A key focus area for any organization modernizing the workplace is labor-centric automation. A broader intelligent process automation umbrella (along with system-centric automation and decision-centric automation), labor-centric automation focuses on augmenting and simplifying how work is performed across an enterprise has the potential to get significant productivity gains and can Freeing Employees to Add More Business Value than just performing repetitive mundane tasks.
A new set of tools under RPA were probably the most successful generalist automation tools for the modern enterprise. Even as Robotic Process Automation (RPA) is gaining acceptance across many industries. RPA has many horror stories as well. A recent survey showed that >40% of RPA projects fail to deliver expectations in terms of,
The post pandemic world equally saw enormous focus towards digitization and digital transformation across all industries and functions. But equally it put a lot more focus on costs, time and performance since many businesses had their business models questioned and had to adopt to severe cost optimization measures.
“The goal is to reshape work to enable employees to spend their time on activities that add value to the business while automating repetitive, low-value work,
From early use cases largely in the business process outsourcing (BPO) industry, RPA is evolving from first-generation assisted RPA toward next-generation technologies that incorporate artificial intelligence (AI). As defined in a research report from the Everest Group for UiPath, a leading RPA vendor, RPA breaks down into four categories:
Assisted RPA (RPA 1.0) – Deployed on a worker’s desktop, the major objective of this use of RPA is aimed at improving individual worker productivity.
Unassisted RPA (RPA 2.0) – Robots are deployed on a centralized server from which they can be controlled manually. Organizations can leverage RPA in this scenario to automate end-to-end tasks. Also, workflow scheduling/queuing is possible from a central control console.
Autonomous RPA (RPA 3.0) – A progression from unassisted RPA solutions, autonomous RPA can handle more complicated rules-based processes with some built-in decision making, such as handling changed priorities for a more dynamic style of workload balancing.
Cognitive RPA (RPA 4.0) – Building on autonomous RPA, cognitive RPA can integrate with artificial intelligence (AI) technologies such as machine learning and natural language processing. AI would enable RPA to handle the unstructured data of a process and enhance capabilities such as using computer vision to find objects on a screen.
A study conducted on "Future of Work" survey and found that over 80% of the leading organisations have dedicated workplace transformation initiatives in place and many of them including the ones who have implemented RPA are looking to enhance productivity and drive more business results out of their automation roadmap and with predictable business results.
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