Turn your internal documentation into a smart knowledge assistant

You run an SME and feel like you spend your days hunting for documents: procedures buried in folders, outdated templates, different answers depending on who picks up the phone… Meanwhile, the same mistakes keep happening and new hires learn everything “on the fly”. You keep hearing about generative AI and smart knowledge bases, but it all feels far from your daily reality.

In this article, we’ll look at how to use AI not to “revolutionise everything”, but to bring order to your internal knowledge and make it finally usable by everyone. The goal is to turn your scattered documentation into a simple knowledge assistant your teams can rely on, without a heavy IT project.

1. Why your current documentation doesn’t really work

Before talking about AI, it’s worth understanding why most documentation systems fail in SMEs.

1.1. Typical symptoms in small and mid-sized businesses

You might recognise some of these situations:

• Procedures stored in shared folders… but no one knows which version is current.
• Email, quote or contract templates multiplied into ten different variants.
• Different answers given to the same customer question.
• Internal experts constantly interrupted to repeat the same explanations.
• New hires spending weeks asking “Where do I find this?”

When information exists but can’t be found or used, it’s as if it didn’t exist at all.

1.2. How AI changes the game (without replacing everything)

Until recently, making a knowledge base work required:

• a lot of discipline to keep it updated,
• time to read and search through documents,
• tools that were often clunky or hard to use.

Generative AI lets you change how people access information, without necessarily changing all your tools:

• your documents stay where they are (Drive, SharePoint, CRM, internal folders),
• AI becomes the interface: you ask a question in plain language,
• it searches your knowledge base, summarises and formats an answer, without inventing new rules.

The idea is not to let AI “decide”, but to use it to:

• find the right information quickly,
• adapt content to the context (customer email, internal note, checklist),
• relieve your experts from having to repeat themselves.

2. What a knowledge assistant actually looks like in an SME

Imagine a single entry point for your teams:

“Ask your business question, the assistant answers based on the company’s official documentation.”

2.1. Concrete examples by function

• Customer service:

  • An agent asks: “What are the conditions to extend a warranty for a business client?”
  • The assistant replies using the latest version of your terms and your internal guidelines.

• Accounting / finance:

  • A team member types: “Remind me of the rules to approve a supplier invoice over €5,000.”
  • The assistant generates a step-by-step checklist based on your approval process.

• Human resources:

  • A manager asks: “What are the onboarding steps for a new sales rep?”
  • The assistant provides a checklist built from your onboarding pack.

• Operations / logistics:

  • A warehouse manager asks: “What checks do we need to perform before shipping orders?”
  • The assistant summarises the relevant quality instructions.

2.2. What the assistant should – and should not – do

What it does well:

• Search for the right information in a pile of documents.
• Write clear, structured answers.
• Adapt tone and format (internal note, customer email, supplier message).
• Produce “ready-to-use” outputs: emails, simple procedures, checklists.

What it must not do on its own:

• Change your business rules.
• Make sensitive decisions (legal, HR, financial).
• Answer on topics that are not covered in your documentation.

You stay in control: AI prepares, humans validate and decide.

3. Build your knowledge assistant in 4 steps

You don’t need to cover the whole company at once. Start small, on a useful and manageable scope.

3.1. Step 1 – Choose a precise business scope

Look for an area where:

• questions are frequent,
• answers already exist in documents,
• errors are costly in time or money.

Typical examples:

• answers to recurring customer questions,
• invoicing and collection procedures,
• standard HR rules (time off, expenses policy),
• quality procedures for a specific product line.

Ask yourself:

1. Who will use the assistant? (customer service, finance, managers…)
2. What types of questions will they ask? (prices, delays, internal processes…)
3. Which current mistakes do we want to avoid?

3.2. Step 2 – Collect and clean the right documents

Before thinking tools, sort out the raw material: your documents.

1. List existing sources:

   • shared folders,
   • Word or PDF procedures,
   • training decks,
   • internal FAQs, reference emails.

