The Self-Service Support Model in Robotic Process Automation (RPA)

As organizations mature in their automation journey, there is an increasing demand for models that empower users with more autonomy while reducing dependence on centralized IT or third-party teams. One such approach is the Self-Service Support Model in Robotic Process Automation (RPA). This model allows users to take control of their own automation needs, enabling them to develop, manage, and troubleshoot their RPA bots independently with minimal intervention from centralized support teams.

The Self-Service Support Model fosters innovation, promotes faster execution, and empowers employees to handle their automation requirements, making it an attractive choice for organizations with well-established RPA capabilities.

What is the Self-Service Support Model in RPA?

The Self-Service Support Model in RPA enables business users or citizen developers to independently develop and manage their automation solutions without relying heavily on a central RPA team or external support. The goal of this model is to democratize RPA across the organization, giving departments and teams the tools, training, and resources they need to handle their own automation needs.

In a Self-Service model, users are typically provided with:

- Access to low-code or no-code RPA tools, allowing non-technical users to build bots.

- Guidelines and best practices for creating automations.

- Self-help resources such as knowledge bases, FAQs, and troubleshooting guides.

- Support from automation champions or internal RPA experts when necessary.

This model is particularly suited for organizations with a mature RPA Center of Excellence (CoE), where automation has already been established across different departments and users are familiar with the processes and technologies.

Key Components of the Self-Service Support Model

1. Citizen Development Platforms:

- Low-Code/No-Code Tools: The heart of the self-service model is the availability of intuitive RPA platforms that allow users to design and deploy bots with minimal programming knowledge. Tools like UiPath StudioX/Web, Microsoft Power Automate, and Automation Anywhere’s A2019 provide drag-and-drop functionalities, enabling users to create automation workflows.

- Pre-Built Templates: Users are provided with pre-built bot templates or modules that can be customized to meet their specific needs, making bot creation faster and more accessible.

2. Training and Enablement:

- Self-Service Training: Comprehensive training resources such as online courses, certification programs, and video tutorials help users gain the necessary skills to build and maintain automations on their own.

- Knowledge Base: A central repository of articles, best practices, guides, and FAQs ensures that users can find solutions to common issues quickly without needing to escalate to a central support team.

3. Governance and Compliance:

- Automation Standards: Although users have autonomy, it is crucial that governance frameworks are in place. A clear set of standards and guidelines ensures that all automations comply with security, quality, and performance requirements.

- Approval Workflows: Certain complex or high-impact automations may still require approval from a central RPA team or CoE before going live. This ensures that critical business processes are not disrupted by improperly designed bots.

4. Automation Champions:

- RPA Mentors/Coaches: Some organizations implement an “automation champion” program, where select employees act as RPA mentors or experts. These champions provide guidance and assistance to other users when they encounter difficulties, serving as an intermediate layer of support.

- Community of Practice: A collaborative environment where users share knowledge, troubleshoot issues, and exchange tips on building and optimizing bots.

5. Self-Service Support Tools:

- Self-Help Portals: Organizations often establish a centralized online portal where users can access documentation, video tutorials, forums, and troubleshooting guides to resolve issues independently.

- Chatbots or Virtual Assistants: Some companies provide AI-driven support bots or virtual assistants that guide users through common troubleshooting processes or help them create automations.

Benefits of the Self-Service Support Model

1. Increased Speed and Agility:

- By allowing users to independently develop and maintain their own bots, the Self-Service Support Model accelerates automation initiatives. Teams can implement automation quickly without waiting for approval or support from central IT or the RPA CoE.

- This model enables faster scaling of automation programs as more employees are involved in bot development, reducing bottlenecks in the RPA lifecycle.

2. Reduced Dependency on Centralized Teams:

- The reliance on a central RPA or IT team is significantly reduced, freeing up these teams to focus on more complex, strategic automation initiatives. Users can resolve their own issues, create bots, and make updates without seeking constant support.

3. Empowerment of Employees:

- By putting automation tools directly into the hands of users, the Self-Service model promotes employee empowerment and innovation. Employees can identify automation opportunities within their daily tasks and quickly deploy solutions that improve their own productivity.

4. Cost Savings:

- With reduced dependence on external support teams, organizations can lower the operational costs of managing and maintaining their RPA environments. Additionally, since automations are handled internally by business units, the need for extensive RPA staff can be minimized.

5. Fostering Innovation:

- Employees on the front lines of business operations are often the best sources of innovation, as they understand the pain points and inefficiencies in their workflows. The Self-Service model allows them to directly address these issues, fostering a culture of continuous improvement.

Challenges of the Self-Service Support Model

1. Quality Control and Governance:

- One of the main challenges of the Self-Service Support Model is maintaining consistent quality and ensuring that bots are designed following best practices. Without proper governance, there is a risk of creating poorly designed or redundant automations that may cause errors or disruptions.

- To mitigate this, organizations need to establish robust governance frameworks that standardize how bots are created, reviewed, and deployed.

2. Security and Compliance Risks:

- Giving users direct control over automation development can lead to security risks if sensitive data or critical systems are not adequately protected. Users may inadvertently create automations that bypass security protocols or expose sensitive information.

- To address these risks, security policies and checks need to be embedded into the automation development process, and users should be trained on data privacy and security practices.

3. Skill Gaps:

- Although low-code/no-code platforms make RPA more accessible, some users may still lack the necessary skills or understanding to develop complex automations. This can lead to frustration or failure to achieve desired outcomes.

- Ongoing training, coaching, and access to RPA champions or mentors are essential to help users navigate more challenging automation scenarios.

4. Lack of Centralized Visibility:

- With multiple users developing bots independently, it can become challenging for the CoE or central IT teams to maintain visibility over the entire automation ecosystem. This may lead to redundancy or inefficiencies across different departments.

- Implementing reporting and tracking mechanisms that give central teams oversight of all deployed automations is essential for maintaining overall RPA health.

Best Practices for Implementing the Self-Service Support Model

1. Provide Comprehensive Training and Resources:

- Offering robust training programs and resources is key to ensuring that users are equipped with the skills they need to successfully develop and manage their bots. Ensure that training is available on an ongoing basis and is tailored to different skill levels.

2. Establish Governance Frameworks:

- Clear governance policies should be in place to guide users in bot development. This includes guidelines for bot design, security protocols, and performance standards. Approval workflows can also be established for more complex or high-risk automations.

3. Support Collaboration and Knowledge Sharing:

- Encourage collaboration among users through a community of practice or forums where they can share experiences, tips, and solutions. Automation champions should be readily available to provide additional guidance when needed.

4. Enable Self-Service Support Tools:

- Invest in a centralized self-service portal where users can easily access documentation, FAQs, video tutorials, and support from virtual assistants or AI chatbots. These resources should be user-friendly and continuously updated.

5. Monitor and Measure Automation Performance:

- Although users may be developing and maintaining their bots, it’s essential to have centralized monitoring and reporting in place. This allows the RPA CoE to track automation performance, ensure compliance with governance frameworks, and identify areas for improvement.