Scaling RPA: How to overcome maintenance challenges

Many organisations struggle to scale their Robotic Process Automation (RPA) programs effectively due to the ongoing maintenance and support costs associated with existing automation. Teams often find themselves spending too much time fixing outdated bots, rather than focusing on innovation.

In this article, we’ll explore some key challenges faced by RPA programs and how to tackle them to reduce maintenance burdens and enhance scalability.

Key challenges in RPA maintenance

Once a bot is in production, it’s crucial to establish a communication mechanism to notify the end-user or support team when the bot starts, terminates, or encounters an issue. This can be done through emails, SMS, or by raising termination/support tickets on platforms like JIRA or ServiceNow. This ensures that any operational issues are quickly addressed, reducing downtime and preventing larger problems from escalating.

Here are seven major issues that can arise in RPA maintenance:

• Application changes: Updates to the applications that bots interact with often lead to broken automated workflows, requiring continuous support.
• Process evolution: Business processes change which can disrupt the efficiency of existing bots.
• Scope changes: Shifting priorities from management or other stakeholders can alter the scope of a process, requiring bots to be reconfigured.
• Bot upgrades and enhancements: As technology evolves, bots require updates to remain effective.
• Monitoring and performance tuning: Bots need consistent monitoring to ensure they run optimally, often requiring manual adjustments.
• Compliance and security: Bots must be regularly updated to comply with changing regulatory and security standards.
• Scalability: As the number of bots or transaction volumes increases, scaling the RPA solution can become costly and complex.

Addressing application changes

One of the biggest headaches for RPA teams is when applications that bots interact with are updated. To alleviate this pressure, reusable functions should be built and standardised early in the program. For example, if 10 bots interact with LinkedIn and LinkedIn changes its login page, only one reusable login function needs to be updated. Without this approach, each of the 10 bots would require individual updates, significantly increasing maintenance time and costs.

Managing process evolution, scope changes, and bot enhancements

While there’s no magic wand to prevent process evolution or scope changes, these challenges can be managed effectively by:

• Proper documentation: Ensure the Process Definition Documents (PDD) capture all the process details accurately. Tracking recurring changes within departments can help predict future adjustments.
• Ticketing system: Track all change requests using a ticketing system that can be reported to senior stakeholders. This helps prioritise requests and allocate resources effectively.
• Reassess the benefits case: Re-evaluate the benefits of a change against other pipeline projects. Sometimes, improving an existing bot might not offer the same ROI as developing a new process.
• Reuse components: Ensure developers are building bots with reusable components in mind, allowing enhancements to impact multiple bots across the organisation, not just individual ones.
• Challenge changes: Always assess whether the proposed change is truly necessary and whether it will have a significant impact.

Monitoring and performance tuning

Monitoring bots to ensure optimal performance doesn’t have to be resource-intensive. Some key strategies include:

• Automated monitoring: Implement tools that continuously track bot performance and provide real-time alerts, reducing the need for manual oversight.
• Key performance indicators (KPIs): Set clear KPIs for bot performance, making it easier to spot any signs of degradation and take action before issues arise.
• Scheduled maintenance: Regularly scheduled maintenance allows for systematic performance tuning and avoids unexpected failures.
• Load testing: Simulate high transaction volumes to ensure bots can handle peak demand, avoiding performance slowdowns.

Ensuring compliance and security

As regulatory requirements and security threats evolve, keeping bots compliant and secure is essential. Preventive measures include:

• Automated audits: Implement automated compliance audits to continuously monitor bot activities and ensure they meet internal and external standards.
• Security patches: Regularly update bots and infrastructure with the latest security patches and best practices for encryption and data handling.
• Role-based access control (RBAC): Limit access to bots by assigning roles based on necessity, reducing the risk of unauthorised modifications.
• Change management: Establish a structured change management process to track all bot updates, especially those related to compliance.

Overcoming scalability challenges

As RPA programs grow, scalability becomes a major consideration. To scale efficiently:

• Cloud-based infrastructure: Cloud platforms provide flexibility and scalability, allowing organisations to adjust resources based on demand without the overhead of maintaining physical infrastructure.
• Modular bot design: Design bots using modular components that can be reused across different processes. This not only simplifies updates but also reduces maintenance overhead.
• Dynamic resource allocation: Use scheduling and orchestration tools to ensure bots are only active when needed, optimising resource use and keeping costs down.
• Capacity planning: Regularly assess current and future bot demands, and perform capacity planning to ensure infrastructure can support growth without over-provisioning.