As AI’s impact on businesses’ operations grows, so too does the need to maintain the integrity and availability of the data powering these tools and platforms. Without robust management Information sources can quickly become outdated and inconsistent. Recent research finds that the financial consequences of bad data can be in the hundreds of thousands.
Organisations with weak internal data controls risk unintentionally amplifying misinformation across their operations, leading to flawed decisions and costly outcomes. In fact, the productivity and efficiency gains promised by AI’s strongest advocates are impossible to realise if the underlying data is poor. AI can scale bad information just as rapidly as good.
Increasingly, organisations recognise that appointing a dedicated data custodian is essential to safeguard against these risks.
This is giving rise to a new, dedicated role that organisations heavily reliant on AI will increasingly need to establish: the AI librarian.
What is an AI librarian?
An AI librarian’s role is dedicated to maintaining the integrity, accuracy, and discoverability of an organisation’s data and information. They ensure that knowledge is properly collected, organised, and made retrievable. As an outcome AI systems can deliver reliable, actionable insights.
As AI technology rapidly spreads across businesses, this role is becoming essential for managing and governing the flow of information within the enterprise.
Traditional librarians shaped the information age by organising texts and safeguarding written knowledge. Today, AI librarians are becoming indispensable in the AI era. Businesses that fail to establish this role risk allowing fragmented, outdated, or biased information to erode the value of AI initiatives and the decisions made based on those outputs.
The importance of curated, trusted knowledge
AI’s success in business depends on being powered by curated, trusted internal data, not just broad public web sources. For example, an HR team looking to generate accurate headcount forecasts or compensation analyses would benefit far more from up-to-date internal data on employee roles and salaries than from general industry benchmarks.
While public data is abundant, it increasingly lacks the necessary context, accuracy, and relevance for precise decision-making. This challenge is further heightened by the fact that much of today’s internet content is now AI-generated, compounding the risks of misinformation and shallow insights.
Curating and maintaining data sources is complex and requires dedicated oversight. Relying on the individual contributors and teams alone makes it difficult for leaders to ensure standards are upheld and processes followed. That’s where a dedicated custodian becomes essential.
Human oversight plays a critical role in setting up systems that deliver meaningful, reliable insights. AI hallucinations are a reminder that the technology can generate false answers by stitching together information from scattered sources.
A hybrid approach, where AI handles processing and humans review and refine outputs, safeguards accuracy and relevance. This requires continuous feedback loops and proactive audits to ensure that even rarely accessed data remains current and reliable
As businesses rely more heavily on AI, the AI librarian takes on a crucial role in safeguarding the integrity and usability of organisational knowledge. Their core responsibilities should include, but not be limited to:
- Human oversight: Supporting the effective use of AI tools by providing training, promoting a clear understanding of their limitations, and maintaining ongoing human involvement
- Providing contextual data classification: Applying structured classification models to ensure that every article, decision, or data point is properly categorised and anchored in the right context. This enables more accurate information retrieval and helps users understand the origin of information.
- Implementing data governance frameworks: Creating and maintaining robust strategies that ensure information remains accurate, relevant, and compliant with internal and external standards.
Mitigating the sprawl of low-quality data
To deliver meaningful outcomes with AI, organisations need more than just advanced tools, they need the right processes to support them. This includes embedding human-in-the-loop workflows that keep expert oversight in place at key decision points, as well as implementing continuous monitoring systems to track data quality and model performance over time.
AI librarians play a critical role in managing these processes, providing clear ownership and accountability for the integrity, relevance, and alignment of data with business objectives.
While AI is a powerful force multiplier, without structured oversight it can just as efficiently amplify bad information as good. Robust data quality frameworks, proactive audits, and feedback loops are essential to prevent the spread of bias, inaccuracies, or misinformation. Together, these practices not only protect the quality of outputs but also help sustain long-term trust and confidence in AI-powered tools across the organisation.
The future of AI librarians
Forward-looking leaders should act now to establish formal accountability for knowledge integrity within their organizations, ensuring the accuracy, reliability, and alignment of their knowledge base. This accountability will lead to better AI outcomes. It will also empowers teams to make informed decisions, generate new ideas, and develop innovations built on a strong, trustworthy foundation.
As AI adoption accelerates, companies that secure their knowledge foundations now will unlock greater productivity, sharpen decisions, and deliver a lasting advantage.
Deel is the all-in-one payroll and HR platform for global teams. It helps companies simplify every aspect of managing a workforce, from onboarding, compliance and performance management, to global payroll, HRIS and immigration support.
