Extract key values ​​and data points to structure information and streamline business processes.

[Aklima@416]

Unstructured data is difficult to process and organize because it does not have a predefined format. Think about the different types of documents such as employee contracts, vendor agreements, onboarding documents, etc. They all have different formats.

To bring structure to these unstructured documents and improve the usability of the information, employees must create metadata. This is a basic description of the document that can include the contract number, transaction details, or the names of the parties involved.

Although metadata is essential for search, information compliance, retention policies, and workflows, it is often poorly managed. Very few employees bother to do it; if they do, it is often inconsistent across the organization.

Manual metadata creation is a slow, tedious and error-prone process. As a result, companies typically misclassify up to 20% of their documents, losing them forever (source: ARMA International).

Fortunately, AI solutions can automate metadata extraction and eliminate human errors. They can “read” and “understand” the content of documents and extract key values ​​such as company name, contract due dates, locations, etc. This technology significantly reduces the time spent on document processing and allows employees to focus on other tasks that bring more value to the organization.

Automate manual, time-consuming tasks that are beyond the capabilities of robotic process automation (RPA) offerings.
Robotic process automation (RPA) has proven to be an ideal solution to improve business efficiency by automating simple and repetitive tasks. However, unstructured data still poses a significant challenge for RPA.

RPA can only replicate pre-established actions. It must be pakistan mobile number list for whatsapp explicitly programmed to extract metadata for each document type. Furthermore, in the case of unstructured documents, it is virtually impossible to teach the robot to extract the relevant information. Hence the need for intelligent automation. AI solutions can analyze documents intelligently, just like a human would.

Fortunately, there are technologies that can help take unstructured data from being a nuisance to not being a problem. Robotic process automation (RPA) once seemed like the answer to every problem, but that was before intelligent document processing (IDP) came along. Instead of being limited to structured data, intelligent document processing is taking it to the next level.

Additionally, unlike RPA, AI solutions continually learn from experience and automatically improve performance over time. They can operate with little to no human intervention.

For example, with ProcessMaker IDP, we aim to create a collaboration between humans and artificial intelligence. We give computers the tools to learn from knowledge experts in order to make decisions. When the machine has doubts, it asks employees to validate the results. And if there is an error, the algorithm is retrained. This process allows ProcessMaker IDP to continuously learn with the human expert in the loop.