With new technology and developments appearing every day, the field of AI app development is continuously changing (check the Topflight Apps blog entry to know all the details). Incorporating artificial intelligence (AI) into app development is one of the most intriguing and significant breakthroughs. These technologies are already being applied to developing creative and captivating mobile applications that provide consumers with individualized experiences. Top-tier businesses like Amazon, eBay, and Tinder are heavily integrating AI into their applications to deliver tailored mobile user experiences and increase profitability as they see the business supremacy of AI. The trend is so evident that tech giants believe that by 2024, AI will fuel almost 95% of client transactions. This article will explain what AI mobile app development entails and why you need AI integration if you’re trying to enter this market and build an AI app.
How do AI apps work?
Artificial intelligence is ingrained in our mobile gadgets. Predictive text, autocomplete, and even facial ID result from it. According to recent reports, Apple continues to be one of the biggest supporters of artificial intelligence, investing billions of dollars in the field. With a $206 billion investment in AI and other cutting-edge technologies, Samsung is not far behind. In addition to hardware, these businesses include intelligence to enhance the user’s journey and improve the usability of any application.
Four main categories may be used to organize all AI applications in mobile apps. These pertain to what the human brain is capable of.
- Reasoning: All concepts for artificial intelligence app development rely on this capacity. Using reasoning, machine intelligence can recognize the connections between items and respond congruent with human intelligence. Most intelligent programs, like Replika and the Socratic app, are built on this capability. The foundation for other human-like abilities is laid by the ability to think.
- Creativity: Although machine creativity cannot be synchronized with ours, AI applications can provide actual results. Artificial intelligence often encourages human creativity and generates concepts that can energize our creative process. Users may improve their email pitch by adjusting their style using apps like Crystalknows.
- Planning: One of the core uses of AI in mobile apps is its predictive capability. Prediction is estimating or concluding the future from the data at hand. Machines must be able to learn from data and recognize patterns to produce accurate predictions. The Youtube app’s search autocomplete feature or Lisa, an Instagram engagement predictor, displays predictions. Google Maps employs machine learning algorithms to forecast traffic, an anticipated journey time, and an expected arrival time.
- Classification: Finding the category to which an entity belongs is the problem of categorization in artificial intelligence. Many AI applications depend on classification, including speech recognition, natural language processing, and vision. Siri is a prime example of a mobile application that uses natural language processing.
These four kinds of jobs set the foundation for more specialized intelligent applications, such as text analysis or facial recognition.
A step-by-step guide to making an AI app for Android or iOS
Whether creating an NLP solution or a recommendation engine, each artificial intelligence application development business follows a similar plan.
- Find the obstacle: Your attempt to tackle a particular business challenge informs the whole development process. In contrast, a problem with a customer-facing solution may be this one. You should conduct significant market and business research to achieve this. For B2B solutions, internal processes may be present. For B2C goods, customer journeys and personas may be present. Product discovery is essential for setting up your solution for success and saving time and money.
- Choose a vendor: The next step is to choose a few possible AI partners to help you realize your company concept. With such a large selection, narrowing down your potential companions might be challenging.
Here are some recommendations to help you choose wisely.
- Experience – a seasoned market presence naturally suggests that additional abilities and technologies are available.
- A portfolio of comparable projects will enable you to establish expectations that have been proven to work.
- Using AI technology and relevant experience will double your chances of a productive partnership.
- Positive brand perception and client testimonials – trustworthy intelligence teams are simple to find online and have an excellent reputation on Clutch and other platforms.
- Create your own cure: Make app design mockups once you and your technical partner have signed a contract. The design team will outline user journeys and conduct in-depth market analyses to achieve that. A final layout emerges from this phase, which is subsequently forwarded to the development team.
- Choosing a tech stack: A developer will need to employ a variety of programming languages and frameworks, depending on the complexity of the AI project. Typically, the solution and data quality determine the precise combination of technologies. An intelligent application makes use of both conventional technology and clever automation-promoting methods. When it comes to artificial intelligence, certain suspects operate in specialized markets.
- Programming languages: Regarding conventional smart-friendly languages, Python rules the field. Mobile platforms, on the other hand, need the use of mobile-based languages. As a result, Swift and Objective-C are popular choices for iOS programming, whereas Kotlin is a popular alternative for Android development. TensorFlow and sci-kit learn are the most helpful machine-learning libraries for intelligent applications. Additionally, technologies like Microsoft Azure, IBM Watson, and Amazon Machine Learning make regular tasks and app development more accessible, reducing development time.
- Framework: The development of clever algorithms is made simpler by frameworks and application programming interfaces. They provide prepared solutions for applications using deep learning, neural networks, and NLP. Developers may create algorithms quickly, flexibly, and simply with the aid of Keras, Theano, and Pytorch.
- Create an AI algorithm: Thanks to clever algorithms, your application will have human-like skills. However, developing an algorithm takes time and largely depends on the input data quality. Unstructured or segregated data will thus hinder the process and take longer to clean up and assemble. The BI team will model and train the algorithm once your data is prepared. The ultimate choice is the one that provides the highest level of accuracy.
- Launch your MVP: Your team may test the effectiveness of your solution now that all the components are prepared. An early version of your program is known as a Minimum Viable Product. It has a minimal set of essential functionalities sufficient to test the application with actual users. The team may refine the idea and create a complete application once user input is collected.
- Updating and maintaining your product: The support staff performs routine maintenance tasks for your system to stop data drift. Since AI maintenance involves ongoing concept and data upgrades, it differs from other applications. The latter will guarantee that your algorithm’s accuracy stays constant and does not decline. That also applies to routine updates like security fixes and version revisions.
The future of AI mobile app development holds immense potential and exciting possibilities. While it offers promising prospects, it’s important to stay updated with the latest advancements, acquire relevant skills and expertise, and adhere to ethical guidelines to maximize the potential of this path. If you are interested in AI software development, you may also like this article.