Intelligent Document Automation: Eliminating Manual Efforts from Enterprise Processes
The age of digitization and automation is picking up pace. Artificial intelligence is touching many business and industrial processes, addressing several business expectations including efficiency, productivity, and ROI. One of the processes where the intelligent automation model seems set to drive massive value in enterprises is unstructured documents –how information in documents is extracted, stored, processed, and managed.
At the core of modern business processes lies the challenge of extracting information from a document and taking a specific action. In the quest for consistency, repeatability, and efficiency, businesses have created standard workflows for most tasks.
Without the right intelligent automation in place, documents have to be reviewed manually to extract the relevant information that can spark the next action dictated by the workflow. The manual process offers little consistency, timeliness, and efficiency – which eventually translates to errors, rework, and additional cost.
Challenges in all-human interventions
Human potential is the driving force behind creations, innovations, and connection. But mundane processes are the crucial battleground for human intervention and the question of how much value they can bring. Honestly, humans often need stimulation for routine tasks. However, it’s also in such mundane, repetitive, and everyday tasks that humans also commit errors, delay, and fail to deliver consistency.
As it happens, routine is something that can be easily automated for better outcomes.
Consider a process like invoice processing in an enterprise. Large enterprises will have hundreds of suppliers generating thousands of invoices. These invoices have to be manually read and interpreted to extract the relevant information required to kick off the payments process. The workflow of approvals, budget release, payment, and accounting acknowledgment can flow smoothly only after the manual first step. Given the sheer volume of invoices, think of the productivity improvement if the manual process could be automated?
Intelligent Automation with OCR (Optical Character Recognition)
Automation demands addressing semi-structured and unstructured data. Handling structured data is largely a solved problem, the challenge arises for unstructured data. In the case of invoice processing, think of the different formats, varying text, descriptions, and standards different invoices follow. How to automate that?
What adds human-like intelligence to computing capabilities is the integration of ML (Machine Learning). This is the key to enhancing more cognitive capabilities and addressing the complexity of the processes being automated. This is also where intelligent OCR (Optical Character Recognition) technology helps in document definition, document identification, data extraction and classification, and learning. OCR looks at the digital form of images and texts to identify underlying patterns and converts them into text. However just OCR is not enough. Once you extract all the text from the document, it essential to extract only the text/ fields that are of most relevance. ML then steps in to ensure just that. Using various ML techniques, one can train the machine to predict the parts of the entire text into relevant fields. Due to the diverse types of documents (contracts, invoices, resumes, payment advice, purchase orders etc), it is important to classify, validate and constantly learn from user inputs.
Business expectations in document automation
When it comes to digitally transforming the enterprise processes with intelligent automation, here’s what enterprises expect:
Essential components of Intelligent automation
Here’s how intelligent automation of enterprise processes could pan out:
The evolution in the technology is visible even as we consider the process outlined here. This process, as laid out here, involves a significant manual effort at the beginning to define the templates and lay out the rules. The value of automating this process can be enhanced when the document processing becomes independent of templates and intuitive so that it’s not restricted by rules. That’s the emerging standard in intelligent automation. ML presents a more sophisticated, cost effective and scalable method.
Business benefits of implementing machine learning in unstructured documents management
Enterprises adopting intelligent automation solutions for automating mundane tasks will experience the following benefits:
Automation is well-placed to become the norm across businesses for handling mundane tasks. However, the next wave of process automation would revolve around amping up the processing of semi-structured and unstructured documents. Processing invoices, resumes, legal contracts, and other documents manually would be inefficient, non-scalable, complex, and might lead to errors. Having an intelligent automation model with OCR, AI, and ML to automate these routine processes will improve efficiency, effectiveness, accuracy, and consistency. That will drive the next wave of automation in enterprise processes.