Libraries have spent millennia refining the art of organizing, retrieving, and safeguarding information. Their systems prioritize clarity, accountability, and ethical stewardship, qualities businesses now find indispensable as they integrate AI into their operations.
While AI promises efficiency, its success depends on structured implementation, thoughtful governance, and human oversight. Look no further than the example set by librarians.
These four principles, drawn from how librarians manage knowledge, can help businesses adopt AI with greater precision, reliability, and long-term impact.
1. Define Your Risk Tolerance
Before making big changes, librarians study what already exists–what works, what doesn’t, and what repeatable processes to leverage. The same approach maps nicely onto enterprise AI implementation. Understanding past efforts will help you navigate risk, avoid missteps, and replicate successes.
Most enterprises already have policies governing data use. AI is simply another tool that must align with those existing standards. Ideally, these considerations happen before procurement and implementation, since retrofitting governance after adoption can get messy.
Key questions:
- What policies or guidelines already govern data use in your organization?
- What are the consequences of potential misinformation?
- Where can human oversight add accuracy and accountability?
2. Identify Performance Benchmarks
An AI tool is only as valuable as the results it delivers.
Librarians work hard to ensure the resources and tools they provide meet their community’s needs. Measuring output quality and assigning accountability ensures that the technology meets expectations rather than becoming a black box.
Defining benchmarks before adoption sets clear success criteria, but these should evolve as you gain experience with the tool. If performance falls short, that’s your sign to reassess.
Key questions:
- What problem should the tool solve?
- Is it replacing an existing solution? If so, what performance benchmarks applied to that solution?
- What progress should be measurable in a month? Six months? A year?
- Can the tool scale effectively if it performs well?
- What kind of outputs should the tool generate?
- What will you do if the tool doesn’t meet expectations?
3. Evaluate Your Metadata Inputs
Librarians have long understood the power of metadata (descriptive information that makes data easier to find, to interpret, and to use). They also happen to be experts at prompt engineering, a skill businesses adopting AI should take seriously.
AI only knows what it’s told. If it pulls from disorganized, inaccurate, or poorly structured data, even the most advanced models will return outputs that are unreliable or irrelevant.
While most organizations can’t control exactly how AI models are trained, they can influence what those models use to generate responses. The right metadata strategy can help your AI systems retrieve more precise, useful, and contextually relevant information.
Key questions:
- What data sources does the tool rely on when generating responses?
- How important is output accuracy to your overall strategy?
- Can your team support ontology and linked data modeling standards (OWL, JSON-LD formatting, etc.) and related querying methods (SPARQL)?
4. Keep Learning
AI evolves quickly, but keeping up doesn’t require reading every new think piece (though some are worth bookmarking). The real advantage comes from developing a clear, strategic perspective on how AI fits into your business.
One of the most important information organization tools is what librarians call information literacy: the practice of evaluating sources, assessing credibility, and refining information systems over time. Information literacy is needed more than ever to support AI adoption.
Understanding what’s changing and what actually matters helps leaders make confident, informed decisions about AI. For example, techniques like lateral reading, which is used to evaluate the credibility of online information, are now being adapted to specific AI models in order to scrutinize performance and to improve outputs. These evolving strategies help you continuously refine how your organization evaluates models and improve implementations over time.
Key questions:
- What internal mechanisms exist for evaluating AI performance and relevance?
- Who is responsible for staying informed about AI or industry advancements?
- How do you distinguish meaningful AI developments from hype?
- What lessons from early implementations can be applied to future initiatives?
Ultimately, your goal with implementation should be to facilitate more informed, strategic decisions that align with your organization’s goals. The principles that have guided librarians throughout history offer a proven framework for navigating AI with confidence.
Need a sounding board to ensure your AI initiatives are practical, aligned, and effective? Factor can help. Contact us today to schedule a consultation.