2. Select only what’s usable:

   • latest versions,
   • rules that are actually applied,
   • documents that are reasonably clear.

3. Remove or clearly label what’s obsolete:

   • old price lists,
   • replaced procedures,
   • draft documents that were never validated.

AI doesn’t “fix” bad documentation. It amplifies what you feed it. A small, clean corpus is better than a huge, contradictory one.

3.3. Step 3 – Configure a simple, secure AI assistant

In practice, you can:

• use a dedicated “knowledge assistant” solution,
• or combine a general-purpose AI tool with your documents via connectors.

Key points to define, even in simple, non-technical terms:

• Which documents the assistant can access: only the ones you selected.
• Answering rules:
  • the assistant should reference the documents it uses,
  • it should say “I don’t know” if the information is not available.
• Confidentiality:
  • don’t start with highly sensitive data (detailed HR files, health data, legal disputes),
  • restrict access to certain teams if needed.

You can express the guardrails in natural language, for example:

“You must answer only based on the documents provided by the company. If you’re not sure, ask the user to check the official procedure. Never invent new rules.”

3.4. Step 4 – Test with real users and iterate

For 2–4 weeks, run a pilot phase:

• 5–10 users test the assistant in real situations,
• important answers are reviewed by a human before being sent to customers or applied,
• users log what works well and where answers need to be corrected.

You then adjust:

• the reference documents (procedures that need clarification),
• the instructions you give to the AI (tone, answer format),
• the scope of use (what must remain 100% human).

4. Best practices to keep control and quality

4.1. Define a simple “knowledge charter”

To avoid drift, clarify a few basic rules:

• Who can change reference documents? And how they are validated.
• How a new procedure is approved before it’s added to the assistant.
• When AI must not be used:
  • sensitive customer cases,
  • HR decisions,
  • complex legal situations.

Ideally, this charter fits on one page and is shared with all users.

4.2. Involve your experts (without overloading them)

Your internal experts (finance, HR, quality, customer service…) are essential:

• they help select the right documents,
• they validate the first standard answers,
• they spot grey areas that need clarification.

But the goal is not to turn them into full-time admins. The assistant should reduce repetitive interruptions, not create extra work.

4.3. Track concrete benefits

Monitor a few simple indicators:

• average time to answer an internal or customer question,
• number of direct requests to experts on the same topics,
• errors or disputes linked to incorrect application of rules,
• how teams feel about finding information (“easier”, “no change”, “harder”).

You don’t need perfect metrics. A clear trend is enough to decide whether to scale up.

5. Practical action plan: your knowledge assistant in 15 days

Here’s a realistic roadmap to pilot a first assistant without a big project.

Days 1–3: Frame the scope

1. Choose a specific domain (e.g. standard customer replies, billing rules).
2. Appoint a business owner and 3–5 pilot users.
3. List 10 frequent questions the assistant should be able to handle.

Days 4–7: Gather and clean content

4. Identify documents that already answer those 10 questions.
5. Remove what’s outdated or contradictory.
6. Put all selected documents in one dedicated shared folder.

Days 8–11: Configure and connect AI

7. Pick a simple tool (or ask a partner like Lyten Agency to recommend one).
8. Ingest the selected documents into the assistant.
9. Define clear instructions: tone, answer structure, and a rule to say “I don’t know” when needed.

Days 12–15: Test and decide what’s next

10. Have your pilot users test the 10 frequent questions.
11. Correct the answers and adjust documents where needed.
12. Decide on next steps:
    • Roll out to more users?
    • Add a new type of questions?
    • Or improve the initial scope before scaling?

Conclusion

• AI is not there to invent your rules, but to make your existing knowledge findable and usable.
• A well-designed knowledge assistant reduces errors, speeds up answers and frees up your experts.
• Success is more about organisation than technology: clear scope, clean documentation, simple usage rules.
• In just 15 days, an SME can run a concrete pilot, without redesigning its whole IT landscape.

If you want support with your digital transformation, Lyten Agency can help you identify and automate your key processes. Contact us for a free audit.