Records Management: A Strategic Foundation for AI
In the race to adopt artificial intelligence (AI), many organisations concentrate on algorithms, models, and advanced technologies. However, a critical foundation is often overlooked: the quality, structure, and governance of the data itself. Records and Information Management (RIM) – the policies, standards, and practices that govern how information is organised, stored, accessed, and maintained—is frequently viewed as a compliance or archival function. In reality, effective RIM is a strategic capability that can determine the success or failure of AI initiatives.
Research and real-world implementation cases consistently show that many AI projects stall or fail, not because the technology lacks sophistication, but because the underlying data is fragmented, poorly structured, or unreliable. AI systems depend heavily on accurate, well-organized, and accessible data to generate meaningful insights. When data is inconsistent or difficult to retrieve, even the most advanced AI models struggle to deliver value.
In simple terms, organisations that apply strong records management principles to their data environment create the conditions necessary for AI to succeed.
By implementing structured data governance, clear information lifecycle management, and standardized record-keeping practices, organisations can significantly improve data quality and accessibility. These practices ensure that data used by AI systems is trustworthy, properly classified, and readily available for analysis.
This article examines how adopting robust records management principles and practices can accelerate AI adoption while addressing common barriers such as data silos, poor data quality, and governance gaps. More importantly, it highlights how Records and Information Management should be repositioned from a traditional back-office function to a strategic enabler of successful AI initiatives.
Organisations without AI-ready data
Lack confidence in their data management practices for AI
AI projects abandoned by 2026
Will be scrapped if they lack quality AI-ready data
$5M Annual cost of poor data
Lost per organisation due to bad data quality (in 25% of firms)
The AI Bottleneck: Disorganized, Siloed Data and Poor Data Quality
Many AI initiatives stall early because they are built on siloed, low-quality, or poorly understood data. AI systems are only as effective as the information they are trained on – a principle often summarized as “garbage in, garbage out.” Unfortunately, many organisations discover too late that their data is not AI-ready.
According to Gartner, 63% of organisations either lack the necessary data management practices for AI or are unsure whether they have them in place. This lack of data readiness has serious implications. Gartner predicts that through 2026, up to 60% of AI projects will be abandoned if they are not supported by “AI-ready” data. In other words, a majority of AI initiatives risk stalling in the pilot phase simply because data is disorganized, siloed, or of poor quality.
Beyond operational challenges, there is also a growing concern about AI accessing sensitive or regulated information, potentially exposing confidential data and increasing the risk of security breaches and compliance violations. Additional research shows that only one-third of organisations have made meaningful progress in AI adoption, with poor data quality identified by chief data officers as the primary obstacle to success. Even when AI projects are not abandoned, teams often spend significant time addressing data issues instead of developing innovative solutions. In fact, data scientists report spending as much as 80% of their time locating, cleaning, and organizing data before they can begin building AI models. This creates a major bottleneck that slows development cycles and significantly increases project costs.
The data quality challenge has persisted for years, with only gradual improvement. For example, a hospital in North America reported in 2021 that an AI system designed to improve patient care never progressed beyond the prototype stage because the required data was spread across 20 different legacy systems, making integration extremely complex. Ultimately, the project was abandoned before it could deliver any value.
More recently, a 2025 report from the UK Government’s Public Accounts Committee warned that AI adoption in the public sector is “at risk” due to poor data quality and incompatible legacy systems. The report emphasized that data issues remain a “persistent and long-standing” barrier, particularly because AI systems rely heavily on high-quality, well-structured data. When data is siloed, inconsistent, or inaccurate, AI models struggle to learn effectively or produce reliable insights. The stakes are high. Poor records and information management does not just slow AI initiatives – it can completely undermine them. Organisations may invest heavily in advanced AI technologies, only to discover that the tools cannot deliver meaningful results because the underlying data is incomplete, outdated, duplicated, or inaccessible.
This is why forward-thinking organisations are shifting their focus toward strong records and information management (RIM) practices as a foundation for successful AI adoption.
