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The epoch of Industry 4.0 is first and foremost about the across-the-board digitalization drive that permeates an ever-growing number of business domains. Among software products that inundate the global IT market, mobile apps are probably one of the most wanted, yielding prodigious revenues to their creators and catering to a broad scope of user preferences worldwide.
Yet, the spectacular growth of the mobile application industry may fall short of its promise unless significant players in the realm keep abreast of the innovative know-how that shapes the face of modern civilization in the early third millennium. One of such technologies is artificial intelligence (AI)—a disruptive breakthrough in the world of machines and computer systems that imparts them with the ability to solve complicated problems emulating the operation of the human brain.
Marrying AI and mobile apps is on the radar of companies providing app development services. Securing such solutions gives companies a considerable edge over rival products and higher user satisfaction. These two mission-critical business factors combine to bring a stream of profits into the coffers of entrepreneurs who proved to be far-sighted enough to jump on the AI bandwagon of state-of-the-art technologies sooner than others. By the way, if you’d like to save your time and money on artificial intelligence app development, you can consider cross platform mobile development services.
Besides these two major assets, implementing artificial intelligence in mobile apps guarantees other boons to companies and customers.
The size of the global AI-based mobile app development industry was estimated at USD 19.59 billion USD in 2023, and is expected to reach 170.07 billion USD by 2032 at a CAGR of 27.14%. Such figures are not surprising given the fact that artificial intelligence in mobile apps has remained at the top of the technologies for many years now, which, according to experts, “is the future.”
The main drivers of this industry are AI algorithms, machine learning, natural language processing, and computer vision, which allow AI integrated apps to analyze incoming information like the human brain (but thousands of times faster) and, just like the human brain, based on this information, generate certain conclusions, be it providing users with a selection of films based on the history of their previous viewings, diagnosing the physical condition of a patient, or something else. If we add to this the ongoing transformation of the service sector and its gradual transfer to the digital plane, it becomes clear that artificial intelligence in mobile app development will remain popular for a long time as a cheaper and more effective replacement for human labor.
Mobile artificial intelligence will bring several perks to companies that launch their apps:
Seeing such unquestionable benefits, companies are increasingly implementing this technology into their products. To learn more mobile app development technologies, read this article.
Are you sure you know how the AI software development process works? The fact is that it is somewhat different from the development of other applications since it often requires the simultaneous participation of two teams: one working on the implementation of AI and the other working on the main part of the project in which the AI will be implemented. That’s why we decided to break down the differences below and outline the sequential steps that will lead you to a successful launch of your AI solution.
The process of implementing AI in mobile apps starts with a simple concept—considering the need for this technology in your specific project.
In particular, before introducing AI into mobile applications, it’s important to clearly understand what your end goal of this AI mobile app development is and whether the use of a particular technology will be justified in your case.
The fact is that many entrepreneurs try to create hype applications, stuffing them with trending functionality that can be implemented using simpler (and more cost-effective) tools. As a result, they spend enormous amounts of money on developing the application, and, at the same time, the audience does not accept it because owners of such software are unable to realize the benefits of AI and convey it to them.
Whatever approach you choose to implement AI in mobile apps—be it developing from scratch or using ready-made platforms that provide customized AI models for any need—you must understand that ultimately the success of your software will depend on whether you have data necessary to train these models. And this imposes certain obligations on you: in particular, we’re talking about the preparation of this data.
Specifically, before loading training data into a specific model, you’ll need to clean it, bring it into a single format, and sort it. For this, you’ll probably need the help of specialists. Otherwise, you risk creating an application whose results will not be as accurate as you expect.
Once your data is properly formatted and cleaned, you’ll have a better understanding of exactly which model you need to implement (or which model you need to choose from existing ones). If the second option is closer to you (which is quite reasonable, given the current abundance of AI platforms offering customizable models for any business purpose, such as TensorFlow, Amazon Web Services, Google Cloud Platform, etc.), you’ll need to decide which platform you will ultimately choose.
The fact is that often, the choice of platform depends not on the availability of an AI model of the desired type, but on related technologies that you are going to implement—be it cloud computing, IoT, or something else. If you realize that none of the ready-made solutions have the capabilities you need, you’ll have to resort to custom development. In this case, you have to be prepared that the development of your mobile software will take quite a long time since training a custom model will take up most of this process.
Once the model is selected and configured (or developed from scratch), you can refine the rest of the software. Sometimes, these steps happen simultaneously, with another development team working on business logic and implementing AI-independent features while the first team works on the AI model. As for assembling parts of your mobile application together, this is usually implemented via an API.
After introducing AI into a mobile app, the software development team begins a lengthy testing procedure. The fact is that such software is especially susceptible to erroneous behavior, and finding bugs can take quite a lot of time. Therefore, before presenting your mobile application to its target audience, make sure that its functions do exactly what was originally intended.
Now you’re all set to deploy your mobile application and make it available to its target audience. However, your work will not end there—probably, after interacting with thousands of real users, some of them will discover bugs that were not found during the testing process. This means you’ll need to provide in advance a convenient tool for collecting user feedback. Thus, after some time of testing your software in the field, you’ll be able to collect a list of shortcomings and eliminate them in the new version. This way, you will retain the loyalty of your audience and be able to take a stable position in the AI mobile application market.
Here is a roster of AI-powered technologies that can take mobile apps to new functional heights.
Machine learning (ML) is likely to be the most popular artificial intelligence technology in IT. ML is the ability of computers and software products to make knowledgeable decisions and make conclusions by learning from their past experience.
