There are currently 2.87 million apps that are available in Google Play. The number of apps on the market has exploded due to scalability brought on by AI.
Artificial Intelligence offers distinctive advantages in app development, reducing a significant amount of effort on the part of engineers. Robust modern approaches utilize AI and machine learning to tackle various challenges, vastly reducing the total number of hours spent on the most tedious stages of the development process.
There are a lot of things people don’t know about AI. These include the amazing features that help developers create powerful apps.
Many features that end-users take for granted in modern apps are only possible because of strides in Artificial Intelligence, for example, image recognition features made popular by apps like Google Lens. If your business is in the process of bringing a new app to market, it is vital to investigate the advantages of AI in app design. This technology not only streamlines the development process but can also augment your app with new future-forward features that customers would like.
The Most Popular Types of AI-Powered Apps
Market research suggests that over 90% of apps will include at least one AI-based module by 2025. Some of the most popular implementations of Artificial Intelligence include the following:
- Chatbots, such as Google’s ground-breaking LaMDA AI, which was so convincingly lifelike that it tricked one of Google’s senior software engineers, Blake Lemoine, into believing that it was sentient.
- Digital Assistants, like Amazon’s Alexa product, connect Amazon’s customers with a growing library of apps that users seamlessly call upon with voice commands.
- Search Engines, with the ever-present Google Search, utilize AI to rank web pages and multimedia content, as well as to determine exactly what clients are searching for and deliver the most relevant results.
- Personal Productivity Tools, like Grammarly, utilize machine learning and natural language processing to analyze and process the structure, syntax, grammar, readability, and overall coherence, engagement, and appropriateness of articles and essays for writers.
- Navigational Tools, with one example being Uber’s UberEats app, which uses a sophisticated set of AI tools to connect customers to local drivers to deliver meals, drinks, and snacks from local vendors.
- Social Networks, like Snapchat, use built-in facial recognition AI to map fun effects over its users’ faces in real-time.
These are only a few examples from a vast ocean of AI-enabled apps. You don’t have to look far to see Artificial Intelligence implementations for any project imaginable, from projects like PlantNet which identifies species of plants to ELSA Speak, an app that helps its users learn English words and pronunciation with instant feedback from AI.
AI-driven app development in many ways is a scientific research project. Depending on the complexity of your AI app and whether you’ll be building on top of existing AI frameworks and APIs or building up everything from scratch, the build can take a lot of extra work in the research and development phase. The estimated research and development window also depends on whether or not the core features of your app require AI or if AI will merely power some of the extensible aspects of your app.
Integrating Artificial Intelligence into your app’s login flow for improved security takes a lot less work than developing Snapchat-like Augmented Reality features, which depend on more sophisticated AI modules like facial recognition, not to mention the packages and modules responsible for rendering 3D graphics.
AI is designed to learn to perform tasks, whereas traditional software cannot take advantage of these machine learning paradigms. In conventional software, the programmer writes code that deals with inputs and outputs and has to deal with organizing and programmatically processing data. In contrast to traditional programming paradigms, where engineers write algorithms for solving a business problem, AI can itself select an algorithm by identifying patterns in data. For example, if image recognition AI sees enough pictures of an apple, even if the lighting conditions and colors and shapes of the apple somewhat vary, it begins to interpret the data, make inferences, and spot patterns; this helps determine whether or not an apple is present in the photo.
In the coming years, Artificial Intelligence will cut down on development time even with conventional application programming, helping programmers generate valuable insights and even connect interactively to code editors to improve and debug code on the fly. AI-аssisted coding projects promise to help programmers write code faster, avoid mistakes, streamline context switching, and work with unfamiliar modules and APIs.
To understand what you need to build your company’s AI app, you need to determine the scope and goals of the project right away. Aside from figuring out how many technical and non-technical specialists you need on your team, you also should figure out the logistics of the project and estimate how much time the development could take. When all of the basics are sorted, you’ll also need to determine what kind of data you need and how much data you need to create your AI app. During this critical stage, you also have to discuss what kind of AI capabilities need to be a part of your project, such as computer vision, speech recognition, generative AI, and so on.
The next phase of developing an AI app is gathering the data, deciding which data is essential and how it will be stored, choosing the way you’ll annotate the data, highlighting and labeling blocks of data, and choosing file formats. There are also different types of data sources, such as data sourced from open-source or public data sets, data scraped from various sources, data that comes from annotation or unsupervised models, etc.
Once you have the data, the project can enter its actual development phase. AI app development includes PoC (Proof of Concept) and demo. The first thing an AI development company does when a product owner asks it to build an AI-based application is assess whether AI is the core of the product or an add-on. The answer to this question affects how complex the solution will be. The PoC phase of a new AI project should be AI-centric, which means we should start with the riskiest part of the project, the AI feature, and avoid other project features if possible.
Where to Start?
Any AI project, let alone any software project, carries risks for a business and its investors. Some risks are derived from the suitability of the data, but other risks materialize from the algorithms and the implementation of the AI module itself. Mitigating the risks skillfully means starting product development only after the AI module’s accuracy is suitable to meet your business’s expectations and goals.
It is easy for businesses to get on board with the AI hype without having a realistic plan of how to implement this powerful technological paradigm into products. However, AI can be complicated and expensive. That’s why the requirement for significant research and development before implementation is a reality. Starting strong with thorough research and a solid proof-of-concept helps make sure the project is feasible and ready for investment. So, before you launch your AI project, be sure to enlist the help of real AI experts who can identify all the risks and mitigate them, turning artificial intelligence into a real advantage for your business.