Robotic Process Automation

Why do RPA projects often fail during the scaling phase in large enterprises?

KE Asked by Kevin Peterson · 05-08-2024
0 upvotes 13,431 views 0 comments
The question

We had a successful pilot with three bots, but as we tried to roll out thirty, everything fell apart. We are seeing constant credential errors, server timeouts, and high maintenance costs. What are the critical components of an RPA Center of Excellence (CoE) that prevent these scaling issues? 

3 answers

0
PA
Answered on 12-10-2024

Scaling fails because companies treat RPA like a "macro" rather than enterprise software. You need a dedicated CoE to handle Governance, Infrastructure, and Change Management. A common mistake is not having a "Bot Heartbeat" monitoring system; when you have 30 bots, you can't manually check if they are running. You also need standardized "Exception Handling" frameworks (like the Robotic Enterprise Framework in UiPath) so that every bot logs errors in the same way. Without centralized logging and a robust credential vault like CyberArk, your IT security will eventually shut you down. Scaling is 20% development and 80% operational readiness.

0
RI
Answered on 14-10-2024

Are your business process owners involved in the maintenance phase, or is the IT department expected to fix every bot when a business rule changes?

JA 16-10-2024

Richard, that is exactly our problem. Business owners change the Excel templates or web forms without telling IT, and the bots break immediately. We are trying to implement a "Process Change Request" workflow, but it’s slow. How do other companies encourage "Citizen Developers" to take ownership of their own bots while still maintaining the strict governance needed by the IT department?

0
LI
Answered on 20-10-2024

You need a "Control Room" or "Orchestrator" with auto-scaling capabilities. If you are manually starting bots on 30 different VMs, you've already lost the scaling battle.

PA 30-11--0001

Linda is right. Orchestration is the backbone of any scaled RPA initiative. Without a central "brain" to manage work queues and resource allocation, you'll never achieve true efficiency.

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