Our pilot RPA project was a success, but we are now struggling to scale the automation to other departments. What are the biggest technical and organizational hurdles we should expect, and how do we ensure that our bot infrastructure remains manageable as we increase the number of automated tasks?
3 answers
Scaling RPA is less about the bots and more about the Governance Model. The most common pitfall I see is the lack of a centralized Center of Excellence (CoE). Without a CoE, different departments create bots in silos using different standards, leading to a maintenance nightmare. You need to establish standardized templates for error handling and logging from day one. Additionally, many firms fail to account for "Application Drift." If the underlying software that the bot interacts with updates its UI, the bot breaks. You must have a robust change management process to sync software updates with bot maintenance.
Are you using a dedicated orchestrator to manage your digital workforce, or are you running standalone bots on local machines? Management becomes impossible without a central hub for scheduling and monitoring.
Focus on process selection. Not every manual task should be automated. Use a complexity-to-value matrix to prioritize high-volume, low-exception processes for the best ROI.
Patricia is right. Automating a broken or overly complex process only leads to "automated chaos." Process optimization must always happen before the actual bot development.
We are currently using UiPath Orchestrator, but we are struggling with license allocation between attended and unattended bots. We have many seasonal tasks where we need more capacity for just a few weeks. Do you suggest moving to a cloud-based consumption model to handle these spikes in processing?