming business. Personalization in the present app incorporates a recommendation system based on machine learning techniques to provide optimized playlists and songs compatible with the user’s listening habits.
The recommendation system based on AI algorithms also lies behind personalized playlists such as Discover Weekly and Daily Mix. In other words, through the use of AI, Spotify not only increases users’ loyalty but also enthralls them with the music of their choice.
There are multiple domains of Amazon’s operation where AI is involved in enhancing the consumer experience and optimizing the service for business requirements. The recommendation engine, driven by AI, suggests products based on user behavior, purchase history, and browsing patterns.
AI is also incorporated in Alexa, a home voice control developed by Amazon, which answers to voice commands and questions using natural language processing. Furthermore, AI algorithms strengthen the aspects of inventory procurement and supply, delivery, and customer satisfaction.
Self-driving cars exemplify cutting-edge technology, harnessing AI features to navigate autonomously and enhance road safety.
(image by Lay)
Next to Google, the technology of self-driving cars is currently at its height with Waymo, another Alphabet Inc. company. It self-drives with the aid of deep learning and computer vision, identifying objects in real environments and making decisions in such environments. Information obtained through sensors is processed by AI programs to address issues related to safety and efficiency on the road.
New inventions from Waymo are touching on subtopics such as Artificial Intelligence in Self-Driving Vehicles, providing a glimpse of how the world of driving might soon appear.
AI integration involves more than simply introducing cutting-edge technology to your program; it also involves improving functionality and changing the user experience. Here are some key features that AI can bring to your app:
AI can learn the interaction pattern of the user and can thus present relevant content. Recommendations, content targeting, or interface customization make users feel that they are important to the service or application.
Personalization in the app is enhanced through AI-driven features that focus on understanding user preferences and habits.
(image by Rizal)
For instance, Netflix employs the use of AI to recommend programs based on the user’s past watch history, thus making everyone’s encounter different.
One can use AI in your app to include voice technologies, compatible with the REACH singularity’s speech recognition and NLP apps in equal parameter. For instance, voice commands enable visually impaired individuals to move around within an application, while text-to-speech enhances the experience for individuals with reading problems.
These two — Apple’s Siri and Google Assistant — are clear demonstrations of how artificial intelligence has enhanced accessibility through voice interfaces.
The use of AI can help to boost the options for searching within an application. AI algorithms can interpret the context and intentions behind each query made by the user, hence providing more accurate results.
For example, e-commerce mobile apps can employ AI to recommend links and sort them by the user’s actions, making the search for the necessary item less troublesome for the user.
Image and video recognition advanced by AI can recognize objects, people, scenes, and moods depicted in images and videos. It can be applied in a multitude of cases ranging from social site applications putting labels on images of users to healthcare apps that interpret images of the human body.
Understanding image and video recognition through AI features is crucial for developing advanced applications in various industries.
(image by strangehelix.bio)
Google Photos is an application where AI is incorporated to categorize images and even enable a user to look for images containing specific content.
Check out corporate wellness app development for more insight!
Automated NLP helps apps to gauge human language and, in return, offer people efficient and more natural experiences. By applying natural language processing, customers’ questions and concerns can be answered, support can be provided, or even a conversation can be initiated.
For instance, OpenAI’s GPT-3 model can produce text captions that appear to be human-generated, thus allowing chatbots to provide a range of detailed answers to questions presented to them.
Predictive analytics, a quantitative tradition of AI, can help figure out trends that are hidden in large datasets and make forecasts on future trends. The analyses can be applied in almost every field, including financial and healthcare, to predict users’ behavior and thus anticipate specific problems and come up with solutions based on specific data.
For instance, financial applications may identify fraud cases in transactions through the use of predictions, while healthcare applications may predict the spread of diseases through users’ information.
For this section, we asked our experienced developer Andrian Yarotsky to tell us and demonstrate an example of implementing AI into an app. So, continue reading!
There are a lot of tools for learning how to implement AI into an app, but we're sticking with LangChain for our examples because of its comprehensive suite of features tailored specifically for natural language processing tasks.
LangChain can be used to integrate AI into an app by providing a streamlined interface for interacting with advanced language models like OpenAI's GPT. By leveraging LangChain, the application benefits from simplified API calls, enhanced message handling, and the ability to easily extend functionalities, such as incorporating additional AI models or improving prompt templates, thereby making the integration of sophisticated AI capabilities more efficient and effective.
Large language models (LLMs) are great for building advanced question-answering (Q&A) chatbots. Using a method called Retrieval Augmented Generation (RAG), these chatbots can answer questions based on specific information sources. This is one of the most powerful applications of LLMs.
RAG, or Retrieval Augmented Generation, is a method for enhancing the knowledge of large language models (LLMs) with additional data. While LLMs can understand and reason about a wide range of topics, their knowledge is limited to the public data available up to the point when they were trained. This limitation means that if you want to develop AI applications capable of reasoning about private data or information introduced after the model's training cutoff date, you need to supplement the model with the necessary specific information. The process of retrieving this relevant information and integrating it into the model's prompt is known as Retrieval Augmented Generation (RAG).
