Tech & Skills Required for RPA Success in 2024: The Complete Guide

Hi there! With robotic process automation (RPA) transforming businesses, you’re probably curious about how to make RPA implementations succeed in your company. Well, you’ve come to the right place!

In this comprehensive guide, I’ll break down the key technologies to pair with RPA and the technical and analytical skills your team will need. I’ll also walk through RPA implementation steps, common challenges, and tips to avoid failures.

Let’s get started!

What is RPA and Why Are Businesses Adopting It?

RPA is software that automates repetitive, rules-based tasks like data entry and processing transactions. RPA bots act as a virtual workforce to handle manual, mundane work so employees can focus on high-value activities.

According to Gartner, the RPA market grew at a 63% compound annual growth rate from 2018 to 2019, reaching $1.3 billion. In fact, they expect RPA software revenue to top $2 billion by 2022.

What’s driving this growth? Companies are turning to RPA to:

  • Reduce operational costs by automating labor-intensive processes
  • Improve efficiency by cutting process times from hours to minutes
  • Minimize errors and increase compliance with consistent bot performance
  • Enable employees to focus on creative, strategic work rather than data processing

RPA is making inroads across many industries:

  • Banking: Automating loan processing, customer onboarding, transaction reconciliation
  • Insurance: Claims processing, policy administration, compliance audits
  • Healthcare: Patient appointment scheduling, medical coding, billing automation
  • Supply chain: Inventory management, shipment booking, order status updates

Clearly, RPA adoption is accelerating. But simply implementing RPA tools isn’t enough to realize benefits. The rest of this guide will cover the technologies and skills you need to set your RPA initiatives up for success.

Complementary Technologies to Boost RPA Impact

While RPA excels at structured, rules-based tasks, it lacks cognitive abilities to handle unstructured data and decision making. Combining RPA with emerging technologies gives a more robust automation solution.

AI and Machine Learning Allow RPA to Handle Unstructured Data

RPA bots follow programmed rules and can’t adapt to new scenarios or learn from experience. That’s where AI and machine learning come in!

AI technologies like computer vision, natural language processing (NLP), and machine learning empower RPA with cognitive abilities to manage unstructured data. Bots can analyze images, interpret text, and understand patterns to automate semi-structured tasks.

According to Mckinsey, AI-enhanced RPA can automate up to 45% more processes than RPA alone. For instance, RPA + AI can automatically classify and extract data from legal contracts rather than relying on predefined extraction rules.

Process Mining Provides Insights to Optimize RPA Implementation

Process mining analyzes system logs and event data to model real-life processes. It identifies:

  • Process bottlenecks slowing things down
  • Differences between actual processes and what’s documented
  • Opportunities to improve processes before automation

This factual data enables smarter RPA planning. Companies can identify the most impactful processes to automate and ensure bots align to real workflows.

A 2019 study found 92% of process mining users report it helped accelerate RPA implementation. For example, Siemens saved over 50% in project costs by using process mining, reducing RPA development time from months to weeks.

Data Extraction Converts Raw Data for RPA Consumption

RPA can only automate well-structured data and processes. Data extraction tools convert messy, unorganized data into standardized digital format for RPA bots to work with.

Whether it’s résumés, forms, invoices, or contracts, data extraction eliminates manual data entry by automatically pulling relevant info into spreadsheets or databases. Combining RPA with data extraction improves the quality and speed of data processing.

How to Implement RPA: Key Steps to Success

Once you’ve got the right complementary technologies, it’s time to implement RPA! Follow these best practices for smooth RPA rollouts:

Step 1: Document and Analyze Current Processes

Take stock of current workflows and performance metrics. Look at:

  • Process steps, handoffs, bottlenecks
  • Volume, cycle times, errors
  • Costs of manual processing

This data will highlight automation opportunities and savings potential.

