RPA vs Intelligent Automation: Understanding the Evolution in 2026
The automation technology landscape has undergone a fundamental transformation over the past three years. Robotic Process Automation (RPA) — software robots that mimic human actions to automate repetitive digital tasks — is being absorbed into a broader category of Intelligent Automation (IA) that combines RPA with AI, workflow orchestration, process mining, and low-code development platforms. According to Gartner's June 2026 Magic Quadrant for Hyperautomation, the standalone RPA market is declining for the first time as organizations shift investment toward integrated intelligent automation platforms that address end-to-end process transformation rather than task-level automation.
This evolution from RPA to IA is not merely a marketing rebranding — it represents a fundamentally different approach to automation. RPA focused on automating individual tasks: a bot logs into a system, copies data from one screen, pastes it into another. IA focuses on transforming entire processes: understanding documents, making decisions, orchestrating work across systems and people, and continuously improving based on data. The shift is from automating tasks to automating outcomes.
Understanding the Automation Spectrum
| Capability | RPA (2018-2023) | Intelligent Automation (2024-2026) |
|---|---|---|
| What it automates | Individual, rule-based tasks | End-to-end business processes |
| How it works | Screen scraping and UI interaction | APIs, AI, workflow orchestration, and UI automation combined |
| Data handling | Structured data only | Structured, semi-structured, and unstructured data |
| Decision making | Rule-based: if-then logic | AI-powered: classification, prediction, recommendation |
| Adaptability | Brittle — breaks when UIs change | Resilient — adapts through AI and API-based integration |
| Governance | Limited; bot management focus | Comprehensive; full process lifecycle management |
| Typical ROI | 25-40% efficiency gain on automated tasks | 40-65% efficiency gain on automated processes |
The Integration of Low-Code and Automation
The convergence of low-code development and intelligent automation is one of the most significant technology trends of 2026. Low-code platforms provide the process orchestration layer that RPA always lacked — the ability to design, execute, monitor, and continuously improve end-to-end processes that span multiple systems, include human decision points, and adapt to changing conditions. RPA bots become just one type of automation capability within a broader intelligent automation platform, used specifically for legacy systems that lack APIs, while API-based integration handles modern systems more reliably.
This convergence means that process automation is increasingly accessible to business users rather than requiring specialized RPA developers. A business analyst can use a platform like Informat to design a process that includes: AI document processing for incoming forms, API integration with modern cloud systems, RPA-style automation for legacy systems, human approval steps for exception handling, and analytics for continuous process improvement — all designed visually without specialized programming for any of the automation types.
Migrating from RPA to Intelligent Automation
Organizations with existing RPA investments should view them not as sunk costs but as stepping stones. The migration path involves: inventorying existing automations and categorizing by suitability for API-based replacement vs. continued UI automation, adopting an intelligent automation platform that can orchestrate both legacy RPA bots and modern integration patterns, and gradually replacing brittle UI-based automations with more resilient API-based integrations as modernization progresses.
Why Informat Delivers Intelligent Automation
Informat provides: end-to-end process orchestration combining all automation types, AI capabilities including document understanding and decision intelligence, low-code accessibility enabling business users to build automations, enterprise governance for automation at scale, and API-first architecture reducing dependency on fragile UI automation.
Conclusion
The RPA era is giving way to the intelligent automation era — not because task automation was a bad idea but because it was an incomplete one. Automating tasks without redesigning processes creates fragile, siloed automations that break when underlying systems change and fail to address root causes of inefficiency. Intelligent automation platforms, built on low-code orchestration with integrated AI capabilities, enable organizations to automate outcomes rather than tasks — transforming entire processes in ways that are more impactful, more resilient, and more accessible to the business users who understand the processes best.