Intelligent / Cognitive Automation Cognitive automation, also sometimes referred as intelligent or smart automation is the fasted growing field within automation. As automation is mainstream business now, any discussion on RPA would surely get into conversation about Intelligent/Cognitive automation. What is Intelligent Automation While automation is old as the industrial revolution, digitization greatly increased activities that could be automated. Initial tools for automation, which includes scripts, macros (also read: https://www.luceats.com/macros-powershell-scripts-and-rpa/) and robotic process automation (RPA)
Blogs
Robotic Process Automaton (“RPA”) can complement overall data analytics in multiple ways from data creation (data entry to reports & dashboard). In this article we will focus on essentially 2 critical function from data management standpoint. Create Meta Data. As BOTs executes and complete task(s), it creates can create logs or records for audit, analytical and/or diagnostic purposes. This meta data is used for several other purposes to embark next level journey Data enablement in
Business challenge: With the increasing pressure to deliver operational efficiencies, CROs, Pharma, Biotech leaders can use RPA (robotic process automation) to integrate multiple sources of vast volumes of data for evaluation and insights. RPA technology application: Automating highly repetitive manual processes will accelerate the time-to-value and unlock the human workforce to focus on more creative, critical business-centric activities to accelerate organization growth, productivity, repetitiveness, high-volume processes with defined steps and systems are typically good candidates
Digital workforce is still a workforce so at a high level it would follow similar protocol of assessing number of FTE (physical or digital) required to perform specific tasks and activities. From digital workforce perspective it depends on How many business functions Digital workforce will be managing? Are your business processes sequential or can be run in parallel? What are the dependencies across multiple digital workforce? For example, in invoice processing, a bot may scan
