We have successfully automated five processes in HR, but our license costs are skyrocketing as we try to expand to Finance. What are some strategies for scaling an RPA Center of Excellence (CoE) efficiently? Is shifting to a "Citizen Developer" model a viable way to reduce the dependency on expensive professional developers?
3 answers
Citizen Development is the future. If you empower the Finance team to build their own small bots using "Low-Code" tools, your CoE can focus purely on high-security, complex integrations.
Scaling effectively requires a shift from dedicated bot machines to high-density robots where multiple bots run on a single Windows Server instance. To manage costs, look into "Bot Insights" to see your utilization rates; many companies pay for licenses they only use 20% of the time. Scheduling bots to run 24/7 across different time zones can maximize your existing investment. Regarding Citizen Developers, it’s a great way to handle "low-complexity, high-volume" tasks, but you must have a strict governance framework in place to prevent them from creating "shadow IT" or broken workflows.
Have you considered moving to a Cloud-native RPA platform? Sometimes the infrastructure savings from not managing on-premise servers can offset the licensing costs of a wider rollout.
Paul, while Cloud RPA saves on hardware, the real cost-killer is often the "orchestration" fees. I've found that using an Open Source RPA framework for the simpler background tasks (like Python-based scripts) while keeping the heavy-duty UI work for paid platforms is a more balanced strategy. This "Hybrid" approach allows you to scale the number of tasks without a linear increase in per-robot license fees, which keeps the CFO happy during budget season.
Spot on, Karen. We started a "Bot-a-thon" to encourage this, and the Finance team ended up automating three of their own monthly reports in just two weeks!