
Three Methods for Developing a Mature Digital Automation Strategy
It is now assumed that organizations must automate their offerings and operations to optimize existing processes and provide better customer experiences and value.
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CIO Applications Europe | Wednesday, March 03, 2021

Organizations must consider their maturity levels to reap the digital automation benefits truly. In addition, they should determine what they require to assess and advance their automation efforts.
Fremont, CA: It is now assumed that organizations must automate their offerings and operations to optimize existing processes and provide better customer experiences and value. However, automating alone does not demonstrate whether the business has maximized its value and efficiency. Therefore, the maturity of the digital automation strategy is more critical than simply automating.
It is critical for IT teams to advance their automation strategy after determining the maturity level of their automation solutions. Here are a few methods they should focus on:
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Connect Applications and Processes
Immature strategies are based primarily on a simple task. While it is a great place to start with automation, it is not conducive to growth. To progress from task-based automation to automated workflows, applications and systems must communicate. They should include linked systems that allow for the creation of increasingly complex end-to-end workflows.
Intermittent Use of RPA
While RPA is a powerful and straightforward tool, it is both a blessing and a curse because it is frequently used when it should not be. As a result, processes are poorly designed.
RPA, designed to mimic human behavior while navigating an application's user interface, has limitations that make it difficult to scale across the organization. For example, the RPA will be impacted if the UI of an application changes. Furthermore, if the organization uses multiple RPA bots that are interdependent, a single UI change can cause the entire process to stop working.
Including More AI Solutions
AI can be beneficial when incorporating less organized data into the automation process. Furthermore, as more data and data science tools become available, predictive models can be easily integrated into the unsupervised automated process for simple decision making. This sophisticated AI implementation can free up many expensive human resources, allowing IT teams to focus on higher-value tasks.
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