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Whether to use RPA (Robotic Process Automation) or AI (Artificial Intelligence) for document processing depends on the specific requirements of your use case.
RPA is best suited for simple and rule-based processes that involve structured data, such as extracting data from forms or invoices. RPA bots can be programmed to extract specific data fields from a document and input them into another system, which can help automate repetitive tasks and reduce errors.
On the other hand, AI is best suited for processes that involve unstructured data, such as extracting data from documents with varying formats or layouts. AI models can be trained to recognize patterns and extract relevant information from unstructured data using techniques such as natural language processing (NLP) and computer vision.
Before diving into it let’s discuss the basis difference between two technologies:-
What is Robotic Process Automation (RPA)?
Robotic Process Automation is a type of software that automates tasks by mimicking the actions of an employee. It is used to automate mundane, repetitive tasks such as data entry, document processing, and more. This allows businesses to save time and money by eliminating manual processes and reducing human error.
RPA is an automation technology that uses software robots to carry out manual, rule-based tasks. It works by replicating human actions such as clicking on screens, entering data into fields, copying information from one system to another and more – all in an effort to improve operational efficiency. It can be used for a wide range of processes such as invoicing, compliance monitoring, customer support and document data extraction.
What is Artificial Intelligence (AI)?
Artificial Intelligence is a branch of computer science that focuses on creating machines that can think and act like humans. AI uses complex algorithms and data analysis techniques to make decisions without direct human intervention. AI can be used in document data extraction by using advanced pattern recognition algorithms to identify and extract relevant information from documents quickly and accurately.
Difference between RPA & AI
When it comes to document processing, RPA is designed with efficiency in mind, while AI focuses on accuracy. RPA can automate mundane tasks quickly, but it cannot make decisions or process complex tasks; whereas AI has the ability to learn from past data inputs and make decisions based on those inputs.
For example, an RPA robot can scan a document for specific words or phrases, but it cannot interpret the meaning of those words or phrases like an AI system could. Therefore, if you need accurate results from your document data extraction project, then you should opt for an AI-powered solution rather than an RPA-based one.
Should you use RPA or AI for document data extraction?
RPA offers benefits such as faster turnaround time, accuracy in repetitive tasks, scalability and cost savings compared to manual labor. However it may not be suitable if there is a need for more sophisticated features like natural language processing or pattern recognition which requires more complex algorithms than those offered by RPA tools. On the other hand, AI is better equipped for complex tasks due its ability to learn from past experiences and adapt accordingly with new input data sets; however it may require additional resources for implementation and maintenance due its highly sophisticated nature:-
When it comes to highly structured documents such as tax forms, RPA comes neck-to-neck with AI-based intelligent document processing when it comes to turnaround time.
AI-based document processing solutions can adapt to variations in a document type. With RPA, you have to train the solution for every little change in the document.
Because RPA solutions can’t comprehend context-based data and works primarily by capturing field-level data, it could produce inaccurate results. For example - An RPA solution can sometimes mistake “I’ for “1” as they’re close enough, however chances of making this error is highly unlikely with AI-based solutions as it can capture context-based data and make sense of it.
AI-based solutions are much more accurate than RPA solutions and can adapt to different variations in documents, it is highly scalable.
If your document data extraction process involves a large volume of structured data and is repetitive, RPA might be the better choice. However, if the documents are unstructured and require more complex processing, AI would be a better option.
In some cases, a combination of RPA and AI might be the best approach. For example, you can use RPA to automate the initial data extraction process and then use AI to validate and refine the extracted data.
Ultimately, the choice between RPA and AI depends on the specific requirements of your use case and the type of data you need to extract.
||RPA-based document processing
||AI-based document processing
|Type of Data
||Limited to rule-based processes
||Can handle more complex processes
||Requires programming and rules-based training
||Requires data training and model development
||Good for structured data, may require human intervention for unstructured data
||Good for unstructured data, may require additional data for model training
||Limited to predefined rules
||Flexible and can adapt to new data formats
||Generally low cost
||Generally high cost
Choosing between RPA and AI for document data extraction depends on the complexity of your task at hand. If you need quick results with minimal effort required, then RPA might be the right choice; however, if accuracy is more important than speed, then utilizing an AI-based solution would be wise. Ultimately, understanding what each technology does best will help you make a more informed decision about which one will best meet your needs for this particular project.
In conclusion, when deciding which technology – RPA or AI – should be used for document data extraction depends on the complexity of the task at hand as well as available resources. For simpler tasks that require basic rules-driven automation RPA may be the better choice while AI is better suited for more complex tasks involving natural language processing or image recognition. CTOs must weigh both options carefully before making a decision so they can ensure they are getting the most out of their automation efforts while staying within their budget constraints.