IPA extends RPA with artificial intelligence. Learn how the two differ in data handling, learning, and decision-making, and why organizations often start with RPA first.
Intelligent process automation, or IPA, is seen as a complement to robotic process automation, extending its scope with artificial intelligence capabilities. Understanding the distinction between these technologies is crucial for organizational planning and for deciding where to invest as automation maturity grows.
The Need and Adoption of RPA
RPA platforms appeal to businesses because they require minimal technical expertise and operate with low-code or no-code implementation. The market grew from $1.5 billion in 2019 to an anticipated $7.1 billion by 2025. RPA mimics repetitive computer tasks with defined workflows, but it has a significant limitation: organizations cannot effectively use RPA when as much as 80% of their data remains unstructured, making advanced automation difficult.
Upgrading From RPA to IPA
IPA represents an evolution that integrates artificial intelligence technologies like computer vision, cognitive automation, and machine learning with RPA. This combination creates systems that mimic human actions while making sophisticated decisions based on collected data. A key distinction is that IPA systems continuously learn and improve through feedback. A basic RPA chatbot answers questions, but an IPA-enhanced version gathers user feedback to iteratively improve its responses.
Key Differences Between RPA and IPA
- RPA handles structured data, follows fixed rules, and requires manual instruction updates.
- IPA processes both structured and unstructured data, learns and adapts independently, and supports informed decision-making.
- IPA can access insights from images and text that lie beyond the reach of rule-based RPA.
Factors Making IPA Possible
- Automatic process discovery: AI-equipped bots identify and recommend high-ROI automation opportunities up to five times faster than traditional methods.
- Real-time document processing: intelligent document processing extracts and analyzes millions of documents rapidly, freeing employees for strategic work.
- Enhanced customer satisfaction: natural language processing and sentiment analysis enable contextual understanding of interactions across channels.
Conclusion
Organizations new to automation should implement RPA first, then upgrade to IPA once comfortable. Both technologies deploy rapidly, deliver immediate value, and require minimal infrastructure investment, since they interact with existing system interfaces rather than replacing the underlying systems.