Transitioning from a Compliance Burden to a Strategic Data Foundation
Traditionally, Records and Information Management (RIM) has been viewed primarily as a compliance-driven administrative function. However, in the era of AI and data-driven decision-making, it is increasingly recognised as a strategic capability that supports innovation and analytics. At its core, RIM ensures that information is systematically captured, classified, stored, retrieved, and eventually disposed of according to defined lifecycle policies. When implemented effectively, these practices significantly improve data quality, accessibility, and governance, all of which are essential for AI initiatives.
Rather than serving merely as an end-of-life archiving process, modern records management should function as an operational enabler of AI and advanced analytics. By organizing and governing enterprise data, RIM creates a structured repository of trusted information, the exact type of data AI systems require to generate reliable insights. A critical benefit of effective RIM is its ability to break down data silos and bring structure to fragmented information environments. This includes implementing consistent classification frameworks, metadata standards, and enterprise taxonomies that apply across the organisation.
For example, strong RIM programs ensure that customer records, transactions, communications, and operational data are indexed and connected in a standardized way, rather than scattered across multiple systems under inconsistent labels. When organisations prepare data for AI models, well-managed records mean the information is already searchable, understandable, and ready for analysis. As a result, analysts and data scientists spend far less time manually assembling usable datasets.
Research suggests that data integration challenges can exceed the complexity of building the AI model itself. In some projects, organisations spend approximately 60% of their effort integrating and normalising data from legacy systems, compared to only about 20% on model development. Effective records management significantly reduces this burden by ensuring data is consistent, structured, and accessible from the outset.
Strong RIM practices also improve data quality at its source, preventing downstream problems for AI teams. Records managers implement governance policies such as standardizing data formats, classification, eliminating duplicate records, and maintaining accurate and up-to-date information. They also support the development of “single sources of truth” for critical business data, often through Master Data Management (MDM) initiatives. By establishing these practices early in the information lifecycle, RIM helps ensure that data completeness, accuracy, and consistency are maintained before AI or analytics teams rely on it.
Some organisations now implement formal data quality checkpoints, such as completeness, accuracy, and timeliness – that must be met before AI models move into production, specifically to prevent the classic garbage-in-garbage-out problem. Equally important, strong records management builds trust, transparency, and regulatory compliance into enterprise data, all essential components for responsible AI adoption. When data is well governed with clear provenance, auditability, and adherence to retention and privacy regulations, business leaders and regulators can have greater confidence in AI-driven decisions.
Without this governance, organisations face difficult questions:
- Is the data current and reliable?
- Are we legally permitted to use this personal or regulated information?
- Do we have the documentation needed to explain or audit AI-driven decisions?
RIM helps answer these questions by maintaining clear documentation, audit trails, and governance frameworks. For example, a strong records management program ensures organisations know exactly what customer data they possess, whether they have the appropriate consent to use it, and whether the necessary records are retained to justify automated decisions. This foundation significantly reduces the legal, ethical, and operational risks associated with deploying AI.
The importance of trusted data has been recognised at a national level as well. A recent UK policy study recommended new national standards for data quality as part of the country’s AI strategy, reinforcing the idea that enterprise AI initiatives depend on a foundation of reliable, well-governed information. In short, effective RIM transforms data governance and quality from an afterthought into a strategic priority – a shift that is essential for scaling AI responsibly, efficiently, and successfully.
How Strong Records and Information Management Enables AI Success
In essence, Records and Information Management (RIM) provides the foundational data infrastructure and quality controls that AI systems need to succeed. When records are managed effectively throughout their lifecycle, AI initiatives can begin with reliable, well-organized data, minimizing the need for extensive cleanup or clarification.
As a result, AI teams can focus on developing algorithms, refining models, and generating insights, rather than spending weeks or even months, performing data preparation and cleanup tasks. This shift significantly accelerates development timelines, reduces costs, and improves the overall effectiveness of AI projects.
Equally important, AI-generated outputs themselves become valuable records. Insights, automated decisions, predictive outputs, and AI-generated content all create new information assets that must be captured, governed, and preserved appropriately. Organisations with mature information management practices are better positioned to manage these AI-generated records within a continuous cycle of data creation, analysis, and learning.
In this way, strong RIM capabilities create a virtuous cycle:
well-managed data enables effective AI, and AI in turn generates new insights that feed back into the organisation’s information ecosystem.
