The transformative role of AI in the next generation of records management
While there are many ways AI will disrupt and advance the records management process, these four key applications will make the biggest impact:
• Automating document classification and tagging. AI algorithms can be trained to automatically classify and tag documents based on their content, saving a great deal of time in the process. Once trained, AI can then identify document types, relevant keywords, and metadata. GenAI takes this a step further by “understanding” the longer context of documents, making more nuanced classifications, and leveraging the data for summarization or new content creation. Automating this process reduces the need for manual intervention, improves accuracy, and enables faster retrieval of records.
• Records retention and data hygiene. Many organizations keep records for far too long in their original format, which expands their business risk. As more data and records are added to databases, GenAI helps to resolve this issue by anonymizing customer information and PII to remove confidential information, while retaining the business data that can be used to increase the quality of insights and business intelligence.
• Leveraging natural language processing (NLP) for record analysis. NLP techniques can be used to analyze unstructured data within records, extracting insights,sentiment analysis, and identifying patterns or trends from the text foundation of documents. It allows users to search for information from documents using natural language queries, making it easier to find specific information within large datasets and enabling the interpretation of unstructured data within records. GenAI can enhance this process by understanding complex language nuances and making more sophisticated analyses, such as identifying implicit meanings or cultural contexts within records.
• Predictive analytics for records management. AI algorithms can analyze historical records data to predict future trends or outcomes. For instance, predictive analytics can forecast document access patterns, anticipate changes in regulatory requirements, or predict potential risks associated with certain types of records. AI can improve predictive accuracy by synthesizing diverse data sources and considering broader contextual factors beyond traditional analytics.
AI in action
In the U.S., AI is being applied to streamline and simplify records management by a digital transition project of the National Archives and Records Administration (NARA) that is still in the early stages (geekwire.com/2023/how-the-national-archives-is-using-ai-to-make-records-more-accessible-in-the-digital-age). Once rolled out, it will help to better manage Freedom of Information Act (FOIA) requests by leveraging AI to automatically redact PII.
The National Archives is also in the process of digitizing records as part of the Federal Agencies Digital Guidelines Initiative (FADGI), a broader effort to move away from paperbased processes toward digitization (digitizationguidelines.gov/FADGI). FADGI, which is scheduled to go into effect in June 2024, is designed to increase the quality of documentation that the National Archives stores.
Currently, more than 250 million records have been scanned and digitized out of more than 13.5 billion total pages. The National Archives aims to increase this number to 500 million digitized records.
Final thoughts
As more organizations, including the federal government, adopt AI for records management, it will reap a series of benefits, including lightening the document manager’s workload by automating many mundane tasks. AI will also streamline workflows and improve decision-making processes. This will enhance efficiency and unleash data that can be leveraged across the organization, positively impacting the bottom line while creating competitive differentiation.