Does your organisation need an AI librarian?
An AI librarian ensures data accuracy and integrity, bridging the gap between powerful AI tools and reliable, human-validated knowledge
As AI transforms business operations, a pressing need is emerging – the need to ensure the accuracy, integrity, and discoverability of an organisation’s knowledge. Without structured oversight, knowledge can quickly become outdated, inconsistent, or misleading. And because AI excels at scaling information – organisations without robust internal controls risk accelerating the spread of misinformation internally, and to their users.
Businesses in Singapore are already grappling with these challenges, with 68% citing concerns about data timeliness and quality, and 61% struggling with inconsistent data sources. To address this, a new function is taking shape: the AI librarian. This role ensures that AI models are not just powerful but also trustworthy, curating and structuring knowledge to enhance, rather than replace, human expertise.
How businesses can capitalise on existing knowledge
For businesses, connecting AI systems to internal knowledge bases can be a game-changer. From human resources to sales to customer support, AI tooling can ensure that the collected corporate knowledge and expertise is both accessible and actionable. Today, three in five Singapore organisations are already using virtual assistants powered by AI in HR according to a Deel report, while 73% of sales teams have either implemented or are experimenting with AI technologies, according to Salesforce. The Deel report also found that 98% of Singapore organisations have either already integrated AI into their operations or are considering it as a way to enhance business.
However, AI is only as good as the knowledge it’s built on. Poorly managed information can lead to flawed decision-making, misinformation, and compliance risks, especially in highly regulated industries like finance and HR.
To harness the full potential of AI-driven knowledge management, businesses can consider:
- AI-driven classification: a categorisation model isn’t just about improving information retrieval, it ensures that every data point, decision, or article is anchored in the necessary context to be meaningful. Understanding where information comes from, who it’s for, and how it should be used is just as important as the information itself.
- Data governance strategies: Establishing robust frameworks to ensure accuracy, relevance, compliance, and clear ownership and accountability within AI models.
- Human oversight: Ensuring AI tools are used effectively through training, clear understanding of their limits, and continuous human participation in quality control.
At Deel, we’ve built sandbox environments to test models under controlled conditions, alongside rigorous maintenance and oversight to ensure high-quality outputs. Each dataset is compartmentalised, preventing AI from accessing unauthorised information and reinforcing compliance and trust.
The importance of human validation in AI
AI-driven knowledge management isn’t a set-it-and-forget-it solution. Without human validation, AI can scale biases, errors, and outdated information just as easily as accurate data. While AI processes vast amounts of information, humans provide the necessary context, interpretation, and oversight to ensure its relevance and reliability.
A hybrid approach where AI assists with processing while humans validate, review, and refine outputs ensures ongoing accuracy. Continuous feedback loops, regular audits, and proactive reviews prevent critical but infrequently accessed knowledge from becoming outdated, reducing risks when it’s suddenly needed.
Like the first Microsoft spreadsheet in 1981, AI is a force multiplier that boosts productivity but requires human judgment to be effective. By integrating AI with active human oversight, companies can scale knowledge responsibly, maintaining accuracy, trust, and ethical standards.
Building robust knowledge alongside AI
AI isn’t just about building powerful models, it’s about ensuring those models are grounded in structured, reliable knowledge. That requires human accountability at every step. While AI can process vast amounts of data, it cannot independently verify accuracy, clarify implications, or ensure insights are actionable. Someone needs to bridge that gap.
This is where the role of an AI librarian becomes essential, not just in overseeing information, but in actively shaping and maintaining the knowledge that powers AI systems. It’s about ensuring AI-driven outputs remain accurate, ethical, and aligned with business needs. While AI can assist with tasks like detecting duplication or assessing content quality, it cannot take responsibility for what it produces. That requires continuous human oversight, validation, and refinement.
At Deel, we’ve built the world’s largest global knowledge base for HR-related information with over 30,000 articles, and nearly 2,000 new pieces of content added each month. This isn’t just data at scale; it’s knowledge maintained through rigorous human oversight. A dedicated team of 200 local lawyers and payroll experts continuously reviews and validates updates, ensuring precision, compliance, and real-world applicability.
As businesses integrate AI into their knowledge management strategies, structured oversight is more critical than ever. AI is a force multiplier, but without accountability, it can scale bad information just as easily as good. Organisations that embed expert oversight into their AI systems today will gain a competitive edge, ensuring their knowledge remains accurate, reliable, and aligned with real-world needs.
Dougal Martin is head of knowledge at Deel
