Robotic Process Automation (RPA) is transforming how organizations operate by automating repetitive, rule-based processes. RPA software bots can help improve efficiency, reduce costs, and enhance quality by taking over manual tasks from human workers.

However, implementing RPA requires careful planning and strategic framework development to maximize value. This guide will provide beginners with an introduction to building a robust RPA framework.

What is RPA and How Does it Work?

RPA tools perform repetitive tasks by mimicking human actions and interacting with system interfaces similar to how humans would. Bots log into applications, enter data, calculate figures, copy and paste data between systems, extract information from documents, and complete transactions.

RPA software works by combining UI automation with business logic and workflows. The bots execute tasks by following clearly defined rules and processes set by human programmers.

Key benefits of RPA include:

  • Improved efficiency by automating high-volume, repetitive tasks
  • Reduced costs by lowering labor expenses
  • Enhanced quality and accuracy by eliminating human errors
  • Increased scalability to handle higher workloads
  • Quicker processes by performing tasks faster than humans

RPA tools perform best in environments with highly repetitive tasks, clear business rules, and electronic interfaces. They excel at automating high-volume back-office processes in HR, IT, finance, and customer service.

Elements of an RPA Framework

A strong RPA framework provides the foundation for a successful automation program. Key elements include:

Process Identification and Selection

The first step is identifying automation opportunities by assessing organizational processes against defined criteria. Ideal processes have these characteristics:

  • Repetitive, high-volume tasks
  • Rules-based decisions or data processing
  • Manual data transfer between systems
  • Prone to human errors
  • Time-consuming for employees

Once target processes get identified, they undergo further evaluation based on factors like ROI, ease of implementation, and potential value. The most promising processes get selected for automation.

Technical Feasibility Assessment

The technical feasibility analysis examines if target processes can get automated with RPA tools. This evaluation checks:
  • System interfaces - Assess if they allow bot interaction
  • Application accessibility - Check if bots can log in and access functionality
  • Integration needs - Identify interfaces between systems
  • Exception handling - Evaluate how bots respond to system errors

Processes that pass technical screening proceed to building the automation solution. Those that don't may need other automation approaches.

Solution Design

Solution design maps out the automation workflow, technology architecture, and infrastructure needed. Key activities involve:
  • Documenting the as-is process flow
  • Designing the to-be automated workflow
  • Selecting the RPA tool(s)
  • Modeling the system architecture
  • Provisioning infrastructure (servers, storage)
  • Developing the testing strategy

Thorough solution design sets the direction for building a reliable, scalable RPA program.

Bot Development

Bot development configures software bots to perform the required tasks. RPA developers use the RPA tool to:
  • Program rule-based decisions, calculations, and data processing
  • Build process workflows and business logic
  • Integrate bots with systems like ERPs
  • Set up application-specific keyboards and screen scrapers
  • Establish connections and handoffs between multiple bots

Extensive testing occurs throughout bot development to ensure accuracy.

Testing and Quality Assurance

Testing verifies the automation solution against expected requirements and outcomes. RPA testing activities include:
  • Functional testing - Validate workflow, results, and integration
  • Regression testing – Confirm existing capabilities after changes
  • User acceptance testing – Check usability for business users
  • Load testing – Assess performance under projected transaction volumes
  • Failover testing – Evaluate robustness and fault tolerance

Bots that pass all tests get deployed to production.

Deployment and Monitoring

Bot deployment rolls out the automation to production systems. A controlled rollout helps identify deployment issues before full implementation.

Post-deployment monitoring oversees bot activity in real time using RPA dashboards. Monitoring helps:

  • Track bot uptime and task throughput
  • Log errors for troubleshooting
  • Collect performance data
  • Identify improvements

Active monitoring ensures bots operate reliably and deliver the desired ROI.

Maintenance and Enhancement

Like other software programs, RPA solutions need ongoing maintenance and enhancements. Key activities include:

  • Bug fixes and patches
  • Upgrades to newer RPA tool versions
  • Addition of new functionality
  • Integration with other emerging technologies like AI
  • Reconfiguration when source systems get updated

Proactive maintenance and innovation keep automation capabilities in sync with changing business needs.

Developing a Framework and Operating Model

Organizations need an effective framework and operating model to build, operate, and scale their RPA programs successfully.


