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machine learning

11/24/2020 By

How to Avoid Legacy Bottlenecks

While legacy on-premise platforms are known to provide reliable, consistent results for many organizations, one area of limitation is their ability to support some of the most innovative and differentiating technology advancements available. Whether we’re discussing robotic process automation (RPA) or machine learning (ML), most artificial intelligence (AI) initiatives would not be feasible for on-premises applications.

It is the very nature of a cloud environment that made AI opportunities accessible to many organizations. Constellation Research, in its CIO predictions, noted “the push for automation, especially with AI technologies, will expand both within the CIO’s own purview, and become a widespread priority with the most internal stakeholders to-date.”

There is tremendous interest in improving productivity by finding new ways to do more with less. AI, data lakes, and other innovative technologies are automating mundane tasks and uncovering new ways to analyze and aggregate data. By using data sets across the enterprise ecosystem, you can have greater transparency than previously imagined.

But first, you must have a platform that supports these options. In fiercely competitive environments, deploying truly innovative solutions can be profoundly game-changing.

Miller Industries is an organization that overcame those legacy bottlenecks by moving to the cloud, quickly realizing substantial gains. They harmonized data across the organization and provided new dashboards for greater transparency, and reduced manual data analysis by 80%. Miller’s next phase includes using AI and ML to mine this data and leverage machine learning to anticipate future demand in its chassis program. Findings can be shown in automated dashboards, providing the company with a real-time picture of its current and anticipated demand.

Original article written by Infor.

Filed Under: News Tagged With: CIO, CloudSuite Industrial, dashboards, data, erp solutions, Infor, legacy systems, machine learning

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