Mobile AI (Artificial Intelligence) experienced significant growth in 2024, reaching approximately $19.5 billion. Projections indicate that this upward trajectory will continue, with the market expected to expand at a compound annual growth rate (CAGR) of 25.1% between 2025 and 2034 (source).
In a more recent analysis, the mobile AI market was valued at $22.658 billion in 2024 and is anticipated to grow at a CAGR of 28.49%, reaching $79.367 billion by 2029 (source).
These figures underscore the rapid and sustained expansion of the mobile AI sector, which is driven by technological advancements and the increasing integration of AI into mobile applications.
Let’s take a look at the 6 steps to integrate AI into your mobile app.
1. Define the role of AI in the application
This first step is equivalent to the first question you need to ask yourself before creating a mobile application. Here, the creation or selection of an artificial intelligence depends on the objectives you wish to achieve, as well as the problems you wish to solve. Here are the four main business needs for which intelligent algorithms can significantly help.
Improve your application’s User Experience
AI integration into your application offers several key benefits. By analyzing user behavior and habits, AI personalizes the app interface for an optimized user experience. Natural Language Processing (NLP) enhances the understanding of customer feedback, facilitating continuous app improvement. Additionally, an AI-powered chatbot ensures fast and efficient problem resolution.
Chatbots can reduce customer wait times by up to 90%, significantly improving the user experience (webotit.ai). Furthermore, live chat can increase conversion rates by an average of 40% (salesgroup.ai).
These statistics highlight the positive impact of AI and live chat solutions on customer satisfaction and business performance.
Optimize your visibility on social networks
Generative AI produces relevant, targeted content for your social networks. 63% of marketers confirm that this strategy has improved their visibility on these channels. Social networks are increasingly important in the purchase process, particularly for 87% of customers.
Make strategic decisions through data collection
In-depth data analysis, together with real-time monitoring of key performance indicators (KPIs), enables trends to be identified. AI uses them to simulate different scenarios and, thus, estimate future sales or forecast vulnerabilities or evolutions. This is all the easier if your business software is interconnected.
Automate other repetitive tasks to boost productivity
Integrating intelligent algorithms into your application can automate routine administrative tasks such as invoice creation, follow-ups, appointment scheduling, and system updates. This approach, known as Business Process Automation (BPA), can significantly improve operational efficiency. According to a Deloitte report, organizations implementing Robotic Process Automation (RPA) have experienced:
- 86% increase in productivity
- 59% reduction in processing costs
- 90% improvement in quality and accuracy
- 92% enhancement in compliance
These statistics underscore the substantial benefits of automating business processes, including reduced operational costs, improved quality, and enhanced compliance.
2. Select the right platform to create your AI
Once you’ve defined your objectives, you go to an artificial intelligence platform to choose the most appropriate tool. This is an integrated set of technologies for creating or using machine learning (or deep learning) models (source).
You can buy a pre-configured generative AI model or design your own. Various functionalities are available, such as workflow management, scalability capabilities, or multi-language integration. To protect data confidentiality or enhance GPS functionalities (source), it is recommended that you create a fully customized AI platform.
Leading cloud providers now integrate AI platforms such as:
- Google Cloud AI;
- Microsoft Azure AI;
- IBM Watson;
- Amazon Web Services (AWS);
- Amazon SageMaker.
What are the pros and cons of a preconfigured AI model?
A preconfigured AI model is a great way to save time and money. It’s ideal for basic functionalities such as chatbots or image recognition and is easy to integrate. However, it cannot meet overly specific needs, and its performance depends on vendor updates. On the other hand, the confidentiality of sensitive data is not always guaranteed, and costs can quickly rise due to the subscription or the amount of data to be processed (source).
What are the pros and cons of a customized AI model?
Developing a fully customized AI model offers enhanced data control and quality content, providing a competitive edge. However, it requires significant investment in time and resources, including specialized professionals:
- Data Scientists: Analyze complex data to develop and train AI models.
- Machine Learning Engineers: Transform data scientists’ prototypes into scalable solutions.
- Data Engineers: Build and maintain data architectures, ensuring smooth data flow to algorithms.
- Software Developers: Integrate AI functionalities into your application’s infrastructure, collaborating with engineers.
These roles are essential for successful AI implementation (source).
3. Fill your algorithm with quality data
Filling your AI algorithm with quality data is essential for building an effective system. Data collection can come from various sources, such as internal or open-source data, devices like laptops, tablets, and apps, and platforms like CRM systems and Excel sheets.
Tools like Google Open Images, GitHub, Google Analytics, and Hotjar can help collect valuable data. After collecting the data, the next step is to organize and clean it by correcting errors, filling gaps, and labeling it (e.g., images, videos, text files) for categorization. Data scientists typically do this to ensure high-quality data for model training (source).
4. Train your AI model to perform well
When the data is ready, you train your artificial intelligence to recognize and make the most of it. This involves a combination of Machine Learning and Deep Learning. The first technique makes the algorithm autonomous in its learning to improve independently. The second focuses on the evolution of results based on positive or negative feedback.
This stage aims to fine-tune your machine so that its predictions become ever more accurate and effective. After each training session, the AI undergoes a validation stage to assess its performance. If its performance is still not optimal, simply repeat the cycle and train the algorithm with new variables.
Once your intelligent system is comfortable with all the data in your directory, it enters the final test phase. This time, the queries it receives have no labels. Nevertheless, a rigorously trained Machine Learning model can answer them without making any mistakes.
5. Complete the integration of AI into your mobile application
Has your artificial intelligence passed the validation stage? Congratulations! Now, all you have to do is integrate it into your mobile application. There are three ways to do this (source):
- Host the AI directly on a server or cloud platform.
- Integrate it on an application programming interface (API).
- Add new elements to the application’s user interface.
Once integrated, complete the final tests to ensure your AI works appropriately. Its parts must work together without conflict, and the application must respond reliably to user needs. Pay close attention to your algorithm’s response time, as this parameter remains a key user experience (UX) point.
6. Monitor and optimize the performance of your algorithms
Just because your AI is online doesn’t mean the work is over. On the contrary, you need to constantly monitor its performance, such as the accuracy of its predictions or the speed with which it processes queries. Thanks to Machine Learning’s properties, make sure that your algorithm continues to identify and memorize new data so that it stays up to date. Optimizing the machine through various tests and new training sessions guarantees a satisfactory rate of engagement with this tool.
Bottom line
By integrating AI into your mobile (or web) application, you improve the User Experience of your tool like never before. As a result, you gain engagement and, ultimately, conversion rates. However, adding an intelligent algorithm to your application is a methodical process that requires know-how and substantial resources. At BluDeskSoft, we can help you add advanced application features based on your specific needs. Take a look at our services for web and mobile applications.