Robotic process automation (RPA) promises immense cost savings and efficiency gains through automating repetitive, rules-based tasks. However, multiple surveys show over 50% of RPA projects fail to achieve expected ROI. The #1 reason? Suboptimal implementation.
In this comprehensive guide, I outline a proven 26 step approach to RPA rollout based on leading practices refined through 100+ enterprise deployments. Follow this framework to maximize value and minimize risk.
Why Implementation Matters
RPA alone is just software. Real transformation requires changing processes and equipping people. Common pitfalls include:
Not identifying the right processes: Blindly automating broken, inefficient workflows yields little benefit.
Overcustomization: Building complex bot solutions without reusable components drives up costs.
Lack of visibility: Many companies discover too late that bots do not work as expected in production.
Neglecting change management: People are critical for successful adoption yet often an afterthought.
With the right approach, companies can avoid these. The 26 steps fall into 5 phases:
1. Discover: Map and optimize processes (Steps 1-5)
2. Prepare: Select partners and solutions (Steps 6-12)
3. Develop: Configure automation capabilities (Steps 13-17)
4. Deploy: Test, pilot and go live (Steps 18-22)
5. Scale: Expand scope and maintain continuity (Steps 23-26)
Now let‘s explore each step to transform your operations.
Phase 1: Discover – Map and Optimize Processes
Step 1: Conduct Process Discovery with Data-Driven Visibility
Start by deeply understanding existing workflows. Methods like interviews provide limited visibility compared to data-driven process mining. Leading solutions auto-discover processes by tapping into system event logs and databases. Others combine this with AI analyzing employee desktop activity for task-level detail.
Real Example: A Fortune 500 telecom used Celonis process mining to automatically map thousands of process variants from multiple backend systems spanning order management, billing, payments and more. This provided complete visibility unachievable otherwise.
Advanced systems like my company Cognos Analytics deliver interactive process dashboards revealing:
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Actual process flows
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Cycle times with bottlenecks highlighted
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Step-by-step metrics
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Variations
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Data issues
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Automation potential per activity
Such transparency is crucial for identifying top automation candidates worth the effort. It shifts analysis from opinions to facts.
Step 2: Quantify Improvement Potential with Data Models
With processes discovered, data-driven models quantify savings opportunities from:
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FTE Reduction: Using cycle times to calculate human hours automated
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Quality Gains: Correlating errors with activities to size reduction potential
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Compliance Benefits: Flagging audit fail points addressable via automation
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Customer Experience Lift: Linking sentiment to resolution times that bots can improve
We combine our automation benchmarks with each company‘s unique data to tailor expected ROI. This builds the evidenced business case justifying investment.
Example: One insurer combined process mining cycle times with records of policy exceptions. Analyzing this data revealed automating eligibility checks could yield $2 million annually through fewer corrections.
Step 3: Optimize Processes Before Automating
Next simplify and optimize processes before adding automation. Apply data-driven root cause analysis to guide targeted fixes. Prioritize high impact changes measurable through defined KPIs.
Also address upstream issues revealed. For example, one bank discovered duplicate data across systems wasted 1,800 hours yearly. By first implementing a Master Data Management framework, subsequent bots avoided needing complex integrations.
Fixing people and process gaps ensures RPA scales successfully. Rush to automation without a solid foundation risks few sustainability.
Pro Tip: Build an optimization roadmap addressing systems, policies, partners, data and demand management surrounding key processes. RPA sits contextualized within this effort.
Step 4: Define Governance and Process Ownership
Clarify process ownership and ascribe accountability for continuous improvement after bots deploy. Appoint data-savvy leaders per process area over automation initiatives and outcomes.
Back governance through analytics on return per initiative, ranked savings opportunities and trends indicating rising process costs. Data enables proactive management.
Step 5: Plan Infrastructure Needs with Registers
Identify interfaces for ingesting inputs and outputs from bots through a formally defined integration registry detailing:
- Credentials & Access
- API Specifications
- Data Formats & Protocols
- Batch Schedules & Frequencies
Such upfront planning prevents downstream hassles getting bots to interface with surrounding systems. It also aids auditing.
Phase 2: Prepare – Select Partners & Solutions
Step 6: Create a Requirements Backlog with User Stories
With target processes refined, detail functional needs for automation via an Agile backlog organizing user stories which capture who needs what, why and business value. For example:
User Story: As a claims manager, I need auto-categorization of medical records to expedite processing of new filings so we can meet 24 hr resolution targets.
Such granular capabilities inform work estimation, sequencing and prioritization based on business impact.
Step 7: Assign Action Owners
For each capability requirement, designate both a business process expert who can demonstrate current execution steps and an automation development lead accountable for mimicking those actions via configuration rather than custom code.
