Robotic Process Automation (RPA) Foi salvo
Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is an emerging technology that refers to software that can be easily programmed to perform routine activities that are currently carried out by human workers, in a controlled, flexible and scalable way.
RPA has the potential to transform today’s workplace as dramatically as the machines of the Industrial Revolution changed the factory floor. RPA offering can deliver automated and improved business processes required to make your organization more effective, and increase capacity in your teams.
What processes are suitable to deploy with RPA?
- Prone to error
- Rules based
- Involve digital data
- Highly administrative
Why Robotic Process Automation?
The benefits of RPA solutions go beyond cost reduction and include:
- Decreased cycle times and improved throughput
- Flexibility and scalability
- Improved accuracy
- Improved employee morale – enables them to add more value
- Allows time to innovate and focus on customer satisfaction
- 24/7 availability
As an industry and technology expert, Deloitte understands the need of their customers to move their scarce resources from repetitive work to high added value tasks. Deloitte’s one-to-one solutions help the organizations to improve the efficiency of their businesses, automating and optimizing the operational processes. There are many organizations that can benefit from RPA, it is time to consider it.
How will RPA evolve?
RPA is the first stage of robotization. Bringing together cognitive and learning capabilities, we create solutions that can simulate interaction and human decision-making. But how does Robotics and Cognitive Automation work?
- Involves the automation of human decision processes, intensive, repetitive and knowledge-rich
- Copies human brain strengths, including parallel processing and associative memory
- Enables interaction with humans in voice and text, using natural language processing in structured and unstructured data
- Understands and leverages large real-time data to filter, process and extract key information automatically
- Uses machine learning to develop context-based hypotheses and make reasonable predictions and recommendations based on concepts and correlations
- Converts text, image and voice data into meaningful concepts and relationships