Artificial Intelligence (AI) is poised to significantly reshape the Records and Information Management (RIM) field over the next five years. AI offers transformative potential by enabling organizations to manage records more effectively and comply with evolving legal and regulatory requirements. However, this progress is not without challenges. Issues such as data quality, system integration, and ethical considerations must be addressed to harness AI’s full capabilities. Here, we explore AI’s challenges, potential uses, and benefits in RIM.
Potential Uses of AI in Records Management and Legal Discovery
Despite these challenges, AI holds immense potential for revolutionizing RIM practices. One of AI’s most promising applications lies in automating routine tasks such as data classification, metadata tagging, and records retention scheduling. By automating these processes, AI-supported applications can reduce manual workloads, minimize errors, and ensure greater consistency in records management practices. The Association for Intelligent Information Management (AIIM) notes that organizations with mature data strategies are 1.5 times more likely to experience enhanced efficiency and decision-making from their AI investments.[1]
AI offers transformative capabilities in legal discovery. Natural Language Processing (NLP) enables AI systems to understand and interpret human language, allowing them to search vast datasets more effectively than traditional methods. For instance, AI-powered tools can identify and retrieve relevant records for legal cases in a fraction of the time it would take using manual methods. This accelerates the discovery process and ensures more comprehensive and accurate results. The growing role of AI in eDiscovery has led to legal discussions, including landmark cases such as Da Silva Moore v. Publicis Groupe (2012), where courts recognized AI-assisted document review as an acceptable method for legal discovery. This precedent has influenced subsequent rulings, reinforcing the use of AI in identifying relevant records for litigation.
AI also supports compliance monitoring by identifying records that contain sensitive information or fail to meet regulatory requirements. Machine learning algorithms can flag potential compliance risks, such as records containing Personally Identifiable Information (PII) that have not been adequately secured. This proactive approach helps organizations mitigate legal and reputational risks while fostering a culture of accountability.
With cyber threats on the rise, AI-driven anomaly detection can identify suspicious activity, unauthorized access, or potential data breaches before they occur. The National Institute of Standards and Technology (NIST) supports the integration of AI in cybersecurity frameworks, emphasizing its role in proactive threat detection and response.
AI and FOIA Requests
AI has the potential to transform how government agencies handle FOIA requests. Traditionally, processing FOIA requests are time-consuming, involving manually reviewing records to determine what can be disclosed and redacted. AI-powered automation can expedite this process by identifying relevant records, applying redactions to sensitive information, and categorizing responses more efficiently.
For example, AI-driven Natural Language Processing can scan large volumes of government emails, reports, and documents to identify key phrases, classify information, and detect sensitive content requiring redaction. Machine learning algorithms can help agencies predict which documents will likely be exempt from disclosure, reducing the time spent on manual reviews.
AI in Declassification Processes
Declassification of government records is another area where AI can offer improvements. The current process is often slow and labor-intensive, requiring subject matter experts to review classified records and determine their eligibility for release manually. AI engines can assist in automating aspects of this process by analyzing documents for classified content and flagging information that should remain restricted.
AI can also enhance compliance with declassification policies by ensuring consistency in classification reviews. By leveraging historical data and established classification guidelines, AI-powered systems can suggest declassification decisions, reducing the burden on human analysts while improving the accuracy of document reviews.
Challenges Posed by AI in Records and Information Management
The integration of AI into RIM introduces several challenges that must be addressed. One of the most significant challenges is data quality. AI systems depend on accurate, structured, comprehensive data to deliver reliable outputs. However, many organizations face the problem of fragmented, unstructured, and incomplete data repositories. According to AIIM, 78% of organizations report challenges in managing the volume, velocity, and variety of information generated.[2]
Ethical considerations also represent a critical challenge. AI algorithms are only as unbiased as the data they are trained on. Biases in training data can perpetuate inequities in decision-making processes. For instance, automated classification systems may inadvertently misclassify records, leading to compliance risks or inefficiencies. The Association for Records Managers and Administrators (ARMA) emphasizes the importance of ethical frameworks to ensure accountability and fairness in AI applications.[3]
Technical barriers further complicate AI adoption in RIM. Many organizations use siloed information systems that lack interoperability. This fragmentation hinders the integration of AI tools across different platforms. AIIM’s research indicates that only 26% of organizations have integrated document and records management systems with other core applications, underscoring the need for greater system interoperability.[4] Addressing these technical and organizational barriers is essential for maximizing the benefits of AI.
