7 use Cases for RPA in Supply chain and Logistics

Pega Introduces X-ray Vision: The Industry’s First Self-Healing RPA

Available in Q3 2020, X-ray Vision will uniquely solve the two biggest problems RPA users face today by:

  • Automating broken bot maintenance: Pega’s RPA survey found developers spend much more time fixing bots than they expected, making bot maintenance one of the top problems in RPA. Using Pega AI, X-ray Vision will detect when bots break and fix them on the fly, helping to ensure automation resiliency. Machine learning will continually update the AI model to improve how it identifies and fixes broken bots over time. No other RPA solution on the market offers self-healing RPA, which will dramatically reduce the time and resources needed to manage and scale bots.
  • Making complex bot authoring faster and easier: The biggest RPA challenge for organizations? The Pega survey found deploying bots ranks at the very top of list for even the simplest RPA approaches. Automating more complex applications requires more advanced and time-consuming RPA methods, such as manual identification of the application controls. Pega eliminates this manual effort by using AI to automate the control identification. Now RPA developers can deploy the full power of Deep Robotics faster and easier than brittle screen scraping methods.

Read more here: Pega Introduces X-ray Vision: The Industry’s First Self-Healing RPA

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