balled up paper in the waste basket
PHOTO: Steve Johnson

Do you remember when Google introduced Google Docs in 2008? It gave life to documents. When you clicked on the link and saw other people working within the same document, it made it feel alive. It was one of the biggest defining moments over the past decade or so that showed that working with content and documents could be different. It didn't have to fit into the same boring lane that it had been in for so long.

Even though document tools have become mainstream and many things have changed in how companies are automating processes, documents remain a real challenge to businesses when it comes to extracting data and connecting to processes and decisions.

Artificial intelligence (AI) is continuously changing almost every aspect of the way we work today, from increasing productivity by making robotic process automation (RPA) bots smarter to using more data-driven insights to improve decision-making skills. AI’s ability to identify and learn from data has proven to be extremely beneficial for many organizations. The technology’s impact on businesses will continue to grow, as will the market. IDC forecasts the overall AI software market will approach $596 billion in revenue in 2025.

With new innovations and technologies continuously emerging, we can expect the ability to understand unstructured data trapped within documents and how we work with and handle the content to intersect virtually every automation system and process.

Document Processing Needs Disrupting

The old ways of document processing and dealing with paper is suppressing business agility. As consumer expectations for an exceptional customer experience (CX) continue to grow, brands and businesses in all industries must look closely at every step of the customer journey to pinpoint where friction may exist. Many companies fall short when it comes to customer onboarding — a highly content centric process. Complexity in digital forms and heavy requirements for manual typing are the biggest barriers when it comes to onboarding customer information. Emerging technologies help automate data extraction, but they still require a good bit of effort from the recipient. They only address the symptoms of a document-creation problem, not the root cause.

Why is document processing so challenging? While there are a few unique challenges, some of the most common ones can include:

  • Enterprise documents containing unstructured data that is difficult for intelligent automation systems to understand.
  • Organizations are unclear how to best connect data to systems and processes.
  • RPA robots are unable to manage the sheer volume of data.
  • The status quo where naysayers don’t see the number of efficiencies that can be gained from a new way of thinking about how to handle documents.
  • Some organizations are still using 10-15 year old legacy capture platforms which need experts to manage it because the systems are not designed for the new user.

Additionally, people still have some misconceptions around what a document is. Many still believe documents are only files but, increasingly, they’re more than that. They can be a set of objects such as metadata, or even documents in cloud databases. Intelligent document processing platforms need to have news skills to automatically process new forms of all types of documents, regardless of format and structure.

Related Article: 5 Common Ways Companies Use RPA to Enhance Document Processing

AI Skills for Documents

Technology innovations and user demands are creating the path for change. The reality is the way enterprises approach transforming their digital operations must be nimble, open and faster than ever, and deliver on the results customers expect. This has inspired a movement towards technologies such as low-code/no-code (LCNC) tools. According to Gartner, LCNC capabilities are the precursor to the fact that in three to five years, as much as 65% of development will be done in-house using LCNC tools, which are complimented by AI software bots.

LCNC platforms make it easier for business users to become citizen developers and be empowered to quickly design, train and deploy skills to intelligent automation platforms. LCNC platforms can add content intelligence skills to RPA and other automation platforms that enable it to understand, extract and classify content without needing to be an expert in machine learning, allowing knowledge workers to be more hands-on with the platforms and get insights from documents that improve productivity and operational efficiency.

But it doesn’t stop there. LCNC tools alone won’t address every need. If you have documents containing unstructured data that need to be turned into structured, actionable information to fuel a process, then you need document skills. An AI-powered document skill encapsulates the advanced technology of Optical Character Recognition (OCR), NLP and machine learning to power the necessary steps to convert a document into actionable data — digitizing the text in a document and interpreting the objects (sentences, words, tables, entities) contained in the document, identifying the document type regardless of the variations, and finally intelligently locating, extracting and validating the data.

Additionally, the availability of pre-trained skills that are trained document models is what drives faster and broader adoption across an enterprise, and ROI for Intelligent Document Processing (IDP). Skills that understand documents right out of the box is what users want today. Similar to how Google Docs lets multiple people contribute in real-time, marketplaces filled with document skills enable collaboration among an ecosystem of users and developers of skills. And when skills are combined with other powerful automation technology (conversational AI chatbots, for example), it not only has a major impact on operations but on CX as well.

While the journey toward intelligent documents may seem like a significant undertaking, the benefits of cost-efficiency, streamlined business processes, smarter and more informed decision-making, improved productivity, and customer satisfaction are much more significant and important.

Related Article: How Low-Code Development Is Transforming Organizations' Approach to Tech