Tech & Skills required for RPA Success in 2024: In-depth Guide

Robotic Process Automation (RPA) has rapidly emerged as one of the most disruptive enterprise technologies of the decade. According to Gartner, RPA revenue grew over 63% to $1.4 billion in 2019. The COVID-19 pandemic has further accelerated demand, with over 90% of organizations now using RPA in some form. Leading analyst firm Forrester predicts the RPA market will reach nearly $12 billion by 2023 as adoption continues rising exponentially.

With this meteoric growth, many companies have rushed into RPA implementations aiming to reduce costs and gain efficiency through automation. However, simply adding software bots alone does not guarantee value. To truly transform operations, organizations must take a strategic approach backed by the right blend of complementary technologies, analytical abilities, and technical skills.

This comprehensive guide examines the essential elements required to successfully scale RPA in the enterprise and avoid common pitfalls. It provides thorough and pragmatic guidance for business and technology leaders looking to capitalize on intelligent automation.

The Transformative Potential of RPA

RPA tools function by mimicking human actions and interacting with digital systems in the same way human workers do. For instance, bots can log into applications, move files across systems, extract data, fill in forms, and complete many other repetitive, rules-based tasks.

The main benefits of RPA include:

  • Cost reduction: Automating high-volume, repetitive manual work minimizes labor expenses. Bots can work 24/7 without rest. According to Deloitte, RPA yields 20-25% in cost savings on automated processes.

  • Speed and accuracy: Bots execute tasks faster and with fewer errors than human workers, leading to dramatic efficiency improvements. McKinsey found RPA improves process speed by 20-50% and reduces errors by 33-50%.

  • Compliance: Bots perform tasks precisely as programmed every time, minimizing costly errors and improving compliance.

  • Scalability: Digital bots can scale rapidly to take on higher workloads without hiring additional FTEs.

  • Improved EX: RPA liberates workers from dull, repetitive tasks so they can focus on higher-value work. 79% of early adopters have reinvested RPA savings into workforce upskilling and change management.

Leading organizations like Walmart, P&G, Chevron, and FedEx have leveraged RPA to automate HR onboarding, supply chain management, finance processes, and customer interactions at massive scale. The COVID-19 pandemic has further necessitated cost optimization through automation. Adoption will only accelerate as more companies realize RPA‘s tangible benefits.

Choosing the Right Processes to Automate with RPA

While RPA can streamline many different types of processes, it is not a silver bullet suited for every workflow. Organizations should strategically identify processes that will gain the most benefit from automation based on specific criteria:

Volume

  • High-volume, repetitive tasks bring the biggest ROI from automating with RPA. Good examples include report generation, claims processing, and invoice reconciliation.

Prone to Human Error

  • RPA helps eliminate errors by doing tasks precisely as programmed. Processes like data entry and document review see huge quality gains.

Time Sensitivity

  • RPA accelerates processes and tasks that are time-critical. Applications like loan underwriting and customer service interactions benefit from faster bot handling.

Process Stability

  • Unpredictable processes that change frequently are unfit for RPA. Bots cannot easily adapt. Well-defined, stable workflows are better candidates.

Subjective Decision Making

  • Bots cannot make context-based decisions or interpret nuances. Processes requiring analysis or complex judgments are unsuitable.
Unsuitable Processes Well-Suited Processes
New product development Claims processing
Writing reports Account reconciliation
Strategic planning Invoice matching
Negotiating deals Onboarding new employees
Resolving IT incidents Spot-checking documents

Fig 1: Examples of processes that are unsuitable vs. well-suited for RPA automation. The most value comes from automating high-volume, stable tasks.

Thoroughly assessing processes using techniques like process mining helps organizations identify where automation will provide the highest ROI. RPA success starts with the right process selection.

Step-by-Step RPA Implementation Guide

Implementing RPA technology involves much more than simply purchasing bots. The table below provides an overview of the end-to-end implementation process:

Phase Key Activities Best Practices
Process Analysis – Map out existing workflows
– Identify pain points
– Uncover automation opportunities
– Conduct workshops with stakeholders
– Leverage process mining for objective insights
Business Case – Calculate ROI projections
– Model costs vs. benefits
– Secure leadership approval
– Account for both hard and soft benefits
– Be realistic on timeframe
Tool Selection – Define must-have capabilities
– Evaluate leading vendors
– Assess services and support
– Get hands-on experience before deciding
– Focus on scalability, governance
Infrastructure Setup – Ensure security, controls
– Provide for scalability
– Test with dev environment
– Build on cloud for agility
– Tap vendor‘s best practices
Bot Development – Automate prioritized processes
– Utilize recorder and coding
– Start small, deliver quick wins
– Library reusable components
Testing – Thoroughly alpha and beta test
– Resolve bugs
– Devise test cases upfront
– Continuously test and improve
Pilot Rollout – Start with noncritical processes
– Measure performance vs. goals
– Limit scope of initial launch
– Encourage user feedback
Scaling Up – Gradually expand automation
– Coordinate with change management
– Allow time for adoption with each phase
– Maintain quick wins
Governance – Monitor SLAs and KPIs
– Update bots for new use cases
– Install central bot management hub
– Track ROI continuously

Fig 2: Summary of key phases, activities, and best practices for executing an RPA implementation successfully

This structured approach ensures all steps in the RPA lifecycle are covered. It is especially important not to rush initial testing and the pilot launch before going full-scale. Getting employee buy-in through change management is also critical for adoption.

