The Overlooked AI Value Center: Document Management

By: Anthony Riggio

How You Should Be Using Artificial Intelligence Today

  1. AI Controversy & Confusion: Overcoming Adoption Barriers
  2. Intelligent Content Processing: The Entry Point for AI Enterprise Strategy
  3. Harnessing & Managing AI: Advanced Document Processing, Productivity & Efficiency
  4. AI Document Management: Immediate and Specific Applications
  5. VisualVault Enterprise Content Management System: Setting Up AI Document Management For the Future
  6. What AI Document Management 2.0 Will Look Like

I. AI Controversy & Confusion: Overcoming Adoption Barriers

All the conflicting headlines about AI these days can be a cause for hesitation (or even trepidation) for many organizations as they consider how to leverage AI within their operations. However, it’s safe to say that most understand it as a technology that is here to stay and that they’ll need to play in the AI sandbox to some degree or another.

We hear that AI has been a major driver of stock markets hitting record highs in 2023 and companies are quickly jumping on the AI bandwagon. At the same time, we also hear troubling developments about newly-launched advanced AI search tools and other AI technologies that are being used to spread outright false, biased or highly inaccurate information. In some cases AI is even being used to create false artificial entities for committing fraud, to extract personal information from individuals or for other nefarious activities having a detrimental impact on broader society.

While both the negative and positive aspects of AI are largely true, it is important to remember that AI is not a new technology and has been delivering benefits large and small for a number of years. These range from voice-activated smartphone assistants to GPS devices, and AI is now embedded in virtually every interaction and transaction across our economy.

Moreover, in the business application context, AI advanced analytics platforms have been around for more than a decade providing critical business intelligence and insights across a range of industries.

For example, in Healthcare, applicants have spanned everything from speeding up drug clinical trials through precise site mapping and patient population recruitment to improving hospital and health system operational and clinical processes and outcomes.

The fact is that there are a number of ways companies can practically deploy AI today that are low risk and high value. Moreover, they can serve as the foundation for a broader, enterprise-level AI strategy. In fact, there is one fairly obvious but often overlooked entry point for organizational AI implementation that should be the engine for later AI-driven initiatives across the enterprise. This pivotal AI entry point is the collection of digital and offline hubs and repositories where all company information and knowledge is located. It is contained in every cabinet of physical document files and every terabyte of stored documents, images, content, and data. The pivotal entry point is every organization’s enterprise document and content management processes and technology systems.

II. Intelligent Content Processing: The Entry Point for AI Enterprise Strategy

For artificial intelligence applications to flourish within an organization’s operations, they must be able to tap into and understand the aggregate native intelligence inputted, collected and stored within it. That intrinsic intelligence is found in its technology systems that house its content. That content encompasses documents, data (both already structured as well as unstructured), images and other digital formats that contain valuable business intelligence. Deploying AI tools in other areas within a company without first harnessing them to optimize archived information and content hubs would essentially result in organizations working in a vacuum.

AI needs to comprehensively learn and understand how an organization works and its digital information hubs are the treasure trove of knowledge it can engage with as the starting point for that process. As the frequency with which they interact with digital content and data increases over time, the AI tools teach themselves – learning more and more about the company. The intelligence gained can then be integrated with other systems and processes that share knowledge with the entire organization.As soon as an organization can evaluate their current methods and platforms for processing and managing documents, content and data, they can then identify approaches and AI technologies that can make these processes smarter. When these steps are completed, the organization can map out and execute an intelligent master AI strategy across their enterprise. For example, an AI tool called Generative Pretrained Transformer (GPT) that requires little coding, can be deployed for accelerated categorizing of large volumes of content with virtually zero effort.

III. Harnessing & Managing AI: Advanced Document Processing, Productivity & Efficiency

Machine Learning (ML) AI are intelligent algorithms that can be “pre-trained” with specific instruction sets and parameters. ML has long been the foundational process for how AI technology was implemented across business applications, and still is largely today. However, the more complex engineering and development timelines associated with ML are often unnecessary and can delay faster AI implementation in some business applications.

In fact, there has been an advent of more streamlined, less complex AI models like GPT and Large Language Modeling (LLM). These models utilize simplified prompts that can tell a document processing system how to categorize and search for document types as well as specific values within documents and content. GPT and LLM-based AI can have a major impact on expediting more immediate information access and governance. While GPT or LLM is deployed, engineering teams can still build advanced ML models to support the longer-term development of self-learning, intelligent AI-driven information management. Whether GPT, LLM or older ML methods are applied, it is vitally important for the organization to reach consensus on establishing the rules and parameters for prompts and instruction sets. These rules must define who has rights to access and alter them if necessary, and how any changes are communicated enterprise-wide. Once GPT prompts, LLM or ML-based document management command protocols are established and agreed to, companies should closely monitor how well the AI is working. That performance monitoring should include whether document automation processes are becoming more efficient as well as flagging any divergences or disruptions from the commands added to the system.

IV. AI Document Management: Immediate and Specific Applications

ML-based AI technology, together with natural language processing (NLP) AI, can be implemented with near-term benefits in a number of document management processes and system functions.

Smart Text Extraction

A company’s physical documents and archived digital document files are embedded with countless content elements and values. When analyzed in aggregate and over time, these can reveal important trends and insights that can be leveraged to improve operations. Algorithms or GPT prompts can be programmed with instructions to look for specific text categories and descriptions in digital documents. These targeted text content searches can reveal intelligence that directs an organization to learn more about a topic. The AI also teaches the document management system over time to more efficiently define, find and access these text values. Ultimately, the result is the system getting smarter as it scans, processes, and engages with more and more document text coming into the organization.

