Mobile app development is an ever-evolving landscape, driven by groundbreaking technologies and shifting consumer behaviors. In recent years, the integration of Artificial Intelligence (AI) and Machine Learning (ML) has become a game-changer for mobile apps, elevating user experiences through personalized content, smart recommendations, and voice-activated functionalities. As we look ahead to 2023, it's crucial for businesses to stay ahead of the curve by embracing AI in their mobile apps.
The Power of AI and ML
AI is a discipline within computer science that seeks to create machines that mimic human intelligence. By applying AI and ML principles to mobile app development, businesses can create more intuitive, user-friendly, and engaging experiences. For instance, AI-powered chatbots can provide instant customer support, while ML algorithms can analyze user data to offer personalized content recommendations.
ML works by identifying patterns in data and leveraging those patterns to perform tasks or predict future data. In the context of mobile apps, ML algorithms can help provide a more customized and adaptive user experience, as seen in features like predictive text and personalized content recommendations.
Advances in Algorithms
The advances in ML algorithm models have significantly increased the effectiveness of speech and image recognition. When using these types of algorithms, accuracy is crucial. The collaboration between cloud computing and ML helps businesses that need to manipulate and analyze real-time data to make future predictions.
Advances in cloud computing have had an enormous impact on machine learning capabilities and have greatly decreased the time it takes to train the algorithms. What used to take weeks or months to train models now can take hours, resulting in the need for fewer resources and lower costs.
Machine Learning APIs
As ML and AI-based mobile applications have evolved, so too have the APIs available. These third-party ML services can offer their own advanced functionality so you don’t need to do it from scratch when developing mobile apps. AmazonML API, for example, is a machine learning service that mobile app developers use to leverage and integrate with new or existing solutions.
This service generates billions of predictions daily. It lets developers easily create ML models and add intelligence to their mobile applications with ease through their selection of services.
Applications of ML in Mobile Apps
There’s a wealth of industry-specific applications for ML. ML is used in finance, which you can see through the use of AI-powered financial assistants, like Bank of America’s Erica. Existing apps will offer this kind of functionality as added value for their customers. Apps can analyze user data and transaction history to come up with accurate predictions and financial advice.
Workout apps also leverage machine learning capabilities to offer personalized lifestyle advice, customized fitness plans and workouts, and more. Transportation apps like Uber and Lyft use machine learning to keep drivers up-to-date about traffic conditions so routes can be optimized.
These types of apps utilize ML algorithms and prediction software that analyzes historical data about traffic conditions to detect patterns and make predictions.
Mobile Commerce
Mobile commerce, also known as mCommerce or m-commerce, are mobile apps that are designed for users to perform a variety of commercial activities, such as online banking and online shopping. While eCommerce has long enabled customers to shop online, mCommerce focuses on allowing them these same capabilities, but right from their mobile device.
While mCommerce has been around for a while, it’s gained momentum in recent years, particularly during the pandemic which has shifted user behavior significantly. PRO TIP: More people are getting comfortable using their mobile phones to shop online and make purchases, and apps are making it easier than ever with voice shopping, one-click ordering, and omnichannel shopping.
Advances in mCommerce
mCommerce has seen significant advancements in the past years. For example, mobile payment apps like Apple Pay, Google Pay, and Samsung Pay have simplified transactions, making it easier for customers to make purchases right from their smartphones. In-app purchases and one-click ordering have also become commonplace, giving an easy, frictionless shopping experience.
Personalized advertising has also become more prevalent in mCommerce, allowing businesses to tailor their marketing efforts to individual users based on their preferences and purchasing habits.