Such capacity is achieved via two techniques. First, the supervised learning model is honed to forecast future responses to fresh data by analyzing historical inputs and outputs. Unsupervised learning works only with the input information discovering persistent patterns (a.k.a. clustering). Whatever ML strategy is chosen, it opens broad vistas for implementing them into apps used in various industries—from education and healthcare to sales and manufacturing. You can learn more about AI in manufacturing here.
An example of ML-driven artificial intelligence in mobile apps is a fintech solution called Oval. This app analyzes user behavior to draw a personalized saving strategy for each customer. Moreover, being a machine learning brainchild, this app improves with each new data set users enter into it to yield more competent advice. If you’re considering the implementation of a mobile app operating big data sets, please, check this article dedicated to enterprise mobile app development.
People today like not only talking on their smartphone but actually talking to the phone. Equipping devices with a conversational user interface (CUI) makes them intelligent enough to obey voice orders and understand human speech by converting it into a format that machines can comprehend. This AI know-how has given rise to virtual assistants like Siri or Cortana that “live” inside our gadgets and help owners who ask them to find a parking space, play their favorite song, make restaurant reservations and do lots of other errands.
Besides providing a comfortable UX for most users, voice recognition apps are indispensable for visually disadvantaged people who get a chance to interact with their smartphones without actually seeing the screen.
Speech synthesis technology enables AI in your mobile device to speak to you as well. Yet, in fact, your gadget does not so much communicate with you as it synthesizes spoken responses based on text entered into it. This service is rendered in Android-powered devices by a native feature with adjustable voice characteristics (speech rate and pitch) and an option of installing extra languages. You can learn more about mobile application features here.
Yet, this bread-and-butter function can be significantly augmented with specialized apps that are quite numerous in the current IT market. For instance, Narrator’s Voice not only reads text from multiple sources but also has a wide choice of voices to reproduce the text and an option of adding sound effects to it (like gargling, echo, or choir). Plus, the AI can run text you wrote through the synthesizer (and save it as an MP3 file), making the app highly instrumental in voiceover endeavors for presentations and slideshows.
Making a mobile app a part of your brand necessitates automating a fair share of interactions with customers. And this is where chatbots step in, relieving your personnel of tedious tasks like helping clients fill out a feedback form or responding to their typical inquiries. This kind of AI-fueled app is now becoming a must-have for smaller companies, whereas all behemoths in the world of business (Apple, Amazon, Microsoft, IBM) employ chatbot technology on a large scale.
Being able to recognize and classify the object in a picture or video is something humans couldn’t do until only recently. Today, AI apps do this with greater accuracy, and this ability finds numerous applications in various fields—from identifying license plates of traffic regulations’ perpetrators to monitoring the quality of products in high-precision manufacturing.
One such app is Calorie Mama, which helps people control the amount of calories they consume by analyzing photos of meals. Moreover, adding new pictures to the database makes nutrition estimates more accurate over time.
Sometimes you may need to extract letters or numbers (or even a big text chunk) from an image to be typed and used for your purposes. Optical character recognition (OCR) apps are here to help you in this previously impossible mission. One of the most popular among them is Google Keep, which captures text from pictures and saves it in digital format so that you don’t have to type anything manually and can delete the image entirely to be left only with characters.
The AI-powered mechanisms can recognize not only text, images, and human speech, but also our physical parameters (face shape, body size, fingerprints) and gestures.
Mainly being employed in marketing, this technology can be leveraged in other industries, as well. For example, banks, correctional facilities, and educational establishments can benefit from utilizing this software.
For instance, Face2Gene is a highly specialized app meant for clinicians, helping them diagnose genetic disorders. It’s honed to analyze patients’ faces, giving instant feedback and enabling healthcare providers to share results in closed group forums.
This is an elaboration of the previous technology but focuses on identifying a person’s emotions. Body language, vocal fluctuations, and facial expressions are telltale signs that serve as a key for recognizing emotions a person has. Reading them is helpful in assessing consumer attitudes to particular products or ads, gauging TV ratings, or even identifying malicious intent of people in airports, banks, stadiums, and other venues. The modern mobile app market is abundant with such products, with Face Reader by Noldus and Project Oxford by Microsoft being two worth mentioning.
You can get even more mobile application ideas here.
As for forecasts for the future of AI in mobile app development, here, AI will begin to bring increasing benefits both to users of mobile software, due to hyper-personalization, increased accuracy, and expansion of the range of tasks that can be automated and developers, thanks to the increasing spread of no- and low-code software development platforms. In particular, thanks to the latter, developers will be able to spend less and less time writing program code for standard features, which means the time required to implement projects will be reduced.
It’s also expected that in the future, there will be more AI-powered mobile apps that combine several technologies at once. Thus, the bet is on augmented and virtual reality, as well as the Internet of Things. Such AI-enabled technologies can find their use in industry, healthcare, and many other business sectors, as well as in entertainment.
Artificial intelligence is making a robust entrance into many digital products, making AI app development a cutting-edge, high-tech domain. If you’re hesitant to choose a mobile app development company, you can always contact us. By commissioning a custom AI solution from NIX, you’ll obtain a high-end AI powered mobile app that will impress you with its seamless operation and perfect UI design.
An AI Solutions Consultant with more than 10 years of experience in business consulting for the software development industry. He always follows tech trends and applies the most efficient ones in the software production process. Finding himself in the Data Science world, Evgeniy realized that this is exactly where the cutting-edge AI solutions are being adopted and optimized for business issues solving. In his work, he mostly focuses on the process of business automation and software products development, business analysis and consulting.
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