Approx. read time : 10 min
Most of us were convinced until last year that it would take at least another decade for artificial intelligence to become mainstream. And then the pandemic happened. It changed how humans interact with technology in ways unimaginable before 2020. The wave of global lockdown turbocharged tech deployment across industries. AI and machine learning adoption are some of the numerous such tech trends of the pandemic era.
AI and ML are now central to our day-to-day living in more ways than you could imagine. And that’s why businesses and organizations of all kinds are taking these technologies more seriously. Mobile apps are perhaps the most widely used tools when it comes to integrating AI and ML.
From facial recognition-based biometric functionality to customer support automation, AI is everywhere. At the same time, machine learning is driving a rapid evolution in consumer technology. Be it setting your Netflix preferences or enabling predictive maintenance in a manufacturing plant, ML has incredibly diverse usages. And mobile apps play crucial roles in all of these. That’s why AI and ML are driving a significant transformation across all areas of app find. Read on to find out how. But before we delve deeper, let’s try to understand the fundamental working mechanisms of AI and ML.
How AI and ML Work
AI is the quest to build systems that can reason, learn, and act intelligently — like humans. And that’s called “artificial” intelligence — intelligence that’s not natural. Machine learning (ML) is a subfield of AI focused on finding patterns in massive amounts of data. ML-powered algorithms have wide-ranging applications, from Netflix recommendations to disease diagnosis.
Artificial general intelligence (AGI) takes AI to the next level. For creating AGI, first, you need to build general-purpose AI. Some of the most prominent features of AGI include unsupervised or self-supervised learning, the ability to transfer learning, and common sense and causal inference.
Deep learning is the most advanced arena of ML. According to AI pioneer, Geoffrey Hinton deep learning will eventually be able to do everything, expanding its scope beyond algorithms. It could also help accelerate the evolution of common-sense AI. For now, AI and ML have already emerged as dominant drives of mobile app evolutions. Let’s check how.
Roles of AI and ML in Mobile App Development
Artificial intelligence and machine learning have started to dominate all the tech-focused areas of mobile app development. With businesses and organizations getting more interested in these technologies, demands for AI- and ML-enabled apps have surged. According to an analysis by the Harvard Business Review, AI will likely open numerous avenues for businesses of all kinds. From offering better product recommendations in an online store to getting deep business insights, AI and ML have a lot to offer.
Medium enterprises and large corporations alike, therefore, investing in emerging technologies to get a competitive edge. As mobile apps dominate everything from grocery delivery to healthcare, AI and ML will play a more crucial role in business success. This post seeks to help you understand mobile app trends pivotal to a modern-day business. Below are some of the most striking app development trends prompted by AI and ML.
The Rise of Facial Recognition
Facial recognition saw a remarkable surge in 2020. It has remained on news for the past year for all the good and bad reasons. For instance, facial recognition has enhanced data security by reducing the chances of identity theft. However, the technology is also being used to track down people by authoritarian nations like China.
But the positives of facial recognition largely outweigh its negatives. For example, financial services companies are relying on advanced facial recognition to prevent fraud. AI-enabled mobile apps can ensure that the right person is using a mobile device or a banking app. Due to its growing popularity, most smartphones nowadays have in-built facial recognition capabilities.
These trends have a significant impact on how businesses use mobile apps. That makes facial recognition an increasingly dominant driver of next-gen app development. Tech-based service providers like dating apps and fintech companies are already experimenting with AI. Other services are likely to adopt the technology for enhancing customer experiences. That means both B2B and B2C apps will adopt AI for upgrading functionalities.
Voice Assistant Goes Mainstream
Voice assistant is perhaps the most widely loved AI-best consumer technology. Whether you use Apple’s Siri, Amazon’s Alexa, or Google Assistant, all are based on AI. And these AI-based assistants have seen rapid consumer acceptance in developed and emerging markets alike.
Voice assistants are also growing their presence in online searches. With the technology’s evolving capabilities, more and more people are preferring voice assistants over texts. And tech companies are continually improving their voice assistants. For example, Apple’s Siri now allows you to personalize your voice assistant. You could choose different language accents for detection, Indian, American, British, etc. Siri also allows you to choose your type of voice assistant you want, such as American female voice, Indian male voice, etc.
App developers around the world are incorporating voice assistants to enhance customer experiences. Chatbots, for instance, are facilitating seamless customer support around the clock. As lockdowns forced call centers and offices to close, AI assistants replaced humans to offer customer service. Industries as diverse as banking and food service have since relied on voice assistants for prompt customer support.
Improved Search Engines Powered by Algorithms
Without commercial and administrative activities alike moving online, search engines have become more crucial than ever. The combined power of advanced web capabilities and deep machine learning is making search engines better. Tech giants like Google and Microsoft are using advanced algorithms to offer more relevant search engine results. These algorithms rely on machine learning to understand patterns. Mobile and web apps that rely on online searches must best use machine learning to get the desired user engagement.
The Popularity of ML-Powered Mobile Apps
Machine learning transforming mobile app experiences in numerous ways. ML capabilities are incredibly effective in tracking activities and storing users’ historical data. It can also help users correct spelling, respond to voice searches, and help find the most relevant search results. Product and content recommendations are some of the most popular applications of machine learning in mobile apps.
