With the rising popularity of mobile app development, an increasing number of developers are seeking to harness the capabilities of AI to craft more captivating and intelligent user interactions. More than 70% of consumers worldwide are actively utilizing AI-enhanced mobile applications, and it is anticipated that the market for AI-driven mobile app development will expand to around $73.27 billion by the year 2027. Here we’ll delve further into AI into app development.
Although the integration of AI into mobile applications is a relatively recent phenomenon, many businesses are already taking an interest in creating their own apps. AI can enhance user experiences, automate in-app tasks, and provide valuable insights. However, embarking on this journey requires careful planning and execution to avoid huge costs.
As Mike Erlihson, principal data scientist, states, “The cost and time required will depend on the complexity of the to-be-implemented AI model, the amount and quality of available data, the expertise of the development team, and the infrastructure required to support the AI tools and apps.”
Let’s walk through the essential steps for incorporating AI into app development to avoid any potential pitfalls.
1. Assemble Your Team And Research
The first and most important step in incorporating AI into your app is forming an excellent team. Any successful AI integration demands collaboration among experts who possess a diverse set of skills. Your team must include an experienced project manager, a software architect who can design technical solutions and define non-functional requirements, and a business analyst responsible for gathering, assessing, and documenting functional requirements.
Building the right development team is also essential to the success of your AI integration project. You should seek out AI developers with expertise in Python, UI designers, web and mobile developers, testers, and DevOps engineers. Hiring full-time developers from reputable development companies, rather than freelancers, can provide the necessary expertise and project management support required for complex AI integration.
Before delving more with AI into app development, take a look at successful AI-powered mobile and web apps. Explore how others have harnessed AI’s capabilities to create innovative solutions and what teams they have assembled. Examples of successful AI-powered mobile apps include Google Assistant, Amazon Alexa, and Socratic.
2. Clarify Technology Selection And Objectives
Before integrating AI into mobile apps, you will need to define your goals precisely. Determine the desired user experience and how AI can help achieve it, whether through enhancing accuracy, streamlining processes, or creating personalized interactions.
Once your objectives and data are in order, select the AI technology that aligns with your app’s goals. This could be utilizing open-source frameworks like TensorFlow or leveraging cloud services such as AWS or GCP. Your choice of technology tools should be tailored to your specific app functionalities and scalability requirements.
3. User Benefits And Technology Stack
Next, it’s essential to assess how integrating AI and ML can provide tangible benefits to your app users.
Consider the potential applications of AI in your app, such as analyzing user behavior for personalized recommendations, implementing AI chatbots for customer support, or detecting fraudulent transactions. Identifying the right use case for AI is crucial to your project’s success.
Given that you are integrating AI into existing digital solutions, continuity in your technology stack is advisable. Python is very well-suited for developing AI modules due to its array of extensive libraries and support for key AI capabilities like ML, image recognition, and natural language processing.
Database selection is also crucial, with options such as MySQL and PostgreSQL for structured query language (SQL) databases and MongoDB or Apache Cassandra for NoSQL databases. They all offer different advantages and disadvantages. PostgreSQL, for example, stands out as a highly robust and advanced database management system, making it an excellent choice for organizations requiring swift execution of intricate queries. On the other end of the spectrum, MySQL emerges as the ideal solution for companies facing tighter budgets and space constraints.
4. The Art of Testing
With your team and technology stack in place, it’s time to take the plunge with a test. The software development process should encompass designing UI changes, data collection, AI/ML algorithm implementation, and testing. Additionally, ensure that robust information security solutions are in place to protect your app and user data.
If your AI app involves a chatbot, you could create scenarios where users pose common queries, unusual questions, or even complex inquiries. From there, you can evaluate how well the AI handles each situation. You can also conduct A/B testing to assess the performance of an AI-enhanced application against either a non-AI variant or an alternative AI model. This methodology aids in demonstrating the real-world advantages and enhancements that AI technology brings.
After rigorous testing, you can deploy and integrate the new AI system seamlessly into your web and mobile apps. By following these steps, developers can harness the power of AI into app development to create innovative and user-centric applications that stand out in today’s competitive market.
