RPA vs AI – Differences you need to know
Robotic Process Automation (RPA) and Artificial Intelligence (AI) may seem similar at first glance, as both automate tasks. However, they serve distinct purposes. For instance, RPA mimics human actions for repetitive tasks, while AI replicates human intelligence for decision-making. Although RPA is a subset of AI, their roles and applications differ significantly. In this blog, we explore these differences to help you understand their unique contributions to businesses.
What is robotic process automation?
RPA automates repetitive tasks by enabling software bots to mimic human actions. Specifically, these bots rely on structured data to perform systematic tasks, such as data entry or file transfers, at predefined times. In other words, RPA eliminates manual effort in rule-based processes, acting like a digital employee.
For example, enterprises use RPA to streamline low-level, repetitive tasks, freeing up resources for strategic priorities. As a result, RPA saves time and boosts efficiency in industries like banking, insurance, and telecom.
What is Artificial intelligence?
AI empowers machines to replicate human intelligence, enabling them to make decisions and act intellectually. Unlike RPA, AI processes both structured and unstructured data, adapting to new information through algorithms and learning. Consequently, Artificial Intelligence handles complex tasks, such as forecasting or personalized recommendations, without predefined rules.
In essence, AI’s decision-making ability sets it apart from RPA’s rule-based automation. For instance, AI drives innovations like virtual assistants (e.g., Siri or Alexa), which respond intelligently to user inputs based on data analysis.
Examples of RPA and AI
RPA excels in tasks requiring no improvisation, such as data scraping, application logging, or API connections. For example, banks use RPA to automate data transfers, while insurance companies rely on it for claims processing. In contrast, RPA avoids tasks needing judgment or creativity.
AI powers advanced applications like IoT devices, machine learning models, and virtual assistants. Specifically, Siri analyzes user queries to deliver tailored responses, while AI-driven logistics tools optimize supply chains. Moreover, industries leverage AI for forecasting taxes, managing inventory, and enhancing product recommendations.
There is no defined use of AI; AI automation is present in all RPA processes and machine learning and IOT, etc. AI is the mother ship with every other automation process being a part of it. Industries and organizations seeking more complex and analysis-based software support turn to AI. Areas like forecasting tax, planning logistics, recommendation-based actions, inventory management and product optimization make the most out of AI.
Key differences (RPA vs AI)
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Thinking vs. Doing
AI acts like a thinking human, analyzing data, trends, and patterns to make decisions. For instance, it predicts customer behavior using algorithms. Conversely, RPA follows predefined rules to execute tasks, focusing on doing rather than thinking.
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Process-Centric vs. Data-Centric
RPA is process-centric, automating structured, rule-based tasks with no deviation. In contrast, AI is data-centric, training itself on structured or unstructured data to perform tasks. As a result, AI adapts dynamically, while RPA adheres strictly to instructions.