Strong RPA governance provides oversight and strategic direction across three areas:

1. Policies:

  • Automation strategy
  • Business case modeling
  • Vendor selection
  • Lifecycle management
  • Risk management
  • Compliance and controls

2. People:

  • Role definitions
  • Organization structure
  • Cross-functional collaboration


  • Tools and platforms
  • Infrastructure
  • Software engineering
  • Information security

An RPA Center of Excellence (CoE) typically oversees governance.

Delivery Model

The delivery model defines how to structure, source, and execute automation programs:
  • Insourcing: Internal IT group handles everything
  • Outsourcing: Third-party provider develops solutions
  • Hybrid: Mix of internal and outsourced resources

Organizations must weigh factors like capabilities, capacity, and cost to choose the optimal model.

Operating Model

The operating model establishes the ongoing execution of RPA programs across these dimensions:

Roles and responsibilities - Defines automation tasks for business analysts, RPA developers, IT support, project managers, and process owners

Workflow - Specifies handoffs between assessment, development, testing, deployment, monitoring, and maintenance

Tools and systems - Determines software and infrastructure for authoring bots, managing deployment, monitoring operations, and securing data

Support model - Sets up service desk and user support for business users and RPA technical team

Continuous improvement - Implements mechanisms to refine the framework, enhance productivity, and expand automation

Organizational Change Management

Effective change management helps users across the organization embrace RPA:

  • Communicate RPA vision, approach, and timelines
  • Provide RPA training customized to user groups
  • Actively involve business teams in solution design
  • Recognize quick wins to build momentum
  • Solicit user feedback to guide enhancements
  • Redeploy displaced labor by upskilling and retraining

Organizations can maximize adoption and realize RPA's full benefits with thoughtful change management.

Key Takeaways for Developing an RPA Framework

For new RPA adopters, keeping these best practices in mind will help establish a solid automation foundation:

  • Start with a well-defined RPA strategy aligned to business goals
  • Institute strong governance and oversight before launching pilots
  • Thoroughly assess candidate processes and select the most viable for automation
  • Conduct technical feasibility analysis to confirm process automation viability
  • Design comprehensive solutions before bot configuration begins
  • Follow a structured bot development, testing, and deployment protocol
  • Implement robust monitoring and support post-deployment
  • Plan for ongoing maintenance, improvements, and innovation
  • Define clear framework components, operating model, and organizational roles
  • Manage organizational change to drive RPA adoption across the business

With meticulous planning and preparation, RPA novices can set their programs up for success and significant ROI. This well-architected framework will accommodate scalable growth as skills, resources, and use cases expand.


As this guide outlines, developing a thoughtful framework and operating model lays the groundwork for RPA success. However, building and managing an automation program requires significant expertise and cross-functional collaboration. For many organizations, partnering with an industry leader like Worksoft can accelerate results and maximize ROI.

Worksoft offers the next-generation intelligent automation platform for end-to-end business process discovery, testing, and automation. Our codeless, low-code solution delivers quick time to value along with enterprise-grade scalability, security, and centralized governance.

With Worksoft, organizations can jumpstart their RPA initiatives, achieve rapid automation, and continuously innovate and optimize ongoing operations. To learn more about unlocking the power of true intelligent process automation, contact Worksoft today.

Our automation experts can assess your needs and demonstrate how Worksoft’s enterprise-ready platform can drive your digital transformation forward.

Frequently Asked Questions

Q: What are some key things to consider when selecting processes to automate?

A: Look for high-transaction volumes, repetitive steps, rules-based decisions, manual data transfers, and prone to human error. Also assess ROI, ease of automation, and potential business value.

Q: What role does change management play in an RPA program?

A: Critical role in getting user buy-in across the organization. Needs tailored communication, training, user involvement in design, recognizing quick wins, soliciting feedback, and redeploying displaced labor.

Q: How should the success and ROI of RPA programs be tracked?

A: Through metrics like process cost, cycle time, productivity, quality, compliance, customer satisfaction and monitoring bot uptime, throughput, and errors.

Q: What are some risks to consider regarding RPA?

A: Technology risks like bot failures, lack of scalability, and unsecured data. Business risks like unclear ROI, ineffective change management, and lack of ongoing support.

Q: What is the best practice for starting with RPA?

A: Begin with a pilot of 1-2 simple automations to test capabilities and build momentum before expanding. Look for quick wins to demonstrate RPA value early on.