These subject matter experts (SMEs) will train solutions using their know-how while minimizing technical debt.
Step 8: Determine Sourcing Approach with Data
Consider tradeoffs regarding developing capabilities in-house vs outsourcing to services partners. The latter provides speed by leveraging external expertise but with less customization.
Take a data-driven approach using metrics like internal bench strength, capability gaps vs roadmap and cash flow runway. Calculate tipping points on make vs buy to guide optimal resourcing decisions.
Real Example: One retailer used a resourcing optimizer balancing cost, speed and control to determine where to apply internal build vs external buy across their automation roadmap spanning edges cases in fraud analysis, inventory planning and order brokering.
Step 9: Select a Production-Grade Process Mining Engine
As outlined before, process mining provides immense value throughout automation lifecycles by revealing detailed process analytics not visible otherwise. It helps with discovery, optimization, automation planning, pilot testing, monitoring and ongoing improvement.
Leading mining platforms like my company Cognos Process Mining offer prebuilt connectors accessing data from systems like SAP, Salesforce, Oracle, Workday and more. This accelerates analysis.
Evaluate key dimensions like ease of use, data connectivity, advanced analytics, scalability to tens of thousands of processes, as well as fit-for-purpose industrialization enabling enterprise reliability when choosing your solution.
Step 10: Pick RPA Software Aligned to Use Cases
Now pick your automation platform based on assessment criteria spanning:
- Vendor credibility defined by market share, ratings and reviews
- Licensing models balancing flexibility vs complexity
- Ease and speed of bot configuration
- Tooling for non-developers vs hardcore coders
- In-house vs cloud deployment options
- Intelligent functionality like AI augmentation
- Integration ecosystem including major enterprise systems
Leaders here include UiPath, Automation Anywhere, Microsoft, NICE, Blue Prism and rapid no code solutions like JiffyRPA. Augment these with AI capabilities.
Pro Tip: Check cloud marketplaces like UiPath Activate, Automation Anywhere BotStore and Microsoft Power Automate for reusable components before building from scratch.
Step 11: Embed AI for Complex Task Automation
While RPA handles structured data, smarter solutions are needed to interpret unstructured content like emails, texts, documents, conversations and images.
Continue using RPA for core sequencing while embedding specialized AI engines for complex steps. For example:
- Conversational AI chatbots handling inquiries
- Computer vision reading complex documents
- Predictive analytics anticipating events or needs
- Natural language generation drafting written communications
Leading solutions like IPsoft Amelia and ABBYY FlexiCapture provide such capabilities to enhance automation effectiveness.
Step 12: Curate the Technology Ecosystem
Connect chosen solutions into an integrated automation platform. Leverage APIs and data models defined during planning to assemble components into a seamless environment following defined architecture guiding technology guardrails.
Use orchestration to sequence workflow across tools, manage hand-offs, handle monitoring and deliver a unified interface into the collective suite.
Curate the partner landscape rather than piece-meal plugins. Revisit this platform periodically to swap improved solutions as innovations emerge across fields like computer vision, conversational AI, robotic mobility and smart analytics.
Phase 3: Develop – Configure Automation Capabilities
Step 13: Create Detailed Process Models Aligned with Data
Detail how target workflows will operate end-to-end post transformation through meticulous process documentation.
Model artifacts like process maps, logic flows, data schemas and UML diagrams codify the to-be specifications for development. Annotate steps to define automation ownership across bot types.
Use BPMN standards to visualize steps, inputs, outputs, decisions rules and related systems. Such modeling reduces downstream rework and ensures alignment.
Step 14: Build the MetaBot Controller
A MetaBot sequences orchestrates multiple automation components into an end-to-end process application. Think maestro conducting a complex symphony of automation.
Configure execution logic handling workflow branching, exceptions, notifications, decision escalations and handoffs between systems. Drag-drop workflow designers simplify this automation assembly.
Feed MetaBot historical cases and data to train handling of infrequent scenarios so automation gracefully handles edge cases.
Step 15: Construct TaskBots for Activities
Now construct TaskBots automating specific activities within end-to-end processes. TaskBots execute repetitive work based on rules for structured input/actions.
Import pre-built plugins and components vs coding from scratch to reduce development costs by up to 90%. Have SMEs guide training to mimic human execution. Use tools like TestProject and Selenium to automatically validate accurate logic across expected variations.
Step 16: Develop InsightBots Injecting Intelligence
InsightBots apply analytics models to support automation by:
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Prediction bots anticipating events like credit risk or machinery failures so applications proactively respond
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Intent bots determining goals signaled in customer queries to guide appropriate responses
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Optimization bots prescribing optimal next actions to maximize desired outcomes
Such bots combine flexible rules foundation from RPA tools with turnkey AI capabilities for advanced self-learning.
