Apps are become a necessary component of our everyday existence. We use them for everything from communication to entertainment, shopping, banking, health and fitness tracking, and more. As consumer demand grows for more intelligent and personalized apps, developers are turning to artificial intelligence (AI) to build the apps of the future. Here, we explore the current and future impact AI will have on app development.

What is Artificial Intelligence?

Artificial intelligence refers to computer systems or machines that can perform tasks that typically require human intelligence. These include visual perception, speech recognition, decision-making, language translation and more. AI systems learn from data and experience without being explicitly programmed.

As AI capabilities grow more advanced, so too do the potential applications. AI is powering everything from digital assistants like Siri and Alexa to recommendation engines, predictive text capabilities and much more. Developers from AI development services are now harnessing these advancements to build the next generation of intelligent apps.

Key AI Technologies for App Development

Several specific AI technologies show particular promise for enhancing and transforming mobile apps:

Machine Learning

Machine learning is a subset of AI that enables computers to learn behaviors from data without being explicitly programmed. Machine learning algorithms uncover patterns in vast volumes of data in order to make predictions or decisions. There are four main types of machine learning:

1. Supervised learning. Input data is labeled, and the algorithms learn to predict future data points based on the examples it is trained on.
2. Unsupervised learning. The algorithms must find patterns in unlabeled, uncategorized input data.
3. Semi-supervised learning. Data that has been labeled and unlabeled are combined.
4. Reinforcement learning. The algorithms are rewarded for correct answers and adapt their approach accordingly.

For app developers, machine learning powers features like recommender systems, predictions, object recognition, speech recognition and more.

Natural Language Processing

Natural language processing (NLP) is the capacity of computer systems to comprehend, interpret, and work with human language. It uses text or audio data to extract intent and meaning.

App developers are using NLP to enable features like intelligent assistants, chatbots, predictive text, grammar correction, language translation and text summaries.

Computer Vision

Computer vision enables computers to identify, understand and classify visual elements using digital images and videos. This includes object recognition, image labeling, image segmentation, facial recognition and more.

Computer vision allows apps to "see" the visual world. These power features include detecting barcodes, identifying images, moderating content, guiding autonomous vehicles, and more.

Current Uses of AI in Apps

By 2025, AI is projected to be a standard feature in 90% of modern applications. Common examples of AI in app development include:

Predictive Text

Messaging apps like SwiftKey use AI to offer next-word predictions that improve typing speed and accuracy. The app learns from millions of data points to predict the user's most likely next words in real-time.

Recommendation Engines

Entertainment apps like Netflix, Spotify and YouTube leverage machine learning algorithms to understand user preferences and serve up personalized recommendations to keep customers engaged.

Chatbots and Digital Assistants

AI-powered chatbots can understand natural language requests and provide helpful information or complete tasks for users. Digital assistants like Siri, Alexa and Google Assistant streamline interactions.

Object/Image Recognition

Retail apps allow users to take a photo of an item, and AI will recognize the exact product or something similar to purchase. Photo storage apps can automatically tag people and objects in images thanks to computer vision.

Predictive Analytics

Finance apps can analyze spending patterns and other data to alert users to unusual activity. Ridesharing apps, on the other hand, can predict arrival times and driver demand based on machine learning models.

Voice Control/Commands

Thanks to natural language processing, apps across various categories, from smart homes to gaming, can be controlled via voice. This hands-free experience offers added convenience.

The Future: Upcoming Advancements

The applications of AI will push apps far beyond their current capabilities in years to come. As the technology progresses, here are some key ways artificial intelligence for app development will shape the future:

Hyper-Personalization

Applications will use machine learning, contextual data, and biometrics to provide remarkably precise customization. Fitness applications, for instance, could focus on very particular food and exercise regimens according to the user's genetics, medical history, and lifestyle choices. Playlists tailored to a listener's varying emotions and energy levels throughout the day might be generated via streaming applications.

Predictive Interactions

Our applications will get smart enough to know what we need even before we ask. A travel software that recognizes your impending plans based on calendar entries may present flight possibilities as soon as you open it. To make sure clients never run out of necessities, e-commerce apps might automatically schedule recurring purchases based on historical purchase history and use.

Flawless Voice Control

New advances in natural language processing will enable applications to comprehend our voices, dialects, and slang consistently. Conversational voice commands will accomplish complicated multi-step operations with ease. It will be as if you are speaking with a real person through technology.

Immersive Experiences

Apps will utilize augmented and virtual reality powered by AI for unprecedented immersive experiences. For example, shopping apps could allow you to visualize furniture in your living room before buying. Education apps could transport students directly into historical events or the depths of space. Possibilities for entertainment, gaming and more will be endless.

Increased Automation

Apps across industries, from finance to manufacturing, will harness AI to automate an expanding number of tasks and processes. This will drive greater efficiency while allowing humans to focus their efforts on more meaningful work. It will also make powerful app capabilities more accessible to everyday users, no matter their technical skill level.

Enhanced Security

AI shows promise to improve cybersecurity and drastically guard user privacy. Machine learning algorithms will grow more effective at detecting breaches across networks and devices by identifying unusual activity. Apps may also leverage biometrics for advanced threat detection and prevention.

Making AI Work for Your App

Here is a process to consider when exploring whether and how to implement AI.

Identify a Specific Need

Don't use AI for app development just for its own sake. Look at your users' pain points and determine if the technology can offer an innovative solution. Ensure the investment will provide sufficient value.

Start Small

Begin by tackling a well-defined problem with a narrow, targeted AI system rather than trying to develop general artificial intelligence. Crawl before you walk. Once you prove the value, expand from there.

Focus on Data Quality

AI is only as good as the data behind it. Prioritize getting robust. Clean data sets to train machine learning algorithms effectively for your desired task.

Put AI to the Test

Validate that your AI system performs as expected under real-world conditions with rigorous testing. Expose any limitations or necessary adjustments before launch.

Plan for Maintenance

Remember that AI must be monitored and maintained. Ensure your team understands the models and can update them to improve and prevent degraded performance over time.

Make AI Transparent

Be upfront with users about when and how AI is used in your app. Provide opt-in/opt-out choices where reasonable. If an AI system makes an inaccurate prediction or recommendation, offer a feedback mechanism to enhance the experience continually.

Conclusion

Consumer applications are set to be transformed by artificial intelligence in the future. Currently, application developers who are using AI and those who are in a position to master future AI opportunities will have a competitive edge. The key message here is that with the right planning and prototyping, AI can be integrated effectively to enhance customer experience and set your product apart. It is high time to begin experimenting with whether and how AI can enrich the experience of using your mobile application.