As we like to say, an AI is only as smart as the knowledge behind it.
Many companies try to push current LLM-style AI as being genuinely intelligent, but the reality is different. Today’s AI systems are excellent at certain types of analysis and information retrieval, but their method of “thinking” is vastly different from how humans think. If this is not taken into account when setting up an AI assistant, the AI may not learn the right lessons when trained on your knowledge base.
An organization needs to understand knowledge readiness for AI and how to structure their data in ways that allow the AI to learn the right lessons. This is the basis of creating AI ready knowledge.
Defining Knowledge Readiness for AI in Customer Support Organizations
Knowledge readiness for AI means structuring your data systems so that an AI can easily parse them and – more importantly – understand the relationships between the data.
Humans are good at making inferences. Current AI is not. A properly-trained AI doesn’t need to merely be taught facts; it needs to be taught how those facts relate to each other, and their larger implications. Broadly, this means spending time optimizing your metadata and otherwise ensuring standardization within your knowledge base.
In particular, you need to:
- Clear out repetitive articles. AI gets confused if the KB contains multiple articles with similar but slightly different information. Condense repetitive info into a single document.
- Standardize all metatags and terminology. Don’t refer to “Vice-Presidents” in one place and “VPs” somewhere else; the AI will think they’re different things. Or if you have an SOP for “Onboarding New Hires” then it should be written exactly like that every time. Etc.
- Create a standardized taxonomy – naming systems and hierarchy – for your knowledge, which brings further guidance.
- Implement knowledge quality scoring systems. These help the AI decide which of several articles to trust, when in doubt.
- Build a knowledge map, which describes the connections between pieces of information. This is time-consuming, but one of the best ways to keep your AI on track.
What Are Common Misconceptions About Knowledge Readiness for AI?
The biggest misconception is thinking that “data readiness” is the same as “knowledge readiness.” These are different things! Merely having well-organized data in your KB does not provide the knowledge of what that data actually means in real-world applications.
We’ve seen many companies get tripped up by this mistake. Just because your KB is well-optimized for humans doesn’t mean your AI can parse it.
Other common mistakes include:
- Not training agents on your AI tools. This is not the same as casually querying Claude. Agents should understand the basics of how the AI works and be given help with structuring requests of it.
- Treating AI as “fire and forget.” Especially in the first few months, your AI will need to be closely monitored, with one or more people positioned to refine its policies and guardrails over time.
- Thinking you need a huge or technically-proficient team. New tools make it far easier for everyday people to manage their AIs.
How Does Knowledge Readiness for AI Fit Into Your Broader Knowledge Management Strategy?
There is no contradiction here; creating AI ready knowledge should simply become part of your overall knowledge management strategies if you choose to utilize AI assistants.
This will require extra effort and retraining, because the trick is to create articles which are appropriate for both humans and bots at once. The content itself must always be human-readable, and structured like conventional articles. However, this needs to be paired with rigorously defined structures and metadata handling policies which prioritize the “hidden” information that AI relies on for extra context.
So, look to auditing your KB more often to ensure it remains properly structured and free of contradictions. Also put more priority on timestamps, and prevent articles from becoming too old without being edited or revised.
In Conclusion: Evaluate Your Knowledge Readiness For AI
Before implementing some form of AI assistant or AI-powered knowledge management system, you should perform a thorough review of your existing knowledge base. The information within needs to be AI-ready knowledge, or else the AI may fail to properly comprehend the information.
KMS Lighthouse can help! Our AI-powered KMS solutions are designed for easy knowledge discovery and deployment, with built-in tools to assist with data management and maintaining AI knowledge readiness.
Frequently Asked Questions
Broadly, having knowledge readiness for AI means that your data has been vetted, cleaned up, tagged, and otherwise structured for easy AI comprehension. A KB optimized for human readers is not AI-ready knowledge.
The most important attributes are:
- Conciseness. The KB should not have repetitive articles; try to only have one article per topic.
- Standardized naming. Always use the same terminology in the same way every time.
- Clear taxonomy. Give your KB a defined structure, with a clear hierarchy of article importance to help guide decision-making.
- Metatagging. The more the better as long as it’s all standardized. The AI relies on metatags to understand what it’s looking at.
- A knowledge map or similar feature that provides connections and context between various pieces of data.
AI governance is a combination of data-handling policies which make the your knowledge AI-friendly, and human oversight of the process. Humans should be continuously evaluating KB metrics, looking at AI answers vs the sources it’s pulling from, and refining the guardrail policies overseeing its behavior.
An AI will need to be trained over time, almost like a pet, for best results.
Unassisted, an AI has no way of determining supremacy when two KB articles offer contradictory information. This leads the AI to either mix-and-match solutions from both articles, which is likely to be wrong, or else it invents a new answer to avoid the contradiction. Which is also likely to be wrong.
Your KB must have as few contradictions as possible, with up-to-date information throughout, to avoid AI confusion.