Robotic Process Automation (RPA) usage is accelerating across global enterprises, with revenues forecasted to reach $13.74 billion by 2030 according to Grand View Research. RPA adoption is being fueled by quantifiable cost savings, with up to 30-50% reductions in process costs according to McKinsey.
But simply investing in the software alone will not guarantee results. The key is to approach RPA as an opportunity to digitally transform operations, which requires understanding change impacts spanning people, process and technology.
This comprehensive guide outlines the end-to-end RPA implementation journey in 12 key steps:
1. Build a Strong RPA Business Case
The first critical step is justifying RPA by building a compelling business case customized to your organization and stakeholders. Quantify the expected benefits and cost savings in relation to upfront software, services and training investments.
| Metric | Sample RPA Benefits |
|---|---|
| Cost Reduction | 35% by eliminating manual effort |
| Increased Throughput | +50% with 24/7 automation |
| Improved Accuracy | 60% reduction in errors |
| FTE Savings | 4 FTEs redirected to higher value work |
| Payback Period | 200% ROI in first year |
Highlight productivity constraints that automation can address. Showcase best practice examples and success metrics from peers in your industry. Tailor messaging to leadership priorities whether improved customer experience, reduced operational risk, or scaling without proportional headcount growth. Demonstrate how RPA can deliver against these strategic goals.
2. Define Your RPA Strategy and Operating Model
With leadership aligned on the RPA business case, next focus on defining the strategy and operating model required to drive success.
Clearly outline the intended breadth of automation programs – will RPA be applied broadly across business units, or only within certain functions? Set guidelines regarding centralized vs decentralized deployment models.
Determine what mix of technical resources will be leveraged – will development and management be led centrally by IT, or will citizen developers across business units be enabled? What existing roles can take on testing and support responsibilities?
Document an enterprise-level plan detailing:
- Governance Policies: Standards for development, testing, support, change control, audits
- Funding Model: Centralized or federated budget pools
- Training Curriculum: Required technical, solution, business skills
- Communications Approach: Stakeholder messaging strategy and forums
These foundations uphold consistency, rigor and sustainability as programs scale.
3. Select Your RPA Software Platform
The platform selected serves as the technical foundation underpinning automation capabilities and support for hundreds of bots across the enterprise. Balance assessment criteria between business and IT priorities:
For Business Users
- Ease of use to foster citizen development
- Process analytics to identify automation opportunities
- Prebuilt content to accelerate development
For IT Leaders
- Enterprise-grade administration, auditability, and security
- Scalability and availability to support high volumes at scale
- Analytics and reporting to manage and optimize deployment
Also assess vendor financial viability, pricing models, implementation services, training resources, and customer support availability.
While the above focuses on commercial RPA platforms, explore alternatives like integrating existing tools or leveraging fully code-based options depending on technical strengths within your organization.
4. Appoint Your RPA Center of Excellence (CoE)
An RPA Center of Excellence represents the central team chartered to drive strategy, standards, governance and best practices. Appoint members across IT, Ops and business groups to cover all perspectives.
Typical CoE roles include:
- RPA Program Manager: Directs vision, stakeholder engagement, funding
- Development Manager: Oversees build, deployment, maintenance
- Business Analysts: Analyze processes, document requirements
- Trainers: Design training content and deliver to users
- Admins: Manage software, infrastructure, access
Leverage shared services model for maximum efficiency, with CoE providing guidance, tools, templates and training to project development teams executing automation initiatives for assigned business areas.
5. Analyze Processes for Automation
With foundations established, shift focus to the critical process analysis and discovery phase. Key goals are to:
- Identify Pain Points: Where do process bottlenecks, exceptions, and quality issues occur?
- Understand Step Details: What are all inputs, actions, rules, and outputs for each step?
- Document Current State: How long do activities take? What applications and systems are used?
- Map To-Be Future State: How could steps be simplified or flow improved prior to automation?
