Your customers are already using AI.
They are asking ChatGPT. They are asking Copilot. They are asking Perplexity. They are asking whatever assistant is built into their phone, browser or operating system this week.
They are asking questions about you.
- How your service works.
- What your rules are.
- What is allowed and what is not.
- What happens if something goes wrong.
- Whether you are worth trusting.
And those systems are answering them whether you are involved or not.
If you don’t have a publicly accessible knowledge base, AI is making things up about you. Not maliciously. Not deliberately. But inevitably.
This post is about why that is happening, why it matters far more than most organisations realise and why in an AI-first world the most important thing you can do is still deeply unglamorous:
- Write things down properly.
- Publish them.
- Make them the single point of truth.
Your customers no longer start with your website
For years we optimised for search engines. We argued about metadata, page titles and ranking positions. The assumption was that if someone had a question they would eventually land on something we had written.
That assumption no longer holds.
Increasingly, customers start with an AI assistant. They ask a natural language question and expect a single, confident answer. They are not browsing. They are not comparing sources. They are not reading footnotes.
They are asking “How does this work?” and moving on.
That means two things.
- AI systems are now acting as the first layer of interpretation between your organisation and your customers.
2. If you have not published clear, accessible information, those systems have to infer your answers from somewhere else.
There is no pause where the AI says “Sorry, this company hasn’t documented their policies properly”. It answers anyway.
Silence does not result in neutrality. It results in approximation.
AI does not know your business
This is the most important misconception to clear up.
AI systems do not know your internal rules. They do not know the exceptions you agreed three years ago. They do not know why something is done the way it is done.
They do not know what changed last quarter.
Unless you publish that information in a way they can access, they cannot retrieve it. And if they cannot retrieve it, they infer it.
Inference is not understanding. It is statistical plausibility.
AI answers are shaped by public web content, third-party explanations, forum posts and reviews, competitor documentation and patterns from similar businesses.
If your organisation does not have a public knowledge base, your “official position” is being assembled from fragments that were never designed to represent you accurately.
In effect, you are outsourcing your messaging to probability.
Confidence is not correctness
One of the most dangerous properties of AI systems is not that they are wrong. It is that they are wrong confidently.
When a human does not know something, they hesitate. They qualify. They ask for clarification.
AI does not do this by default.
It produces a fluent, well-structured answer that sounds authoritative even when it is built on guesswork. From a customer’s perspective, there is no obvious signal that the information is speculative.
This creates a subtle but serious risk.
A customer receives an answer about your service that feels definitive. They make a decision based on it. When that decision turns out to be wrong, the damage is already done.
At that point, it does not matter that the AI “wasn’t official”. The experience is associated with your brand.
If you care about trust, you cannot allow confident fiction to fill the gaps where documentation should be.
If your knowledge isn’t public, it might as well not exist
Many organisations respond to this by saying “We have documentation internally”.
That no longer matters.
Internal wikis, shared drives and onboarding decks are invisible to AI systems and to customers. They cannot be referenced. They cannot be cited. They cannot act as a corrective.
From the outside world’s point of view, undocumented knowledge is indistinguishable from non-existent knowledge.
In the past, internal documentation was about operational efficiency. Public documentation was about reducing support tickets.
Now, public documentation defines what is knowable about your organisation.
If something is not published, you have no control over how it is described.
Knowledge bases are no longer “help content”
For years, knowledge bases were treated as secondary. Something to build after the “real” product was finished. Something that lived in a footer link, largely ignored until something went wrong.
That framing is obsolete.
A modern knowledge base is not a support artefact. It is your public source of truth, the canonical reference for policies and processes, the material AI systems retrieve and summarise and the thing customers trust when stakes are high.
In an AI-driven environment, your knowledge base becomes the stable layer beneath increasingly fluid interfaces.
Chatbots change. Assistants come and go. But published documentation persists.
Verifiability is what creates trust
When customers are making low-stakes decisions, they might accept a conversational answer.
When things matter, they want something they can check.
They want a page they can re-read, wording they can quote, something they can send to someone else and evidence that the answer is not improvisational.
“The chatbot said…” is not a foundation for trust.
“Your help page says…” is.
Knowledge bases provide fixity. They anchor meaning. They allow disagreement, escalation and correction because there is something concrete to point at.
AI answers do not do this on their own. They need a stable source to refer back to.
Single point of truth beats best guess every time
Businesses do not need creativity in their rules. They need consistency.
Ask an AI the same question twice and you may get two slightly different answers. Both may sound reasonable. Only one may be correct.
A single, published knowledge base eliminates that ambiguity.
It ensures that customers get the same answer today as yesterday, staff and customers reference the same information and automation and humans operate from the same assumptions.
Consistency does not come from intelligence. It comes from documentation.
Hallucinations are a documentation problem
AI hallucination is often discussed as a technical flaw.
In practice, it is frequently a content problem.
When an AI system cannot find a definitive answer, it fills the gap. That is what it is designed to do. The less structure and clarity you provide, the more creative it has to be.
This is why organisations that invest in clear, structured knowledge bases see dramatically better results when they layer AI on top.
The AI is not smarter. It is better informed.
If you let AI sit above undocumented processes, fiction is inevitable. If you ground it in published facts, it becomes useful.
Organisational knowledge decays unless you capture it
Most organisations already have a knowledge base. It just isn’t written down.
It lives in long-serving employees’ heads, email threads, Slack messages and one-off decisions never revisited.
This knowledge is fragile. When people leave, it goes with them. When circumstances change, it becomes outdated without anyone noticing.
AI systems trained on informal communication amplify this problem. They absorb inconsistency and present it as coherence.
A proper knowledge base forces clarity. It requires decisions to be articulated. It exposes contradictions. It makes implicit rules explicit.
That work is uncomfortable. It is also essential.
AI should explain your knowledge, not invent it
There is a sensible hierarchy here, and many organisations have inverted it.
The correct order is knowledge base defines reality, workflows define logic and AI explains and navigates.
When AI is placed at the top without a truth layer beneath it, the system has no anchor.
The result feels impressive until it fails. Then it fails quietly and convincingly.
AI is an interface. Knowledge bases are infrastructure.
If you get the infrastructure wrong, no interface will save you.
Public knowledge bases are now defensive as well as helpful
If you do not define your policies publicly, someone else will define them for you.
Third-party blog posts, Reddit threads, review sites, competitor comparisons and AI summaries based on all of the above.
Once those narratives exist, correcting them is hard. The most effective way to prevent misinformation is publication.
A clear, accessible knowledge base is pre-emptive reputation management.
This is not an argument against AI
This is not a call to avoid AI. It is the opposite.
AI works best when it has something solid to work with. When it can retrieve, summarise and explain authoritative content.
The organisations that get the most value from AI are not the ones chasing novelty. They are the ones investing in boring, excellent documentation.
They treat their knowledge base as a product. They maintain it. They own it. They publish it.
AI then becomes an amplifier rather than a risk.
The unglamorous conclusion
There is no shortcut here.
If you want control over what AI says about your organisation, you must give it facts. Publicly. Clearly. In one place.
If you do not, it will make things up. And your customers will believe it because it sounds confident and because they have no reason not to.
The smartest AI strategy available right now is not adding a chatbot.
It is publishing the truth.