Operating AI-Enhanced RPA with Best Practices for High Performance, Stability, Availability, and Continuous Improvement.
We explore the essentials of implementing Robotic Process Automation across three critical phases, Design, Build, and Run automation. we discover how you leverage modern task and process mining, advanced AI language models like GPT, and AI-driven insights to uncover automation opportunities, to optimize processes, and ensure robust, scalable, and compliant RPA solutions that drive continuous improvement and operational excellence in business.
AI for Run Phases:
- AI strategies for deploying enhanced RPA operations across various departments and functions within the CoE.
- AI best practices for the deployment and management of LLM-driven automation ensuring they deliver consistent performance.
- AI language models improve the accuracy and efficiency of RPA bots, particularly in handling complex tasks.
- AI to focus on maintaining high performance, scalability, and adaptability of RPA solutions helping to meet evolving business requirements.
- AI-driven tools to monitor and manage RPA bots, ensuring they operate efficiently and effectively.
- AI continuous improvement practices with regularly assessment and enhance RPA solutions based on performance data and feedback.
- AI for a robust incident management and predictive troubleshooting using protocols to quickly resolve issues and minimize bot downtime.
- AI for ongoing changes of compliance with regulatory standards and security protocols to protect data and processes across the automation landscape.
- AI helping to provide ongoing support and training to users and ensure they can effectively interact with and benefit from RPA solutions.
- AI to establish performance metrics and reporting mechanisms to track the effectiveness of RPA solutions and demonstrate their value to stakeholders.