LangChain offers a variety of tools specifically designed to aid in building Q&A applications and more general RAG applications. These tools streamline the process of fetching, organizing, and incorporating new data into LLMs, thereby expanding their capabilities and making them more versatile and accurate in handling up-to-date and specialized queries.
Add the necessary packages:
Copy the following code:
Here's another example of a server-side application that processes audio files and generates audio responses using OpenAI and Eleven Labs. The application is built with Express.js and utilizes various npm packages to handle audio processing, transcription, and text-to-speech conversion. When an audio file is uploaded to the server via a POST request, the file is saved temporarily, and its contents are transcribed into text using OpenAI's Whisper model. If the transcribed text is not etmpy the application generates a response using OpenAI's language model and converts this response into an audio file using Eleven Labs' text-to-speech service.
The generated audio file is then sent back to the user. This example could also be extended with parts of code from the pervious example to make the assistant to use external data sources using RAG.
Implementing AI into an app to achieve the desired results is not easy.These issues can include matters such as data quality, all the way to legal and ethical issues. Here, we reveal the most typical challenges that can be faced while integrating AI into an app and how to deal with them effectively.
Data quality and availability are critical aspects that should not be overlooked when making decisions in any organization. Data is the core asset and fundamental building block that supports the creation of AI. However, the quality and availability of data can pose challenges. Low-quality data can lead to low-quality models, thereby resulting in inferior outcomes.
To overcome this, it is important to carry out data validation tests and data cleaning activities. Regular checks and revisions of data collection procedures help maintain data credibility and currency. Additionally, using external data sources alongside internal data can enrich the dataset for AI training.
The complexity of integrating AI into systems can pose significant challenges in ensuring both the quality and availability of data.
(image by Debora)
The integration of AI into apps brings forth numerous legal and ethical implications. Challenges include data privacy, user consent, and the fairness of algorithms used for processing big data. GDPR and CCPA are among the regulations that organizations must strictly adhere to. Transparency regarding the steps AI models take to reach decisions helps build user trust.
To mitigate risks associated with AI implementation, organizations should establish ethical standards and regularly assess the ethical impact of their AI projects.
One challenge is that many systems are built without consideration for AI compatibility, potentially leading to significant operational issues. A modular integration approach, using APIs to integrate AI functionalities with existing systems, can minimize disruptions.
Phased implementation ensures that only parts of the new system are introduced at a time, allowing the existing legacy system to remain active throughout the transition process.
Integrating AI into legacy systems poses a significant challenge due to compatibility issues and the need to ensure seamless interaction between new AI functionalities and existing infrastructure.
(image by Purrweb UI/UX Agency)
The lack of available AI talent impedes the development of AI apps and solutions. Outsourcing to specialized AI firms or hiring consultants can bridge the experience gap.
Developing in-house skills through training programs for current employees is also beneficial. AI-as-a-service platforms simplify integration, as they do not require high technical backgrounds to operate effectively.
Developing AI projects and their implementation and maintenance costs can be expensive, yet the long-term effects are typically much more beneficial than the initial costs. To control expenses, a business can first implement limited, initial projects to prove effectiveness and expansiveness.
It also helps to minimize infrastructure expenses through the use of cloud-based AI services. There is a strong need to undertake a cost-benefit analysis so that the initiative taken is in the business's best interest.
The improper use of AI systems can be altered by malicious means and therefore security is a critical agenda. Measures include the use of encryptions, the use of multiple factors to log in, and having routine security checks on the AI system. When we improve the explainability of AI models, this translates to minimization of security risks by pointing out the existing vices in the models. To deal with the constantly evolving threats, there are updates and patches installed in the AI systems.
This must be respected especially where the use of AI is integrated into the applications of the business; it must respect the industrial norms and legal requirements. GDPR, CCPA, and HIPAA set rules on the ways and means of data management, storage, and processing. Failure to do so attracts penalties and tarnishes that particular organization’s image.
Here are the main 4 rules, following which you should not have problems:
Make sure that all the information gathered and processed by artificial intelligence meets legal provisions on privacy. Use different measures that ensure the anonymity and security of user data, such as anonymizing and encrypting the users' data.
The following guidelines should be followed. It is recommended that users should be clearly told how the information provided by them will be used, and then they should be asked to give their permission. This also leads to the establishment of trust and responds to the regulations that call for disclosure.
It is necessary to keep detailed records of all the actions taken on data. This is useful in establishing compliance during any auditing or investigative actions related to the facility.
One should collect only the information that is required to perform the AI functions. This minimizes the risk of data breaches and optimizes compliance with data minimization legal requirements.