Real Example: A leading airline uses an optimization bot to dynamically price loyalty upgrades balancing customer lifetime value vs flight demand to maximize margin.
Step 17: Connect Automation to Front-End Channels
Finally, expose automation capabilities through channels like virtual assistants, chat windows, SMS/email bots and self-service portals. This bridges human and digital workforce collaboration.
Options range from basing interfaces on custom code vs configurable templates from leading solutions like AnotherMonday, eGain and Knack. Take mobile-first approach.
Phase 4: Deploy – Test, Pilot and Go Live
Step 18: Integrate Automation Components
With capabilities developed, connect components into an integrated solution following defined architecture plans. Leverage APIs and data models to link systems. Use predefined schema mappings to validate consistent data formats across activities.
Depend on orchestration framework to sequence workflow across tools, manage hand-offs, enforce SLAs and handle monitoring. Deliver a unified management interface into the collective suite. Enable permissions based on roles.
Step 19: Establish a DevOps Pipeline
Implement continuous development best practices and toolchains supporting rapid revisions of automation post-deployment.
This accelerates updates by ratcheting changes up from development, testing, staging and ultimately production. Automate builds, tests, monitoring, notifications and rollback procedures.
Version control and work item management streamlines progression across environments while enforcing separation of duties, compliance and disaster recovery protections.
Step 20: Conduct User Acceptance Testing
Verify automation behaves per expectations using historical test cases and data across expected scenarios like normal operations, exceptions, edge cases and extreme situations.
Solicit user feedback to catch gaps upfront through validation sessions focused on usability. Tools like TestProject, Katalon, Tricentis & more enable test case management and test automation.
Step 21: Launch Pilot for Controlled Rollout
Start small with limited scope, exposing automation to a select user base supporting the target process before expanding. This allows learning in lower risk setting during initial rollout.
Pilot success criteria might cover utilization, accuracy, user feedback scores or cycle time reductions. Compare before/after data with analytics and process mining to validate and monitor pilot progress per defined OKRs.
Step 22: Go-Live and Transition through Change Management
Upon successful piloting measured against targets, open access company-wide supported by executive communications getting leadership to sponsor the launch.
Provide self-paced eLearning modules for users and process owners on updated ways of working with automation integrated. Incent super users and participate in launch events to encourage adoption. Share success stories internally.
Phase 5: Scale – Expand Scope & Maintain Continuity
Step 23: Incrementally Widen Adoption
With the foundation set, progressively transition other suitable process areas to boost ROI. This might follow a 70/20/10 framework:
- 70% on core system stability
- 20% enhancing existing workflows
- 10% net new innovations
Avoid too much change at once. Consider forming a value realization office guiding expansion by applying metrics-based governance measuring return on automation.
Step 24: Map the Hyperautomation Roadmap
Hyperautomation combines technologies like RPA, AI and process mining to rapidly transform operations through digitization, datafication and intelligence augmentation.
Define next level initiatives to streamline fragmented workflows, inject predictive intelligence, modernize legacy systems, socialize institutional knowledge and enhance self-service capabilities.
Cloud plays a dual role both operationally enabling scale while innovatively speeding next generation solutions adoption leveraging hyperscaler expertise like Microsoft Azure AI and Amazon Connect.
Step 25: Launch a Center of Excellence
Institutionalize automation leadership through dedicated internal teams codifying expertise and guardrails to govern adoption.
The Center of Excellence governs solutions, provides flatform tooling, monitors models, handles incidents, measures outcomes and drives improvement initiatives. It acts as an enablement engine accelerating transformation.
Consider hybrid models balancing central authority with distributed tribal knowledge by embedding automation ambassadors across business units.
Step 26: Continuously Improve with Analytics
Apply automation intelligence for self-learning solutions using techniques like digital twin modeling and reinforcement learning.
Set this foundation by leveraging process mining with embedded analytics to monitor performance, detect changes and reveal new opportunities. Add ML for pattern recognition and predictive forecasting.
Listen to user feedback, analyze help tickets and study process adherence via quality management techniques like Six Sigma. Address needs through constant incremental enhancements rather than big bang upgrades.
Let‘s Recap – The 26 Steps to RPA Implementation Mastery
Successful enterprise automation requires coordinated orchestration across processes, decisions, data, systems, partners and people reinforced by sound measurement. Progress expands from foundational understanding into institutionalized change through purposeful scaling.
The 26 step methodology balances business context, technical capability and transformational leadership across five interacting phases. Mastering the full lifecycle, not just software installation, unlocks lasting value.
I hope this guide provides a blueprint to maximize your automation potential in harmony with unique needs. Please share other lessons from your own RPA journey so collectively we lift competency across every industry.
