As a seasoned data analyst and machine learning expert with over a decade of experience in intelligent automation and RPA, I‘m often asked – what‘s the difference between robotic process automation (RPA) and intelligent automation (IA)? With the proliferation of automation tools and terms in the market, it‘s easy to get confused.
In this comprehensive guide, I‘ll demystify the terminology, compare RPA and IA head-to-head, analyze the key differences, and provide a framework to help you choose the right automation technology for your business needs in 2024 and beyond.
A Quick Look at RPA
First, let‘s quickly recap what RPA is.
RPA refers to software programs that automate repetitive, rules-based tasks traditionally performed by humans. RPA bots interact with computer systems and software through the user interface to mimic human actions. This eliminates the need for APIs or deep integration with backend systems.
As per Gartner‘s latest forecast, the RPA software market will reach $1.9 billion in 2024. Top use cases include:
- Data entry and extraction
- Report generation
- Accounting and order processing
- HR onboarding and offboarding
- Procure to pay workflows
RPA delivers quick ROI by reducing manual workloads and human errors. It works best for high volume, repetitive tasks with clear business rules and structured data inputs.
What is Intelligent Automation?
Intelligent automation refers to RPA augmented with artificial intelligence technologies like machine learning, natural language processing, computer vision etc. It expands automation beyond repetitive tasks to more complex use cases.
As per leading research firm Everest Group, the intelligent automation market is projected to reach $20 billion by 2025, growing at a CAGR of 20%.
Key capabilities offered by intelligent automation include:
- Machine learning for pattern recognition and prediction
- NLP for processing unstructured data and text
- Computer vision for automating based on visual inputs
- Natural language generation for creating narratives
With intelligent automation, bots can interpret documents, hold conversations, extract insights from data, and continuously improve through machine learning. This significantly expands the scope of automation beyond the capabilities of traditional RPA.
Comparing RPA and Intelligent Automation
Here is a head-to-head comparison of the key differences between basic RPA tools and intelligent automation solutions:
| Parameter | RPA | Intelligent Automation |
|---|---|---|
| Data Processing | Structured data only | Structured + unstructured data |
| Analytics | No inherent capability | Can analyze data for insights |
| Decision Making | Requires explicit rules | Interprets context using ML/NLP |
| Learning | Needs ongoing maintenance | Continuously improves through ML |
| Human Intervention | May be needed for exceptions | More autonomous |
| Implementation Time | Relatively fast | Slower due to ML model development |
| Cost | Lower | Higher |
To summarize:
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RPA is best for simple, rules-based tasks with structured data.
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Intelligent automation expands scope to unstructured data and decisions using AI.
However, lines are blurring as most RPA tools now offer intelligent capabilities either natively or through add-ons and integration.
When Should You Use RPA vs Intelligent Automation?
While both RPA and IA have their own strengths and limitations, the right choice for your business depends on your specific use cases and objectives.
Based on my experience in hundreds of automation initiatives, here are some scenarios suitable for each technology:
Good fits for RPA
- Data entry from PDFs and forms
- Report generation from ERPs
- Transaction processing in finance
- Automating employee onboarding
- Claims processing in insurance
Good fits for intelligent automation
- Classifying support tickets and routing
- Reviewing contracts and flagging risks
- Processing damage claims by analyzing images
- Automating chatbot conversations
- Detecting financial crimes from transactions
However, I recommend evaluating both RPA and IA tools even if you only have simple use cases right now. Choosing a platform that allows easy integration of AI and ML will provide more long-term value.
How to Choose the Right Automation Technology
Here are 8 key factors business leaders should consider when deciding between RPA and intelligent automation:
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Current and future needs: Will you need AI capabilities like computer vision or NLP down the line?
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Data types: Do you need to process unstructured data like emails or scans?
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Skill set: RPA needs less coding skills, but IA requires data science expertise.
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Costs: Intelligent automation has higher licensing and implementation costs.
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Scalability: Intelligent automation platforms tend to be more scalable across geographies and processes.
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Security: Evaluate identity management, access controls, and auditability.
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Ease of use: Low-code RPA tools are easier for citizen developers to use compared to coding IA bots.
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Vendor support: Look for vendors that offer training, implementation services, and post-deployment support.
The Bottom Line
RPA delivers fast ROI for automating repetitive tasks. Intelligent automation expands scope to unstructured data and decisions, but needs more implementation time and costs.
Most importantly, don‘t go by terminology alone. Carefully evaluate tool capabilities and experience of vendors to pick the right long-term automation platform, even if starting small with RPA pilots.
Combining RPA with AI and ML can provide a scalable and future-proof automation solution for your business. Please reach out to me if you need help navigating your automation journey. I would be happy to guide you!