A combination of methodologies provides comprehensive perspective:
- Stakeholder interviews covering process context, pain points, improvement ideas
- As-is process mapping workshops with frontline personnel
- Mining tools to capture systems and identify automation feasibility based on structured vs unstructured data, integration needs etc.
From analysis, prioritize the initial automation roadmap around optimal viability and business impact.
6. Develop Business Requirements and User Stories
With current-state processes well documented, business analysts can detail functional requirements that guide development. Capture specifications covering:
- End-to-End Process Workflows: Step-by-Step activities, decision points and associated rules
- Supported Applications and Systems: List of apps the automation will access with example data fields/inputs to be entered or read
- Technical Dependencies: APIs, libraries, credentials required
- Inputs and Outputs: Specific format of data the automation solution needs to consume and produce
- Business Rules: Detailed conditional logic, computations, data validation checks, error handling required
- Testing Requirements: Format and specifics of test dataset requirements so the solution can be adequately tested
Document requirements within user stories reflecting how end business clients will actually utilize and interact with the automation. This technique connects development directly to customer needs.
7. Set Up Development, Testing and Deployment Environments
Establishing integrated environments, tools and systematic release processes upfront enables efficient, scalable delivery of quality solutions. Best practices include:
- Source Control: Centrally manage solution versions and code
- Parallel Development Environments: Duplicate real-world system configuration and test datasets without risking production impact
- Build Server: Automatically assemble solutions by pulling latest code, configurations, resource files from source control
- Release Management: Orchestrate movement of solutions between development > test > staging > production promoting only those meeting quality gates
- Testing Tools: Incorporate unit test automation frameworks to execute mass regression testing
Such DevOps capabilities help developers rapidly build solutions while insulating business risk.
8. Develop Automation Solutions
With requirements established and supporting toolchain in place, developers can start configuring automation solutions using the selected RPA platform.
Begin with a few quick-win processes meeting key selection criteria:
- Repeatable, rules-based tasks vs unstructured decisions requiring judgement
- High throughput processes where automating portions can significantly reduce overall cycle times
- Multi-system processes requiring significant repetitive manual movements and entry between apps/websites
- High payback contribution where even 50-75% automation can yield tangible savings from eliminating manual FTE capacity
Develop solutions iteratively using agile sprints matching complexity. Continually engage business representatives for rapid feedback to ensure alignment.
9. Conduct User Acceptance Testing
The most valuable testing leverages business teams on the frontline of these processes to generate mass test cases based on real scenarios. Develop test plans methodically evaluating:
- Happy Path: Process working end-to-end with valid, expected inputs
- Sad Path: How does solution respond to incomplete input data?
- Boundary Testing: Exercise threshold values for every numeric field
- Negative Testing: Introduce invalid values into inputs to validate handling
- Error Condition Testing: Can solution recover from application failures or exceptions?
Document defects, re-test fixes, and baseline performance prior to release.
10. Deploy and Pilot Solutions
Employ incremental rollout starting from lower risk/lower visibility processes. Define pilot entrance/exit criteria spanning:
- Quality Metrics: Pass threshold for defects, system test cases
- Performance SLAs: Meet speed, right-first-time KPIs
- Business Validation: Peer review signoff assessing functionality
Gather continuous feedback during pilot phase. Does the solution behave as intended? Are KPIs/ROI tracking to targets? Refine extensively before expanding reach.
11. Manage and Support Solutions
Apply centralized model for performance monitoring, change control and break-fix support leveraging the RPA Control Room and transaction logs.
Monitor throughput levels, failure rates, mean time to repair and compliance with defined SLAs daily using dashboards highlighting trends. Trigger alerts for unexpected deviations.
Have dedicated Level 1 support resources to rapidly respond to bot issues from business users as required. Level 2 and 3 support involving development should focus on system defects, enhancements and improvements.
Continually expand use case breadth and depth. Identify adjacent processes suitable for automation to maximize leverage across fixed platform costs.
