Artificial intelligence, along with deep learning systems, is one of the key driving forces in modern software and hardware development. Regardless of the industry or the sector, AI and machine learning applications are becoming virtually endless, as nowadays these technologies can help businesses achieve their micro and macro goals efficiently and effectively.
When it comes to mobile app development and the features that modern apps bring to the consumers, AI and machine learning can greatly streamline the process while enhancing the in-app user experience. Let’s put that into perspective and take a look at how these technologies are changing mobile app development, just in time for your next app development project in 2023.
Here’s what you need to know.
Enhancing the DevOps process
DevOps is a comprehensive term used to describe the numerous tools, tactics, and processes used for efficient and effective software development, but its foundational pillars can be applied to numerous other departments and sectors. In mobile app development, the DevOps principles and way of thinking can be invaluable in driving down costs while aligning IT teams with operations and other departments in an organization.
You can apply DevOps principles and methodologies in android and iOS app development in the fields of automation, collaboration between teams, continuous testing and optimization, as well as ensuring you’re making constant daily progress. It’s also important to note that AI and machine learning play a vital role in maximizing the potential of every DevOps project.
AI and machine learning are primarily tasked with providing automated, daily testing of software to ensure everything is working properly, but in a separate sandbox environment so that the teams can continue working on the product at the same time. Quality assurance is a big part of AI implementation here, and it helps to develop a mobile app faster all the while ensuring that all features are working as intended.
Detecting malicious behavior and profanity
When it comes to in-app functionalities and ensuring complete data protection and cybersecurity, artificial intelligence and machine learning systems are becoming indispensable to software engineers and the end users. During the app development process, AI systems can conduct regular pen testing and security probing to uncover potential weaknesses, while the deep learning systems can continue to improve cybersecurity measures and create meaningful insights.
After launch, artificial intelligence can be used alongside a profanity filter that automatically spots and flags suspicious behavior in the chat, such as bullying, criminal activity, sexual advances, bigotry, and more. The system can keep learning to not only detect these keywords, but to spot potential malicious or criminal activity ahead of time.
These features can bring the end user much-needed peace of mind, knowing that the app itself is safe and that the chat functionality is smart enough to automatically protect them in various conversations and potentially harmful situations.
Automating chat functionalities
Speaking of chat, we also need to recognize that in-app chat functions have become ubiquitous to modern mobile apps, as they facilitate brand-user communication and communication between users. A good chat function helps develop your brand’s community, but it also allows you to deliver exceptional customer service on a moment’s notice right there in the app.
Modern mobile apps are heavily banking on chatbot marketing as well, allowing the brand to communicate directly with the user in the app by sending direct messages and push notifications. Artificial intelligence comes in when it starts providing smart, timely support along with personalized offers and interactions in the DMs.
Programming AI-driven chatbots and intuitive chat functionalities during the development process is therefore an essential element of good UX and a great way to ensure user satisfaction in the long term.
Building automated self-help desks
The in-app experience is becoming increasingly complex and all-encompassing, but that should come as no surprise. There’s nothing that modern users hate more than having to venture outside the app to get an answer to a question or to complete a certain action. Every time they exit the app because they have to and not because they want to, you risk them going to the competitors.
That’s why software developers are working heavily to create a closed loop of in-app experiences that will keep the user from closing the app while perusing products or services.
When it comes to customer service, a user should be able to reach a support agent directly in the app through a CPaaS (Communications Platform as a Service) system, or if they don’t want to talk to anyone, they should have access to an in-app information desk. Building an intuitive, AI-driven self-help desk functionality and complementing it with an in-app support tool is a great way to keep the users in the app.
Better predictive analytics and personalization
Artificial intelligence can collect and collate vast amounts of industry and user data, and it can help the app development process by generating insights fast for the dev teams. On the other hand, machine learning can support ongoing improvement and app personalization for specific customer segments and target groups.
Some of the best SaaS ideas are born from artificial intelligence and the reports and industry insights it creates, allowing business leaders to identify the biggest gaps in the market and make the right investment. Even if the idea for the app is nothing new to the consumer, AI can still uncover the best ways to make the app better and more unique than what the competitor’s are offering.
Wrapping up
Mobile app developers are always looking for more efficient and effective ways to get their products finished faster without sacrificing quality, while ensuring high customer satisfaction. Getting everything ready in time for the rollout is one thing, but integrating all the features the modern users need while ensuring next-level functionality and security can be a difficult challenge.
That’s why leveraging low-code AI along with machine learning is becoming an integral and a necessary part of the process. Make sure to implement these technologies in 2023 to shorten your development cycle in 2023 and bring amazing new apps to the market.