Besides, machine learning can combine with analytics to provide useful data and deep business insights. ML-powered capabilities also ensure data accuracy, help decision-making, and bolster digital connectivity, among other things. Machine learning is helping mobile app developers to build more flexible, algorithm-based, futuristic mobile apps. The ability of ML to ensure data-driven learning and provide real-time analytics makes it one of the most useful tech tools for businesses. Markets are already seeing a shift from legacy software systems to algorithm-run enterprise applications.
Increased Use of Predictive Analytics
Predictive analytics has an incredibly diverse business usage. Machine learning enables software systems to analyze massive amounts of data and recognize patterns. Using these patterns, ML-based solutions can predict consumer behavior, recommend relevant products or services, and offer deep business insights.
Online food delivery services saw record growth around the world during the pandemic. Be it DoorDash in the U.S. or Zomato in India, all of them depend heavily on AI and machine learning. Predictive analytics play a particularly critical role in delivering the desired services. Both AI chatbots and ML-powered analytics helped services to deliver superior experiences. And as you already know, these services are delivered most through mobile apps.
Food delivery apps were, in fact, were one of the most demanded categories of apps during the pandemic. And for mobile app development companies, building the market-best apps mean using the most evolved technologies. While these technologies enabled service providers to stand out, app makers, too, gained a competitive advantage using AI and ML.
Better App Security & Privacy
Data security has become a top talking point among business owners and app users alike. Data breaches have occurred across industries. And governments worldwide are introducing stricter measures ensuring sufficient data protection. That means protecting users’ data is no more a choice. It’s a must. AI and ML help you implement next-gen security measures.
Apps with built-in AI and ML are better equipped to detect and report potential threats. AI-powered algorithms, for example, can promptly track malware and risky elements. Using automated threat detection functionalities, you can stay one step ahead of hackers and cybercriminals. Security-aware businesses are increasingly using such features to protect their customers’ data.
Engaging Content & User Experience
AI is enabling business owners to deliver deeply personalized user experiences across platforms. Using advanced data analytics and deep machine learning, businesses can understand customer interests. Then they can create the desired experience. With consumers’ growing tendency to buy virtually everything online, such capabilities are more important than ever.
Besides, when you automate functionalities like chats and email responses, customers get a superior user experience. In other words, AI, and ML tick many boxes that make your customers’ overall experience better and more likable. Whether it’s getting a prompt response or personalized email replies, everything adds up to the customer experience.
Training AI for Better Efficiency
Training the AI solution is one of the trickiest parts of building an app that relies on these technologies. Leading AI developers and researchers are using large datasets like ImageNet to ensure superior training. Such tools are becoming increasingly common in developing image-recognition systems. Most image-recognition systems are trained with massive datasets containing millions of photos.
According to findings of the latest study by researchers in Japan, such systems can also start learning everyday objects trained on software-generated fractals. Fractals are complex pictures generated from a common formula. Computer systems use iterations to create such fractals.
Training is particularly sophisticated if the use cases are diverse and unpredictable. An AI learns some basic skills during the pretraining phase. Then it’s trained on more specific and specialized data. Pretrained AI models are usually more efficient learners. It makes AI app developers’ jobs easier since they don’t have to train from scratch.
Business Benefits of AI- and ML-Powered Apps
You may already have an idea of how AI and ML upgrade the tech ecosystem of your business. However, we won’t be doing justice to this post if we list down the specific business benefits. Here are some of the most striking benefits.
Useful Business Insights
Deep machine learning and advanced data analytics together enable you a better overview of the business. You could easily monitor and understand consumer behavior and make decisions accordingly. Machine learning can precisely understand even complex patterns and get useful findings.
Deeply Personalized Experiences
In an increasingly competitive business landscape, customer experience could make or break your brand reputation. Every time a customer uses your app you get an opportunity to create an impression. Personalized design and content-enabled you to make that experience.
Faster Search and Navigation
Speed and seamlessness are crucial to the success of business apps. When you add machine learning capabilities to your app, search experiences get a dramatic boost. For instance, an m-commerce app with machine learning abilities can predict the buyer’s search even if they mistype the product name. Likewise, for food delivery apps, ML makes it easier for the user to find a dish or a nearby restaurant. All these significantly contribute to creating a superior user experience.
Data Mining and Behavior Analysis
Big data is rapidly becoming central to business growth and management strategies. Machine learning and AI allow you to upgrade data mining techniques. You could get some incredibly useful customer behaviors and interests by analyzing complex patterns. Such analyses are only possible with big data and deep machine learning. A growing number of businesses are now adopting such techniques to find out customers’ likes and dislikes, among other things.
Tech experts say the dominance of AI and ML in the business world will only grow over the coming years. With consumers and business executives alike becoming more open to embracing cutting-edge tech solutions, AI- and ML-powered apps are forecast to become mainstream across industries. As an app developer or business owner, you must ready yourself to dynamically adopt these trends and stay ahead in the tech-driven market.