Organisations that already maintain robust information governance frameworks are therefore better prepared to capture, classify, and manage AI-generated outputs while maintaining compliance, transparency, and accountability.
The Impact of Strong Records Management on AI Success
As the comparison illustrates, a strong records management foundation can be the defining factor between AI initiatives that succeed and those that struggle. When Records and Information Management (RIM) practices are in place, data quality and accessibility stop being obstacles. AI projects can move forward efficiently, enabling organisations to achieve results faster and with greater confidence.
Without this foundation, organisations often find themselves stuck in prolonged data cleanup efforts, dealing with unreliable outputs, or even pausing AI initiatives entirely until fundamental data issues are resolved.
Global Perspectives and Real-World Examples
Organisations around the world, across both government and private industry are increasingly recognising the connection between information management and successful AI adoption.
This relationship is particularly evident in the public sector. In 2025, the UK Public Accounts Committee identified poor data quality as a major barrier to AI adoption in government, prompting calls for increased investment in data cleanup efforts and modernization of legacy records systems.
Similarly, in Australia, the Digital Transformation Agency’s trial of Microsoft 365 Copilot highlighted another risk:
“Existing deficiencies in data security and information management may be magnified by AI use.”
This warning underscores a critical point – organisations must address information governance fundamentals before scaling AI tools across the enterprise.
Private-sector findings reinforce the same conclusion. In a recent data trust survey, 68% of Chief Data Officers reported that inconsistent data quality is undermining their AI outcomes. Gartner’s analysis similarly indicates that organisations failing to modernize their data management practices are more likely to see AI projects underperform or be abandoned altogether.
When Strong Information Management Accelerates AI
While poor data governance can slow AI progress, organisations that prioritize records and data management are seeing the opposite effect, faster and more successful AI deployments.
Many forward-thinking companies now treat data as a strategic asset, investing in initiatives such as:
- Enterprise-wide data inventories
- Data quality improvement programs
- Cross-department data governance committees
- Modern, interoperable records management systems
For example, one energy company significantly accelerated its AI deployment timeline after revamping its records classification system and consolidating critical data sources into unified “single source of truth” platforms. With trusted and accessible data already in place, predictive AI models could be implemented much faster.
In another case, a financial services firm expanded its existing data governance framework to include AI-specific policies covering issues such as bias, transparency, and accountability. Because these policies were built on top of an established records management program, the organisation was able to deploy AI models trained on well-governed, compliant datasets.
Even basic RIM practices such as data documentation and information inventories can reveal valuable insights. One global manufacturing company discovered that a small number of high-value datasets supported nearly 80% of its potential AI use cases. By focusing records management efforts on those key datasets, the company dramatically accelerated the impact of its AI initiatives.
The Expanding Role of Records Management Professionals
As AI adoption grows, records management professionals are taking on increasingly strategic roles within organisations. They are collaborating with data scientists, AI engineers, and governance teams, helping ensure that data quality, ethics, and compliance remain central to AI development.
In many organisations today, records management leaders are now participating in AI governance boards or cross-functional task forces, where they help ensure that:
- AI systems use high-quality, trusted data
- Proper documentation exists for AI decisions and processes
- Regulatory and compliance requirements are met
This evolution reflects a broader cultural shift. Records and Information Management is no longer just a back-office archival function focused on retention schedules. Instead, it is becoming a critical component of enterprise digital strategy, resilience, and innovation.
As Rachael Greaves, a records management CEO, noted, the value proposition of records management has evolved. Today, it is about organisational resilience, information security, and leveraging data as a strategic asset for competitive advantage.
Elevating Records Management to Unlock AI’s Potential
Ultimately, treating Records and Information Management as a strategic priority is one of the most effective ways organisations can accelerate AI adoption.
Rather than viewing RIM as a compliance checkbox or administrative burden, leaders should recognise it as the foundation of AI readiness. Clean, reliable, and accessible data does not happen by accident it is the result of intentional governance, strong information practices, and disciplined data management.
The benefits are clear. Organisations with strong information foundations can deploy AI faster, achieve more accurate outcomes, and build greater trust in AI-driven decisions. By investing in these capabilities today, companies are effectively future-proofing their AI initiatives.