Robotic automation’s influence on mobile apps

    Robotic automation’s influence on mobile apps

    The merging of APIs and advanced data analytics engines has enabled enterprise app organizations to address client requirements in a more contextual manner

    CTOs say that new technologies contributed significantly to moving mobile apps to the automation side. As per the client and end-user requirements, mobile apps have been are now more intuitive and smarter than before. This boosts usability and stability for different enterprise requirements and general public needs. Today, mobile apps can enable a wide range of daily tasks, with extensive automation and very little human involvement.

    AI and chatbots have helped update mobile apps towards automation for enhanced usability and user experience. App-fueled automation has become the dominant force behind enterprise mobile app policies and strategies.

    The proliferation of automation based Apps for marketing focus

    Organizations have deployed chatbot across most instant messaging platforms like Slack, iMessage, Messenger, Telegram, etc. Such platforms are implemented by most major organizations to always keep in touch with their clients for feedback and support via the automated chatbot. The integration of intelligent chatbots with messaging platforms has transformed the methods of client service implemented by enterprises.

    CTOs point out that machine learning (ML) models and AI have played critical roles in popularizing the concept of chatbots and ensuring the effectiveness of the tool. Conversational bots are capable of carrying out relevant and practical business interactions. Intelligent chatbots are capable of identifying the user intent and suggesting solutions to be reduced and implemented as per the need of the organization or client. Instagram has deployed AI to display content as per the end-user’s preferences and intent.

    Read More: CMOs Leverage AI-Powered Data Analytics to Drive Better Strategies

    Trends of mobile app automation

    Organizations say that automation will have a significant impact on the industry at a global level. The real expanse of the tech can be understood by focusing on the upcoming trends. Most traditional jobs that required repetitive tasks in the enterprises will be transferred to automation tools. Mobile apps automation will be achieved by the Internet of Things and the connected network of applications, sensors, and devices.

    Prompt customer service will be possible for enterprises via intelligent chatbots. AI, ML, and data-based insights will help develop and deploy mobile apps across enterprises. Remote monitoring and intelligent real-time analytics will enhance client service interactions,   and smart apps will help level the playing field for startups and smaller organizations.

    Bridging the skill gap

    CIOs acknowledge that lack of resources and trained personnel have left organizations clueless about implementing automation tools and manipulating them for business advantage. Most organizations tend to run on the old, non-updated IT hardware; thus, the employees are unable to upskill themselves with the latest tech. This creates a significant obstacle in ensuring widespread utilization of automation tools.

    Read More: Financial Services to Humanize Products Using Data Analytics

    Tools like Platform as a Service (PaaS) and Software as a Service (SaaS) designed especially for the enterprise will be instrumental in adopting the latest tech, tools, and automation platform.

    Moving beyond the Rule-based algorithm

    IT leaders believe that majority of enterprises stick to the rule-based algorithm when dealing with automation tools. This helps to ensure predictable output in a controlled and known situation. A rule-based algorithm may be the building block for almost all automation platforms, including the mobile app automation tools; however, it’s high time for enterprises to shift to more intuitive solutions and analytics methods. This shift has been helped by tech like Natural Language Processing (NLP) and ML.

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