AI applications often require large amounts of data, some of which may be sensitive or personal. This raises privacy concerns and compliance issues with regulations like GDPR or CCPA. When implementing LLM integration it is also important to minimise the possibility of hallucinations. Hallucinations are the events in which LLMs can produce incorrect or fabricated information, which can mislead users and erode trust in the application. To try to eliminate this factor one could utilise function calling. By invoking functions to retrieve data or perform calculations, LLM outputs can be cross-verified against reliable data sources, reducing the likelihood of incorrect information. It is also very important to establish feedback mechanisms where users can report incorrect or hallucinated responses.
Andrian Yarotskyi, Developer @ Stormotion
❗️The ethics of AI deal with justice, responsibility, and open-mindedness. There is a need to guarantee that the models developed by AI will not cause bias toward any group or discriminate against any group. Disparities and inaccuracies should be periodically checked during testing of AI algorithms. It is necessary to establish an integral plan for the protection of AI ethics that will determine the further configuration and usage of AI structures.
Within the Art of Comms project, we are implementing advanced artificial intelligence technologies to automate the process of content review. Our approach is based on utilizing the LangChain platform, which offers flexibility in integrating with various intelligent tools.
Based on the concept developed by our team, we are creating a pipeline for automatic review of video content. The pipeline includes integration with two key services: NVidia Riva for converting audio tracks into text format, considering emotional tones, and Hume AI for content analysis and review using their SDK and our own tools.
(image by Stormotion)
With LangChain, we can effectively integrate these services without requiring deep machine learning expertise. Pre-trained models and AI platforms offer out-of-the-box solutions to automate the processing of large volumes of data and content generation, ensuring high-quality results.
Our goal is to make the process of automatic content review efficient and fast, ensuring high accuracy in analysis and simplifying interaction with content to uphold high standards in communication arts.
Incorporating AI into an application is an excellent solution to enhance your business, attract, and retain more users through personalized approaches and other features. Whether you are figuring out how to make a food delivery app or make a car insurance app, integrating AI can provide substantial benefits. However, despite all the advantages, this task can turn into a real challenge. Let's summarize the main points of the article:
We hope this article has been helpful to you. If you have any questions, we are happy to answer them. Also, feel free to contact us if you are looking for an experienced development team to integrate AI in app. We will be glad to help you! Write to us!
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Identify Needs: Determine the specific business problem AI will solve. Choose the Right Technology and Tools: Select appropriate AI technologies (e.g., machine learning, NLP, computer vision) and platforms (e.g., TensorFlow, PyTorch). Build Your Team: Assemble a multidisciplinary team including data scientists, machine learning engineers, data engineers, and software developers. Collect and Prepare Data: Gather relevant data and preprocess it to ensure quality. Train and Fine-Tune Models: Train AI models on the prepared data and fine-tune them for optimal performance. Integrate and Test Models: Incorporate the AI models into the app and conduct thorough testing. Monitor and Optimize: Continuously monitor AI performance and make necessary adjustments.
Define Objectives: Clearly understand what you want to achieve with AI integration. Assess Data Availability: Ensure you have sufficient and relevant data for training AI models. Select Suitable AI Technologies: Choose the most appropriate AI technologies and tools for your needs. Build a Competent Team: Assemble a team with the necessary expertise in AI and app development.
When integrating AI into an app, it's important to choose the right tools and technologies to ensure efficiency and scalability. The following tools and technologies are recommended: AI frameworks TensorFlow, PyTorch, Keras; NLP tools spaCy, NLTK, BERT; machine learning platforms Google Cloud AI, Azure AI, IBM Watson; data processing tools Apache Spark, Pandas, NumPy.
Key metrics to evaluate the effectiveness of AI in an app include accuracy, which measures how correctly the AI makes predictions; precision and recall, which assess the relevancy of the AI's output; response time, which evaluates how quickly the AI processes information; user engagement, which monitors user interactions and satisfaction; and cost efficiency, which tracks the cost savings and ROI from AI integration.
Unit Testing: Test individual components of the AI model. Integration Testing: Ensure AI components work well within the app. User Acceptance Testing: Validate the AI model with real users to ensure it meets their needs. Performance Testing: Assess the AI model's speed and efficiency. Continuous Monitoring: Implement real-time monitoring to catch and correct issues promptly.
Regular Updates: Continuously update models with new data to maintain accuracy. Performance Monitoring: Implement tools to monitor AI performance and user feedback. Security: Ensure data privacy and protection measures are in place. Scalability: Plan for scaling the AI infrastructure as the app grows. Technical Support: Provide ongoing technical support to address issues and optimize performance.
Personalization: Tailor user experiences based on behavior and preferences. Efficiency: Automate repetitive tasks, freeing up resources for more complex activities. Cost Savings: Reduce operational costs through automation and optimized resource use. Enhanced Decision-Making: Gain insights from data analytics to make informed decisions. User Engagement and Retention: Improve user satisfaction and loyalty with personalized interactions. Innovation: Stay competitive by adopting cutting-edge AI technologies.
These steps and considerations form a comprehensive guide to integrating AI into mobile and web applications, ensuring a structured approach that leverages the full potential of AI technologies.
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