Another AI technology, natural language processing (NLP), can read and interpret the context of document texts and hand-written notes on a document, integrating with and adding to ongoing text-based learnings. It therefore behooves companies to digitally convert as much of their paper document inventory as possible. These assets potentially contain rich text-based insights that can be integrated with digitally captured insights that can collectively better inform learnings. All of the intelligence can, in turn, impact business strategy, forecasting and operational process changes and improvements. AI eliminates time-intensive and error-prone manual review of digital and physical documents for faster insights analysis.

Document Auto-Classification

GPT prompts or machine learning-based algorithms can also be trained with instructions for identifying specific types and formats of already stored digital documents. The AI can then auto-categorize them for easier and faster document management system search and retrieval. Again, the AI continually makes the system more intelligent, so document auto-classification increases in accuracy as the algorithm interacts with more documents scanned into the system over time.

Document De-Duplication Filtering

Organizations manually review documents (via internal staff or costly third-party services) for duplicate content that can be deleted. However, there may be several sections of high value content that information management teams want to make sure they don’t mistakenly delete.

Semantic De-Duplication technologies, which combine machine learning with Natural Language Processing (NLP) AI tools, enable the creation of a unique signature identity for each document. This allows for the calculation of a similarity score between documents. These similarity scores are then compared to predetermined “similarity thresholds”. The thresholds are able to semantically filter, tag, and rephrase text content on scanned document pages that have similar keywords, language, or same but differently phrased meanings.

As the document management system processes more de-duplication actions over time, the semantic de-duplication tools become more efficient, accelerating the overall process. Additionally, the AI can tag and categorize the de-duplicated content values, facilitating efficient searches for documents containing those values.

It is, therefore, critically important for organizations to digitize filed paper documents to identify duplicate page copies that can be discarded. This should potentially reduce physical paper storage space and reduce paper document review costs significantly.

Document Workflow Optimization

Manually defined workflow processes, in which IT systems are used, may not be optimized for staff productivity. AI algorithm technology can be embedded into document management systems to monitor pre-set daily workflows and workstreams used by teams and automatically correct existing inefficiencies in document-based workflows. The AI can also build new, more productive work processes as it analyzes and learns about workflow patterns and outcomes over time, all without disrupting current business operations.

Enhanced Document Security & Governance Oversight

In many cases, a significant percentage of an organization’s physically or electronically stored documents cannot be destroyed for an extended period of time due to legal or regulatory compliance requirements. Once the AI-directed document auto-classification process is completed, the ML algorithm can be taught to identify auto-classified documents that are regulated. The AI then tags them with retention and handling rules to ensure compliance with government and legal authorities, facilitating their retrieval if a document audit is ever requested.

Document Learnings & Insights

AI algorithm technology can also create hyper-automated predictive document analytics processes that can generate company business forecasts and trends for informing changes and adjustments in business strategies. All these learnings come from what an organization’s constantly expanding document, data and content inputs are telling the algorithms over time.

One example is that with AI predictive analytics, companies can better understand trends with their Revenue Cycle Management (RCM) processes by analyzing volumes of documents containing financial data. By feeding financial ledger documentation into the document management system, AI’s document classification and deep analytics capabilities can enable an organization to predict what the future might look like. It can then set parameters for both red flag alerts and for success benchmarks and milestones.

V. GRM’s VisualVault: Setting Up AI Document Management for the Future

AI technology is dramatically changing enterprise document and content strategies, processes, and software platforms. Organizations that have advanced enterprise document and data management systems like GRM’s VisualVault already have a solution embedded with a range of AI tools and models. With VisualVault, these AI capabilities will scale with expanding document and content processing needs, requirements and ever-morphing enterprise information reassessments. These tools will evolve as AI technology evolves.

Many companies today still have less mature or less advanced digital content management applications or hybrid physical/digital approaches for document capture, processing and storage. The emergence of deep AI-integrated advanced content configuration platforms like VisualVault ™ present a timely opportunity to take the AI plunge in a focused, realistic manner with near-term tangible results. Finally, employing the VisualVault ™ platform as the central information management hub, can serve as a key infrastructure backbone for architecting a company’s longer-term wider enterprise AI implementation strategy. The platform can integrate with an organization’s other ERP and IT systems.

VI. What AI Document Management 2.0 Will Look Like

Document and information management is an enterprise functional area that is a critical organizational gateway and conduit to how AI can fundamentally transform business processes. AI technologies will continue to perfect their learning of information and content collaboration workflow best practice processes and outcomes across work teams. As a result, AI process management will emerge as a leading category that companies can take advantage of for optimizing. AI predictive analytics-based forecasting.

While enabling great leaps in information processing efficiency, today’s AI document management 1.0 still needs to store and create reports of AI-extracted data to aid development of insights by company teams. AI document management 2.0 will cut out the AI-extracted data storage step. The next iteration of the technology will read document content, uncover new insights and build predictive analytics on the fly. Additionally, this next iteration of AI document management will intelligently optimize predictive analytics parameters over time to generate improved business outcomes.

Ultimately, controls, rules and protocols must be established by consensus and vigorously governed and tested for how generative AI algorithm models are trained by companies. Only then can AI document content management systems best redefine formats, configurations and process workflows for how overall information is created, ingested and analyzed for insights and auto-change operational improvements in real time.

Today’s AI technologies will begin to fundamentally shift the way businesses will operate in far more nimble ways. They will eliminate many of the process inefficiencies, planning and forecasting blind spots that have long existed. Tomorrow’s AI technologies will produce a new AI-trained workforce and AI job opportunities that will grow and expand businesses exponentially. The critical mass for these promising outcomes will begin with every company’s centers of curated, living knowledge – its digital document and information systems.

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