Examples of machine learning in business processes range from phone and chat wizards to systems that can sort and classify customer orders in a work flow. More advanced systems can augment supply chain activities by adjusting inventory based on order parameters, lead times, and demands. These are just a few of the examples where machine learning can be applied. A more general view is any step where information is collected and sorted then acted on using set criteria are good candidates for ML.
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If you are talking about business process re-engineering, then RPA based automation is based suited for well-defined routine jobs. ML can be used to provide more valuable insights as part of the RPA process based on which you can take various decisions. This depends on the use cases we are trying to solve. E.g. Say in case of banking, where RPA is used for KYC process or say underwriting process, ML can be used to provide predictive score based on which RPA process can take better informed decisions.
Director at a tech services company with 1,001-5,000 employees
Real User
May 27, 2020
In business processes ,there are many decision nodes.if the decision is done through set of rules,you can bring in machine learning to evolve this decision process.If you are collecting the metrics,you can predict these metrics and forewarn the stakeholders. In summary if the data is available ,then you can apply ML to make sense of it.
Data Science Platforms facilitate data analysis, machine learning, and predictive modeling. These platforms integrate tools and libraries, streamlining the workflow for data scientists.Data Science Platforms support a wide range of data analytics and machine learning tasks, making them essential for data-driven decision-making. They offer robust tools for data preparation, model building, and deployment. By integrating with various data sources and supporting multiple programming languages,...
Examples of machine learning in business processes range from phone and chat wizards to systems that can sort and classify customer orders in a work flow. More advanced systems can augment supply chain activities by adjusting inventory based on order parameters, lead times, and demands. These are just a few of the examples where machine learning can be applied. A more general view is any step where information is collected and sorted then acted on using set criteria are good candidates for ML.
Hi Rony,
If you are talking about business process re-engineering, then RPA based automation is based suited for well-defined routine jobs. ML can be used to provide more valuable insights as part of the RPA process based on which you can take various decisions. This depends on the use cases we are trying to solve. E.g. Say in case of banking, where RPA is used for KYC process or say underwriting process, ML can be used to provide predictive score based on which RPA process can take better informed decisions.
Hope it helps.
In business processes ,there are many decision nodes.if the decision is done through set of rules,you can bring in machine learning to evolve this decision process.If you are collecting the metrics,you can predict these metrics and forewarn the stakeholders. In summary if the data is available ,then you can apply ML to make sense of it.