September 26 2021
The implementation of RPA will reduce the workload and cost of IT asset management.
The robot can work efficiently 24 hours a day, and the processing speed is more than 8 times of that of human, and it can hardly make mistakes, which can effectively avoid mistakes caused by human operation.
Flexible expansion capability and “non-invasive” enable RPA to integrate across departments in multiple application systems, and automate cumbersome processes. In case of any problem, the robot will immediately report an error to ensure that it asset management gets timely feedback from all departments. RPA is a good assistant for analysts. It can replace them to perform redundant tasks, reduce workload, and give analysts more energy to make more informed decisions, so as to reduce the cost of purchasing it assets.
AI makes unstructured data management more “digital”
Software or hardware asset applications often require other legal processes to process approvals, signatures, contract negotiations, license modifications, and clarifications. The tasks related to these processes include unstructured data or images in e-mail, voice mail, and chat robots. At this time, RPA alone is not enough, but also need to use AI for unstructured data management. RPA can transfer unstructured raw data to AI, which processes the data and sends the results back to RPA. RPA then links the results to the appropriate IT asset execution process. This enables fast processing of service requests from chat robots, email, and voice mail.
RPA + AI make it compliance audit more accurate
IT asset audits ensure that businesses comply with software license agreements and regulations. In order to ensure compliance, enterprises should conduct self audit regularly. But it often takes a lot of time and resources. Many enterprises have no comprehensive IT asset management, which makes it difficult for them to conduct self audit, so they can only rely on the audit report put forward by the external auditor. It asset management combined with RPA + AI can minimize or eliminate the manual redundant tasks in the process of enterprise self-audit.
In this way, internal auditors can focus more time and energy on the analysis results, help them grow into analysts, and provide more valuable insights for enterprises.