As enterprise adoption of automation technologies continues to accelerate, business leaders are faced with an array of options like RPA, intelligent automation, cognitive automation, and hyperautomation. With vendors using these terms interchangeably, it can be confusing to navigate and identify the right solution.
This comprehensive guide aims to clear up that confusion by exploring the key differences between basic RPA and advanced intelligent automation. We’ll compare their technical capabilities, typical use cases, and considerations for selecting the appropriate tool for your needs now and in the future.
What is RPA?
RPA refers to software programs known as bots that automate repetitive, rules-based tasks in back office functions like:
- Data entry and extraction
- Reporting
- Hiring and onboarding
- Procure-to-pay
In our list of 70+ RPA use cases, you can discover more applications across various business functions.
At its core, RPA tools provide the capability for non-technical users to design and deploy bots via drag-and-drop interfaces and simple logic without needing to code. These bots follow predefined rules and procedures to carry out repetitive administrative tasks like transferring data between systems or extracting information from documents.
Common RPA Capabilities
Typical RPA platforms offer an array of features like:
- Recording manual tasks into bot scripts
- Integrating bots with legacy systems via screen scraping
- Orchestrating bot workflows with business rules and schedules
- Scaling bots across desktop environments
- Centralized monitoring and analytics dashboards
These enable reusable automation across processes like data migration, report generation, transaction processing, and more. However, functionality tends to be limited to structured digital tasks only.
What is Intelligent Automation?
Intelligent automation, also referred to as cognitive automation or hyperautomation, combines RPA with other advanced technologies like:
- Artificial intelligence (AI)
- Machine learning (ML)
- Natural language processing (NLP)
- Optical character recognition (OCR)
Together, these capabilities allow intelligent automation bots to handle more complex business scenarios beyond basic rules-based tasks. This includes processing unstructured data from documents and emails, understanding context to make decisions, and continuously improving performance through built-in machine learning algorithms.
Common Intelligent Automation Capabilities
In addition to the typical RPA feature set, intelligent automation adds more advanced functionality via AI techniques like:
- Classifying documents and extracting data using OCR and NLP
- Translating speech requests into executable bot tasks using natural language understanding
- Leveraging computer vision for automation in visual settings
- Detecting anomalies and threats using analytics and machine learning classifiers
- Chatbots handling natural conversations with process queries and instructions
Together, these allow intelligent automation to go beyond replicating human digital workflows. Bots can now consume unstructured content, interpret requests, and make smart decisions within complex processes.
RPA vs. Intelligent Automation
While RPA solutions have been around since the early 2000s, intelligent automation represents a new generation of more advanced process automation. In fact, Gartner identified hyperautomation as an emerging trend this year.
In their most basic form, RPA tools rely on legacy technologies like screen scraping and scripting. This limits them to only automating simple, repetitive digital tasks with clear rules. Intelligent automation infuses RPA with AI-driven capabilities to understand context, handle complexity, and mimic human-like judgement. This allows them to automate more sophisticated processes that involve decision making with dynamic real-world data.
Structuring Unstructured Data
A key difference between basic RPA and intelligent automation lies in their ability to process unstructured data like text documents or handwritten forms. RPA bots can only scrape data from structured digital interfaces and lack advanced recognition technology to interpret scanned documents, emails, images, or videos.
Intelligent automation leverages AI techniques like natural language processing, optical character recognition, and computer vision to unlock trapped information. For example, insurance claims processing relies heavily on extracting relevant data from scanned medical forms or emailed attachment to auto-populate fields. Only intelligent automation can reliably structure this unstructured content.
Common Machine Learning Methods
Popular algorithms leveraged in these platforms include:
- Computer vision – Convolutional neural networks for image classification
- Natural language processing – Recurrent neural networks and transformers for translation
- Anomaly detection – Clustering algorithms like isolation forests to find outliers
- Prediction modeling – Regression techniques (linear, logistic) for forecasting KPIs
These mature ML methods allow structured insights to be extracted from unstructured data feeds. The outputs can then drive downstream business decisions or trigger automation workflows.