Step 2: Identify Top Processes for Automation

Not all processes benefit equally from RPA. Prioritize processes that are:

  • Repetitive and rules-based: Mundane tasks done according to defined steps.
  • High-volume: Automating hundreds or thousands of daily transactions provides large productivity gains.
  • Prone to errors: Bots perform consistently without human error.
  • Involving multiple systems: RPA seamlessly integrates across systems.

Step 3: Get Organizational Buy-In

RPA will change workflows and potentially displace roles focused solely on simple tasks. Explain the benefits of transformation, like improving employee satisfaction by removing dull work. Provide training so staff understand RPA’s role.

Step 4: Select Your RPA Tool

Leading options include Blue Prism, UiPath, and Automation Anywhere. Evaluate tools based on:

  • Ease of use and training requirements
  • Integration with your tech stack
  • Security and governance capabilities
  • Scalability to support future growth

Step 5: Decide Whether to Outsource RPA Development

Building RPA expertise in-house requires significant investment. Alternatively, outsourcing to an implementation partner can accelerate your first deployments while internal staff learn.

Step 6: Develop, Test and Iterate RPA Solutions

Plan an iterative approach for building bots. Test thoroughly and refine them based on user feedback before full-scale release. Start simple to prove value before expanding scope.

Step 7: Run a Pilot Implementation

Pilot the RPA solution with a small group of users to work out any kinks. Gather feedback to improve the automation.

Step 8: Full Production Rollout

With a successful pilot complete, roll out the proven RPA process across the organization. Closely monitor performance and change management needs in the initial weeks.

Step 9: Maintain RPA Operations

Manage bots like any other IT system. Continuously monitor for errors. Keep bots updated as business processes evolve. Governance and support are key for long-term RPA success.

Technical Skills to Look for in an RPA Developer

Developing and maintaining reliable bots requires a blend of technical programming abilities, process knowledge, and analytical thinking. Look for these skills when building an RPA team:

Coding Expertise in Common RPA Languages

Most RPA tools utilize .NET frameworks and C# for development. Java and Python are also options for creating custom scripts and extensions. Understanding foundational programming constructs like loops, variables, and methods is essential.

Experience with Visual Basic for Applications (VBA) is also valuable, as RPA tools use a VBA-like interface for scripting bot workflows.

Knowledge of RPA Frameworks and Methodologies

Learning RPA-specific frameworks like UiPath Studio helps developers be productive faster. Most vendors provide training resources. Certifications in RPA tools demonstrate hands-on expertise.

Methodologies like Six Sigma help with disciplined RPA solution design and improvement.

A Testing and Debugging Mindset

Thoroughly testing bots during development is critical for identifying logic gaps and errors before deployment. Developers should use real datasets for simulation testing and instrument logs to monitor performance. Unit testing experience is valuable.

When issues inevitably crop up, developers must debug quickly to diagnose root causes and fix problems. Mastering RPA platforms’ built-in debugging tools accelerates this.

Analytical Abilities for Process Improvement

Strong RPA developers don’t just blindly code bots. They analyze processes to identify automation opportunities and account for downstream impacts.

Communication skills are key to gathering process knowledge from business teams. Analytical thinking helps optimize workflows and catch exceptions.

Overcoming RPA Implementation Challenges

While RPA can deliver tremendous value, it also brings change to organizations and disruptions to workflows. Be aware of these common pitfalls:

Insufficient process analysis: Automating broken, inefficient processes just multiplies problems. Conduct thorough as-is analyses before automating.

Lack of support from impacted groups: Involve business teams early and often to secure buy-in and specialized process expertise.

Failure to plan for exceptions: Don‘t underestimate workarounds needed to handle processes that don‘t fit strict rules.

Technical difficulties integrating complex systems: Evaluate compatibility of each system upfront and budget work to smooth connections.

Skills gaps for managing bots: RPA requires specialized skills, so have a training plan for internal teams or external partner.

With the right technologies, disciplined approach, developer talent, and change management, you can create an RPA program that transforms your organization for the better. I hope this guide provided a helpful overview. Wishing you success on your automation journey!

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