By George Harpur, Co-founder and CEO at Aluma
In an era where digital transformation has evolved from an aspiration to a requirement for most organizations to remain competitive, companies everywhere are on a quest to maximize automation of business-centric processes. But despite billions invested in digital working technologies, the mission to improve automation continues to be challenged by unstructured content.
While many organizations have integrated some degree of automation across basic business processes, traditional data capture technologies are limited in scope and scale. In an unlimited, high-volume document universe, businesses need to adopt intelligent, mission-critical automation strategies to see significant ROI with the fastest time-to-value. This means investing in high-performing, intelligent document processing that works in combination with AI, the modern technology that Forrester refers to as “Document Intelligence.”
The Need to Rethink Traditional Document Processing
The majority of business processes today–from customer-facing processes to back-office operations, or compliance workflows, are hampered by high volumes of information in unrigid formats. These can be formatted documents, PDFs, images, or emails–otherwise known as unstructured data. A recent AIIM survey showed that nearly 40% of companies felt that getting control of unstructured information in their customer-facing processes was their number one priority to enrich the overall experience of their customers, while over 50% want to gain control of unstructured information internally to improve productivity and drive automation throughout the organization.
To automate manual workflows, organizations must ingest, classify, and extract unstructured data from the source–financial documents, contracts, digital assets, images, etc.–and turn that information into actionable data insights for further processing.
While viable document processing technology has been available for decades, organizations still struggle to conquer their unstructured data. So what’s getting in the way?
The Old Way
Traditional data capture technologies like optical character recognition (OCR) operate off-line in isolation. These applications tend to be standalone, and data capture occurs asynchronously before uploading into the business process. While these technologies can be highly performant once configured, deploying these systems is no easy feat, and the slightest modification requires specialist intervention. Not to mention, integration with other business systems and applications can be highly limited.
Though it may be tempting to deploy a pre-packaged document processing tool, the often monolithic, heavyweight capture platforms available not only break the bank but are highly inflexible in terms of practical application. To the other extreme, organizations may be inclined to source different automation components individually as needed, but as the tech stack starts to pile up, this can become technically and commercially hard to pull off.
The New Way: Intelligent Document Processing
By moving data capture from off-line to in-line, the automation technology becomes an integral part of the business flow rather than an isolated flow of its own, blurring the boundary between the technology and the complete business process. Intelligent Document Processing (IDP) seeks to do just this. Defined as a set of technologies that can be used to understand and turn unstructured and semi-structured data into a structured format, when enhanced with AI capabilities, IDP works to capture, classify, and extract the most difficult-to-automate, unstructured data.
The technology scans the unstructured document for any relevant embedded information before appropriately processing or enriching it in the form of highlights or bookmarks.
Ideally, the technology needs to successfully coordinate and manage the control flow, queuing, scaling, monitoring, logging, and tracking. With so many moving parts, we’re left with a broad scope for AI to automate what are otherwise tedious manual tasks.