Decision Making Capabilities
While RPA helps streamline the human workflows, exceptions and complex decision making still requires manual review. Intelligent automation incorporates techniques like machine learning and analytics to allow bots to mimic limited human judgement for rules-based decisions. Using historical datasets, they can route transactions to the right workflow or flag unusual cases for auditor review based on learned patterns.
For example, automated credit card fraud detection relies on neural network algorithms examining past transactions to detect anomalies indicative of illegal usage vs normal customer spending profiles. Decision trees could further categorize types of fraud and take corrective actions accordingly.
Sample Bot Decision Tree
Bots powered by machine intelligence can incorporate hundreds of business rules and process branches into their logic. Over time, the models continue to improve decision accuracy based on new evidence.
Continuous Improvement
RPA bots follow preset logic and have no inherent capability to optimize or improve themselves over time. Any enhancements require collaboration between business analysts and bot developers. Intelligent automation leverages AI techniques like machine learning to enable bots to analyze their own performance data and refine algorithms to boost speed, accuracy and compliance.
As an example, an automated HR recruiting assistant could track effectiveness of existing candidate screening criteria and experiments with modifications to dynamically improve quality of shortlists. Uplift analysis provides the embedded analytics to quantify model improvements.
Choosing the Right Tool
When researching automation solutions, don’t get distracted by the labeling as RPA vs intelligent automation. Focus instead on the functional capabilities. Many tools offer a mix of basic RPA features combined with add-on AI services or integration options.
Key Evaluation Criteria
Consider the following factors when selecting the right automation tool:
- Cognitive capabilities: Even if you only need basic RPA now, future-proof by choosing a platform that allows easy integration of AI and machine learning.
- Use case complexity: Are your processes simple and rules-based or do they require judgement calls? Evaluate product samples and test automation scenarios.
- Ease of use: Can citizen developers build and modify bots or is coding needed? Prioritize the right level of technical difficulty.
- Extensibility & integration: How easily can you scale automation across processes, integrate with other tools?
- Pricing model: Upfront costs, ongoing licensing, consumption based? Models vary greatly so align with expected usage.
Use our head-to-head comparison guides on top RPA solutions and leading intelligent automation platforms to narrow down your options.
Industry Adoption Trends
Worldwide spending on RPA software is projected to reach $2.4 billion in 2022, representing a 19.5% increase over 2021. Meanwhile the artificial intelligence software market, which fuels many intelligent automation capabilities, is forecast to accelerate from $62.5 billion in 2022 to $500 billion by 2028.
As per industry surveys, over 70% of organizations are actively exploring or have already deployed some form of RPA. However, intelligent automationadoption lags at just 34% penetration across companies.
| Year | RPA Software Revenue | Growth % |
| 2022 | $2.4 billion | 19.5% |
| 2023 | $3.1 billion | 28.3% |
| 2024 | $4.0 billion | 27.7% |
The accelerated growth reflects increasing enterprise comfort with automation, spurred by pandemic disruption. IDC predicts over 75% of business processes will be automated by 2024 – requiring both RPA and AI capabilities.
Implementation Success Factors
Effectively scaling automation is not simply a matter of purchasing the latest tool. Without thoughtful change management and governance, companies struggle to generate ROI.
Critical factors that influence implementation success include:
- Securing leadership alignment on vision and targets
- Promoting user buy-in across impacted teams
- Establishing centers of excellence with technical skills
- Iteratively building bots aligned to needs
- Tracking productivity and compliance data
Our RPA project implementation guide shares field-tested advice on avoiding pitfalls that can undermine your automation success. Protecting stakeholder enthusiasm through early wins is instrumental in spurring digital transformation.
The Road Ahead
Over the next few years, expect RPA vendors to continue acquiring AI capabilities for enhanced decision making, learning, and language understanding. Cloud platforms will become dominant for scalable bot deployment and management with embedded analytics.
Leading intelligent automation pure-plays like Automation Anywhere, UiPath, and Blue Prism now generate over $100 million in funding per round to further their innovation.
As barriers between capabilities blur in an increasingly competitive space, focus less on branding and more on aligning solutions to your actual process needs for optimal automation success into 2025 and beyond.