Another key challenge is regulatory compliance. As AI-driven records management evolves, it must align with existing legal frameworks such as the Federal Records Act (FRA), Freedom of Information Act (FOIA), and international regulations like the General Data Protection Regulation (GDPR). The Information Governance Initiative (IGI) stresses that AI be governed by robust policies to ensure compliance with these frameworks, especially regarding records retention, disposition, and accessibility.
Most industry sources highlight that organizations need robust metadata strategies to support AI-driven records classification and retrieval. AI’s ability to streamline RIM processes is significantly reduced without standardized metadata and governance structures. Ensuring metadata integrity is, therefore, a crucial component of AI integration into records management. Brillent’s ALICE® Digital Records and Information Management System (DRIMS™) Can assist organizations in automating metadata capture as well as fully automating the records management processes from ingestion to full lifecycle management.
The U.S. National Archives and Records Administration’s (NARA) Position on AI
The U.S. National Archives and Records Administration (NARA) recognizes the transformative potential of AI in federal records management and is actively exploring its applications. In alignment with Executive Order 13960, NARA has prepared an inventory of AI use cases, including current and planned applications consistent with the agency’s mission.[5]
One notable initiative is NARA’s pilot project to use AI for screening and flagging Personally Identifiable Information (PII) in digitized archival records. This project aims to automate the identification of sensitive information, enhancing privacy protections while improving public access to records. Additionally, NARA is developing an AI Ethics Review Team to assess the ethical implications of AI projects, ensuring that deployments align with public trust and legal standards.[6]
Furthermore, NARA’s new Strategic Framework emphasizes building digital capacity through the responsible use of AI. The agency invests in AI research and development to advance responsible AI technologies, aiming to incorporate AI into archival recordkeeping and information-sharing practices to make records more accessible to the public.[7]Growtika
Conclusion
AI represents a transformative force in Records and Information Management, offering significant opportunities to improve efficiency, compliance, and accessibility. By incorporating industry insights from AIIM, ARMA, and IGI, organizations can develop a more informed approach to AI integration. As AI continues to evolve, its impact on RIM will undoubtedly shape the future of information governance, the range of document processing efforts, and compliance practices.
Resources
[1] Blackburn, Alyssa, “New Study Reveals Crucial Role of Information Management in AI Success, AIIM, April 3, 2024,https://info.aiim.org/aiim-blog/new-study-reveals-crucial-role-of-information-management-in-ai-success.
[2] Miller-Liu, Tori, “2023 State of the Intelligent Information Management Industry Report,” Association for Intelligent Information Management (AIIM), April 21, 2023, https://info.aiim.org/aiim-blog/discover-the-future-of-intelligent-information-management-aiim-2023-state-of-iim-report.
[3] Mooradian, Norman, Ph.D., “AI, Records, and Accountability,” Information Management Magazine, ARMA-AIEF Special Edition, Association of Records Managers and Administrators, November 12, 2019,https://magazinearma.org/2019/11/ai-records-and-accountability/.
[4] Miller-Liu, 2023
[5] “Inventory of NARA Artificial Intelligence (AI) Use Cases, U.S. National Archives and Records Administration (NARA), https://www.archives.gov/ai.
[6] Shakir, Gulam (CTO), “NARA AI Compliance Plan for OMB Memorandum M-24-10,” National Archives and Records Administration (NARA), September, 2024, https://www.archives.gov/files/AI/nara-ai-compliance-plans-final-9-24-2024.pdf.
[7] “National Archives’ New Strategic Framework Emphasizes Building Capacity Through Responsible Use of Artificial Intelligence,” National Archives News, October 17, 2024,https://www.archives.gov/news/articles/new-strategic-framework-artificial-intelligence.