Combining RPA with AI, ML, and Other Technologies

While RPA provides the foundation for workflow automation, integrating complementary technologies takes capabilities to the next level:

Natural Language Processing

NLP allows bots to understand free-form data like emails, chats, and support tickets. This greatly expands the types of unstructured processes automation can handle.

Machine Learning

ML algorithms enable predictive capabilities beyond just rules-based programming. Bots can recognize patterns and make data-driven forecasting decisions for use cases like fraud detection.

Computer Vision

CV powers abilities like automated data entry from forms and documents. It converts scans and images into structured data that can feed into digital workflows.

Process Mining

Analyzing system logs helps uncover automation opportunities and provides objective before-and-after data to monitor RPA impact.

iPaaS and APIs

Integrating RPA with other core systems maximizes the value of automation across the enterprise technology ecosystem.

This blend of technologies constitutes intelligent process automation (IPA) and can extend automation to more complex use cases. According to Gartner, over 50% of large organizations will have adopted IPA capabilities by 2024.

Essential Technical Skills for RPA Developers

RPA developers need a diverse mix of technical abilities to build and implement robotic software effectively:

Technical Skill Importance
Programming Scripting languages like Python and .NET allow customizing workflows and bot logic within RPA tools
Debugging Thoroughly testing and troubleshooting bots pre-deployment resolves issues upfront
Cloud Platforms With rapid growth of cloud-based RPA, skills in platforms like Azure, AWS, and Google Cloud are crucial
API Integration Smooth integration between RPA and other systems relies on API mastery
Analytics Data analysis with BI tools helps monitor RPA KPIs and performance
OCR Configuration Optical character recognition setup allows automating data extraction from scanned documents and images

Fig 3: Overview of key technical skills needed for RPA developers

While programming knowledge is mandatory, debugging skills are also vital for delivering smooth automation operations. With cloud-based RPA adoption growing over 50% annually, cloud platform experience is rapidly becoming essential as well.

Critical Analytical Abilities for RPA Success

Beyond technical skills, RPA initiatives require a diverse mix of "soft" analytical, business, and process capabilities:

Process Analysis and Documentation

Thoroughly mapping out current workflows is crucial for identifying the most high-impact areas for automation.

Stakeholder Communications

Getting continued buy-in across the organization requires clearly explaining RPA capabilities and limitations at both leadership and employee levels.

Change Management

Smoothly adapting workflows and employee roles to integration automation rather than just layering it on top is vital for true transformation.

Project Management

RPA projects need structured planning, execution, governance, and performance tracking just like any other IT implementation.

Problem-Solving

When inevitable issues crop up, having strong analytical and troubleshooting abilities prevents minor hiccups from derailing projects.

Business Acumen

Understanding how RPA aligns with and achieves broader enterprise objectives beyond just cost is key for sustained leadership support.

Industry studies indicate RPA projects led by cross-functional teams combining both technical and analytical expertise have the highest success rates.

Avoiding Common RPA Pitfalls

While RPA adoption is accelerating, many organizations still struggle to achieve the full benefits. Being aware of these potential pitfalls allows you to proactively mitigate risks:

Pitfall Remediation
Lack of overarching RPA roadmap Develop a top-down automation strategy aligned to business goals
Automating bloated, broken processes Conduct process assessment and improvement before automation
Insufficient testing and piloting Thoroughly alpha and beta test bots before full deployment
Overreliance on bots without human oversight Strike the right balance between automation and workforce
Inflexible bots that cannot adapt as needs change Architect bots and integration for maximum configurability
Unrealistic expectations due to hype Set measured goals and manage stakeholder expectations
Lack of long-term change management Plan and invest in ongoing training, communication, and culture shift

Fig 4: Common RPA pitfalls and best practices for avoiding them

Falling into these traps leads to RPA failures. Being aware of these risks allow organizations to proactively govern implementations for success.

The Future of RPA – What‘s Next?

RPA technology continues advancing at a breakneck pace. Some innovations to watch include:

Low-Code RPA

Low-code tools like Servicetrace enable business users to build automations without deep technical expertise. This democratizes RPA across the enterprise.

AutoML for Bots

Automated machine learning streamlines training bots on past data to improve decision making. This enhances predictive capabilities without data science skills.

RPA Mobile Control

Developments like Automation Anywhere‘s IQ Bot allow managing and monitoring automation from anywhere via mobile devices.

Hybrid Cloud

Leading RPA platforms provide seamless integration between on-premise and cloud-based bots. This enables flexible deployment.

Hyperautomation

Forrester‘s concept of hyperautomation combines RPA, AI, analytics, integration, and more to rapidly scale automation across the enterprise.

There remains massive headroom for continued RPA innovation, ensuring it will remain among the hottest enterprise technologies for years to come.

Key Takeaways

To recap this comprehensive guide on achieving RPA success:

  • Carefully evaluate and select the right processes to automate to maximize ROI. Avoid automating for the sake of it.

  • Combine RPA with AI and analytics capabilities for more intelligent automation. Don‘t limit to just basic bots.

  • Develop diverse technical and analytical skill sets across RPA teams. Bring together business and technology experts.

  • Create an overarching automation roadmap rather than taking a siloed approach.

  • Govern implementations actively. Continuously track performance and be ready to refine as needs evolve.

RPA promises immense potential for business transformation if adopted strategically. But realizing this potential hinges on far more than just acquiring software bots. Companies must take a holistic approach backed by the expertise, technologies, and frameworks outlined in this guide.

To learn more about successfully scaling intelligent process automation within your organization, contact us at [Company Website] for a consultation. Our team has hands-on experience guiding leading enterprises through their RPA journeys. Get in touch today to get started.