Process automation has long helped businesses save their employees from tedious tasks, letting users focus on value creation. Machine learning systems housed within enterprise app platforms can take automation to another level, providing a vital layer of intelligence and progress that is only achievable when key data is integrated across diverse lines of business.
Businesses could eventually use machine learning to redefine their operational strategies, allowing intelligent devices, robots and similar solutions to make key decisions in more nuanced ways. For example, a CIO Review report highlighted matters such as identifying credit card fraud and performing risk modeling as potential applications of machine learning. These represent critical tasks that organizations are already working to improve using data. Where a system now may identify specific, human-set parameters pertaining to credit card purchases in order to identify fraud, thissetup can adjust on the fly based on shifting data patterns to make more intelligent decisions.
This may sound a bit like science fiction, but itis already valuable for highly specific applications.
"Machine learning is already valuable for highly specific applications."
While ambitious machine learning projects may sound a bit far-fetched at the moment, the technology is already gaining a foothold when intelligent devices are given specific tasks to complete. A Credit Union Insight report pointed to Facebook adjusting users' feeds based on their operating patterns and iPhone keyboards adjusting the sizes of keys on the touchscreen to reflect user typing patterns as key examples of this trend.
Machine learning may have a way to go before it can solve big problems, but it is already ready to help with specific enterprise workflows, and businesses that implement app platforms have a head start.
App platforms enable organizations to align process and data workflows in streamlined ways using low-code tools that simplify app customization. Data is brought together from diverse interconnected sources within app platforms, and this integration creates a vast repository for machine learning systems to pull from. With the data coming together in one cloud platform and apps able to be changed based on shifting user needs, businesses can easily solve key workflow problems by applying this to the issue.
This way of learning is ready to help businesses automateprocesses that don't truly need a human touch, but are a bit too complex for traditional automation tools to handle. App platforms provide a central hub for this activity to take place, empowering users to optimize processes around modern machine learning tools.
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