12. Scale Across The Enterprise
Once proven for early pilots, validate that to-be target operating models involving structure, staffing, support and governance practices can sustain pace at scale.
Expanding reach requires assessing change readiness across people, process and technology:
- People Change: Do teams have sufficient skills for citizen development/support?
- Process Change: How is feedback being incorporated into redesigned workflows?
- Technology Change: Can infrastructure, security, networks scale gracefully with 500+ bots?
Incorporate feedback, augment teams, evolve governance and upgrade platforms as needed to uphold quality standards demand grows. Carry automation into every suitable corner of operations for material enterprise impact.
Overcoming Common RPA Implementation Pitfalls
While following proven implementation steps aids success, organizations still face common pitfalls that can inhibit realizing full RPA potential:
Selecting Overly Complex Initial Processes
Starting with intricate processes involving multiple exceptions risks delaying ROI and diminishing confidence during critical pilot stages. Begin with simpler workflows.
Insufficient Change Management
Underestimating impacts on staff skillsets, role evolutions and communication leads to pushback and skepticism. Invest heavily upfront in organizational change management.
Technical Debt Accumulation
Rushing development without governance on quality standards, testing rigor, documentation and modular code practices slows enhancements long-term. Commit to quality from outset.
Weak Analytics Practices
Lacking mechanisms to track operational metrics pre and post-automation inhibits quantifying outcomes and optimization. Build reporting globally across programs.
Keys to RPA Implementation Success
Those achieving the most substantial and sustainable impact from RPA typically exhibit several common best practices in their implementation approach:
Integrate AI To Enhance Automation
Combining RPA with AI techniques like machine learning, natural language processing, computer vision and predictive analytics leads to more intelligent, self-learning automation capable of mimicking human judgement for more sophisticated use cases. Encourage developers to continually explore opportunities to embed analytics alongside automation.
Maintain Focus on People
Keep staff skills, preferences and change appetite central when designing operating models. Over half of automation benefits stem from process improvement and liberating talent, not just technology alone. Closely couple RPA efforts to talent strategies including reskilling programs.
Foster Culture of Continuous Innovation
RPA is not a one-time project, but rather a platform providing unlimited potential for ongoing reinvention of operations. Build a culture recognizing automation capability as a key operational competitive advantage to be continually expanded vs an transient cost play.
Develop Platform Expertise
While third parties can supplement skills short-term, sustainably scaling RPA ultimately requires cultivating internal technical experts within the platforms and technologies underpinning solutions. Strategically hire and retain specialist resources.
Pragmatically Expand Governance
Balance initially getting programs moving with maturing governance spanning quality, testing, support and change control standards over time. Guard against early bureaucracy that limits creativity and speed.
Learn and Evolve From global Peers
Leading innovators exchange ideas, standards and templates to mutually advance RPA best practices through communities of practice and centers of excellence forums. Actively participate to adopt lessons learned from those ahead in the journey.
Conclusion
RPA delivers transformative value. But Sydney Health learned the hard way that simply buying software is not enough. Lacking solid foundations throughout strategy, governance, testing and support, their initial RPA deployments incurred runaway technical debt. Lofty expectations were quickly overwhelmed by complex exceptions. Defects abounded. Benefits lagged goals.
By embracing RPA as a capability requiring cross-functional maturity – not just a quick automation play – Seattle Health set their course differently. They invested in Centers of Excellence, integrated AI, focused on culture and worked side-by-side with implementation experts. Within 18 months Seattle Health had rebuilt global operations, improved quality and optimized costs through hundreds of software robots.
The lessons are clear – dedicate upfront to the key foundations outlined across the 12 RPA implementation steps above and sustaining impact can be unlocked at scale.
So go forth and pursue full automation potential – the insights and checklists compiled here pave the path to success.
Let me know in the comments if further insights are desired related to RPA or AI implementations from an analytics perspective!