Top 3 Mobile Application Development Trends for Mobile App Development Agencies

Mobile applications have been continuously changing our lives for over five years. And because of their enormous usefulness, they are no less than a significant opportunity for small scale to enterprise-level organizations.

As Statista has published, mobile applications have the potential to generate $189 Billion US dollars in profits.

Not only that, but many technical experts have already predicted that the mobile application development industry would have the most significant market shares in the future that depicts this industry will continue to grow in the future.

This year our business analysts at Tektiks Inc have conducted an online survey, and they collected data from the world’s renowned mobile application development companies around the globe to identify and list down promising mobile app development trends.

As per the finding of the survey, here are the topmost promising mobile app development trends that continue to dominate the mobile app development industry in 2020.

  1. The Age of Augmented reality (AR)/ Virtual Reality (VR)

    Augmented reality (AR)/Virtual Reality (VR) can add 3D elements to any live view by using the camera on a cell phone.

    An app like Snapchat and the game Pokemon Go are great examples of augmented and Virtual reality (VR) that provide a complete immersion experience to the users.

    In 2020, AR/VR will not only be limited to game applications; they will also take over the mobile application industry.

    World’s tech giants such as Google and Apple are both already developing and releasing new AR demos on their new devices, which is a testament that AR/VR is going to be a new game-changer in the coming future.

    In fact, both of these technologies have been seen on social platforms like Snapchat and Instagram for branding or targeting potential clients through AR/VR.

    Both these apps have already released their AR-based filters that can change a human face into different funny characters.

    Here are the few examples of AR & VR trends for mobile application development agencies

    • Mobile AR disruption
    • AR in marketing & advertising
    • AR in healthcare
    • AR in manufacturing
  2. Internet of Things (IoT) Based Smart Things

    The word ‘smart things’ or the ‘smart objects’ had been initially considered as the product of a comparatively new technology called the Internet of Things (IoT).

    IoT is basically a synergic network of physical objects that consist of embedded sensors, electronic devices, and custom software to interconnect all the objects into one system.

    For example, smart door locks, IoT based smart homes, Google homes are a few examples of this IoT application development trend in the app industry.

    In a nutshell, the IoT is one of the life-changing technology in the mobile app development sector. Many mobile application companies can harness the power of this game-changing technology. Future IoT based Mobile application trends include:

    • Mobile connected smart objects
    • IoT integrated smart homes
    • IoT enhanced smart areas
    • IoT based self-driving cars
    • IoT based healthcare innovations
  3. Artificial Intelligence & Machine Learning

    Both artificial intelligence and machine learning have become essential in today’s mobile application design and development market.

    For example, ‘Siri’ is developed with the combination of machine learning, and artificial intelligence is now an essential element of any mobile application, and it is inevitable to separate now.

    Many mobile application development agencies and mobile application services providers have already started employing AI and machine learning in application development to grow revenues in the near future.

    Here are Artificial Intelligence & Machine Learning-based future mobile app development trends that can help mobile application development agencies boost their profits in the future:

    • AI automated DevOps through AIOps
    • AI-enabled chips
    • Automated machine learning
    • Interoperability among neural networks

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