Enterprise AI Search: What It Actually Is and Why It Matters

We've all been there. You need to find that contract, that policy document, that decision from six months ago. You try the company search, get hundreds of results, scroll through pages of irrelevance, and eventually give up and message someone who might remember.

Enterprise AI search is meant to fix this. But there's a lot of marketing noise around the topic, so let's cut through it and look at what's actually happening under the hood.

What we mean by AI search

Traditional search engines (the ones most companies still use) work by matching words. You type "holiday policy", and they find documents containing those exact words, ranked by how often they appear. It's fast and predictable, but it breaks down quickly:

• Search for "annual leave" and miss the document titled "holiday entitlement"
• Ask "how many days off do I get?" and get nothing useful
• Look for "Q3 revenue" and miss "third quarter earnings"

AI search works differently. Instead of matching words, it tries to understand meaning. When you ask a question, it figures out what you're actually looking for, then finds content that answers it, even if the wording is completely different.

How it actually works

The technology behind this isn't magic, though it can feel like it. Here's what's happening:

Turning text into numbers

Every piece of text (documents, paragraphs, sentences) gets converted into a long list of numbers called an embedding. These numbers capture the meaning of the text, not just the words. Two sentences that mean the same thing will have similar numbers, even if they use completely different vocabulary.

This is what lets the system understand that "Q3 revenue" and "third quarter earnings" are related concepts.

Finding similar content

When you search, your query gets converted to numbers too. The system then finds documents whose numbers are mathematically similar to your query's numbers. It's essentially asking: "which documents have meanings closest to this question?"

This happens in a vector database, a specialised database built for exactly this kind of similarity matching.

Generating answers (RAG)

Here's where it gets interesting. Rather than just showing you a list of documents, modern systems use something called RAG (Retrieval-Augmented Generation).

The process goes like this:

1. You ask a question
2. The system finds the most relevant passages from your documents
3. Those passages get sent to a language model along with your question
4. The model writes an answer based on what it found
5. You get a response with citations pointing back to the source documents

The citations matter. Without them, you'd have no way to verify the answer or dig deeper. Good RAG systems show you exactly where each piece of information came from.

The practical difference

Let's compare what happens when someone searches for information about client contracts:

Scenario	Traditional Search	AI Search
Query: "Acme Corp payment terms"	Returns any document mentioning Acme, Corp, payment, or terms	Finds the specific contract clause about payment terms for Acme Corp
Query: "What did we agree about data retention?"	Probably returns nothing useful	Summarises the data retention clauses across relevant contracts
Misspelling: "Acm Corp"	Returns nothing	Understands you meant Acme Corp

What actually matters when choosing a system

If you're evaluating AI search platforms, here's what to focus on:

Can it connect to your data?

Your documents live in SharePoint, Google Drive, Confluence, Notion, Slack, email, databases, and probably a dozen other places. The system needs connectors for all of them, and those connectors need to actually work: pulling in content reliably and keeping it up to date.

Does it respect permissions?

This is critical. If someone doesn't have access to a document in SharePoint, they shouldn't be able to find it through search either. Good systems inherit permissions from the source, so access controls stay intact.

How good are the answers?

Test it properly. Ask questions you know the answers to. Ask questions that require combining information from multiple documents. Ask questions with industry-specific terminology. See if it handles edge cases gracefully or confidently gives wrong answers.

Can you verify the results?

Citations aren't optional. If the system gives you an answer, you need to be able to click through and see exactly where it came from. Page-level citations are better than document-level ones.

Where does your data go?

Understand the deployment model. Is your data leaving your environment? Which language models are being used and where do they run? For sensitive content, you may need on-premises deployment or specific regional hosting.

What can go wrong

AI search isn't perfect. Here are the common issues:

Hallucination

Sometimes the language model makes things up. Good RAG systems minimise this by grounding answers firmly in retrieved content, but it can still happen. This is why citations and source verification matter.

Outdated information

If the system doesn't sync regularly with your data sources, it'll return stale results. Check how often it updates and whether it handles deletions properly.

Permission gaps

If permission syncing isn't working correctly, people might see content they shouldn't, or miss content they should have access to. Test this thoroughly.

Poor document parsing

Garbage in, garbage out. If the system can't properly extract text from your PDFs, spreadsheets, or presentations, the answers will suffer. Complex documents with tables, images, and unusual formatting are particularly challenging.

Getting started

If you're considering AI search for your organisation, here's a sensible approach:

Start with a clear problem

Don't implement AI search because it sounds impressive. Identify a specific pain point: support teams spending too long finding answers, new starters struggling to find documentation, knowledge getting lost when people leave. Start there.

Pick a contained pilot

Choose one team or one data source for initial testing. Get it working properly before expanding. This lets you learn what works without betting everything on the first attempt.

Measure what matters

Track whether people are actually finding what they need. Are support tickets getting resolved faster? Are the same questions still being asked repeatedly? Qualitative feedback from users often tells you more than metrics.

Plan for iteration

The first version won't be perfect. Build in time to tune relevance, add data sources, and respond to user feedback. The systems that work best are the ones that keep improving.

Where this is heading

A few trends worth watching:

Beyond retrieval. Current systems find information and answer questions. The next generation will take actions: booking meetings, updating records, triggering workflows based on what they find.

Multimodal search. Text is just the start. Systems are getting better at understanding images, diagrams, video, and audio content.

Personalisation. Rather than showing everyone the same results, systems will learn what's relevant for different roles and individuals.

The bottom line

Enterprise AI search is genuinely useful technology that solves a real problem: finding information in organisations is harder than it should be. The underlying approaches (embeddings, vector search, RAG) are sound and improving rapidly.

But it's not magic. Implementation matters. Data quality matters. Getting permissions right matters. If you're evaluating systems, focus less on the marketing and more on whether it actually works for your specific documents, your specific questions, and your specific security requirements.

The best way to find out is to test it properly with your own data.