In 2025 Artificial Intelligence (AI) will continue to redefine the way we live, work, and interact. From improving healthcare outcomes to optimising supply chains, AI projects hold the promise of unprecedented advancements. Accuracy, explainability, transparency are often cited, amongst others, as key concepts in discussions about successful implementation of AI projects. However, one critical component is sometimes overlooked: good records management.  

Data Integrity and Quality 

The foundation of any AI system, especially Generative AI, is data. AI algorithms rely on vast amounts of data to learn, make predictions, and generate insights. Therefore, the accuracy, completeness, and reliability of this data are paramount. Good records management ensures that data is systematically collected, organised, and maintained throughout its lifecycle. By implementing rigorous records management practices, organisations can avoid the pitfalls of incomplete or inaccurate data, which can lead to flawed AI models and unreliable outcomes. 

In the healthcare sector, AI models are increasingly used to diagnose diseases and recommend treatment plans. The accuracy of these models depends on the quality of medical records and patient data. Poor records management can result in missing or erroneous data, potentially jeopardising patient safety and leading to incorrect diagnoses. Conversely, well-managed records provide a robust dataset for training AI algorithms, enhancing their accuracy and reliability. 

Compliance with Legal and Regulatory Requirements 

Good records management practices are essential for ensuring compliance with legal and regulatory requirements. AI projects often involve the collection and processing of personal data. The GDPR impose stringent requirements on how organisations handle this data. By maintaining accurate and up-to-date records of data collection, usage, storage, and disposal, organisations can demonstrate their commitment to data protection and privacy. Additionally, effective records management enables organisations to respond promptly to data access requests, audits, and inquiries, further enhancing compliance and transparency. 

Data Security and Risk Management 

Data breaches and cyber-attacks are significant threats to AI projects, as they can compromise the integrity and confidentiality of sensitive information. Good records management practices play a crucial role in mitigating these risks. By implementing robust data governance frameworks, organizations can establish clear protocols for data access, storage, and protection. 

Effective records management involves the use of encryption, access controls, and regular audits to safeguard data against unauthorized access and breaches. In the event of a security incident, well-managed records provide a clear trail of data activity, enabling organisations to quickly identify and address vulnerabilities. This proactive approach to data security not only protects the organisation’s assets but also fosters trust among stakeholders. 

Facilitating Data Integration and Interoperability 

AI projects often require the integration of data from multiple sources, including internal databases, external partners, and public datasets. Good records management practices facilitate seamless data integration and interoperability, ensuring that data from diverse sources can be combined and analysed effectively. 

By standardizing data formats, metadata, and classification schemes, records management enables organizations to harmonize disparate data sets and create a unified data repository. This interoperability is essential for the development of comprehensive AI models that leverage diverse data inputs to generate more accurate and holistic insights. Moreover, well-managed records provide a clear audit trail, allowing organisations to trace the provenance and lineage of data used in AI projects. 

Enhancing Accountability and Transparency 

Transparency and accountability are critical factors in the ethical deployment of AI systems. Stakeholders, including customers, regulators, and the public, demand visibility into how AI models are developed, trained, and used. Good records management practices provide the documentation and audit trails necessary to demonstrate accountability and transparency. 

For example, the development of an AI model for credit scoring requires documentation of the data sources, algorithms, and decision-making processes used. Effective records management ensures that this information is systematically recorded and readily accessible for review. In cases where AI decisions are challenged or questioned, well-maintained records provide the evidence needed to explain and justify the outcomes, thereby enhancing accountability and trust. 

Good records management is the linchpin of successful AI implementation. It ensures data integrity and quality, facilitates compliance with legal and regulatory requirements, enhances data security, enables data integration and interoperability, and promotes accountability and transparency. As AI continues to evolve and reshape industries, organisations must prioritise robust records management practices to unlock the full potential of their AI initiatives. By doing so, they can build a solid foundation for sustainable and ethical AI deployment, ultimately driving innovation and creating value for all stakeholders. 

Get ahead of the game with our Information and Records Management Practitioner Certificate.  Whether you are a records manager, Freedom of Information Officer or Data Protection Officer this practitioner level certificate will teach you the theory of records management alongside practical hands-on application. The next course starts in two weeks with a special introductory price. Places are limited, so please book now to avoid disappointment.